“You can do it!”

“It’s a hit… So many jobs are waiting for you.”

“It’s the decision you can ever make!”

“Good for you.”

At first sight, all this pop talk is great. Motivational. Inspiring. Self-steam.

But is that what learning machine learning (ML) is really about? Is that something interesting (or yet challenging, depending on what excites you more) like it looks?

Let’s face it: learning anything requires time, patience and determination. Imagine this thing is constantly evolving, being updated, and incorporating new technology. The mission could be more challenging but interesting! (no matter what excites you more, both feelings are involved)

Indeed, it’s an ocean, and you don’t know where you can start. 

Feeling scary! No, you shouldn’t, but as we promise, you will know things no one will tell you about this learning journey.

What is Machine Learning?

Machine Learning

Machine learning is one of the fields in that you don’t need to delve deeper into theories like probability mathematics, statistics, or equations until you do. However, it’s a practical domain that you have to have a hand in doing steps, not just knowing steps.

There is no right and wrong approach to breaking through this study area. But there is a learning style that can save years of pain and distraction.

Daniel Bourke, an artificial intelligence (AI) blogger and the owner of mrdbourke.com, claimed that the best very first step to learning machine learning is to start with learning codes. Then, once you see your right codes running, you can get back to machine learning to know how things are going while building these codes.

However, Ron Pick, a computer scientist, said that you should go first with learning more about theories because they will establish a robust foundation for you to be aware of machine learning technologies. Then you can search for a place to install these technologies or bring what you have learnt to life.

Everyone will share their stories based on personal experiences, which may or may not be relevant to your situation. It’s all up to you and what you hope to get out of this.

If you’re obsessed with details and want to have the whole picture before even starting on, the theory’s path is more suitable for you. Otherwise, if you’re a practical person who wants to go right to things, creating with codes would entice you more to build your artificial intelligence career. 

That brings us to the second question.

Know What Machine Learning Prerequisites

Indeed, many things we have covered in this long blog post can be listed under this point. But as we promise to guide you with practical subjects and all the best resources no one will tell you, we want to make sure that you have a solid foundation to build on to make this journey exciting, fun, and easy as much as possible. 

This solid foundation should include basic mathematical modelling, statistical learning, and data structure. 

Yes, machine learning is about practical projects. Still, you also need to understand the theoretical part, which will help you learn how to deal with different situations when solving coding problems. 

Before you even venture into these spaces and building systems, let’s narrow down what you should be prepared for:

Math and Statistics

  • A mathematical foundation is a must, but you don’t need to be an expert. Advanced calculus and differential equations are not required. Frankly, it could be a waste of time. Nothing of both will be needed to perform complete tasks.
  • Statistics, probabilities, and algebra are inevitable, and these three areas are helpful for machine learning. 
  • Programming is a place you should start with as well to develop a sound knowledge of coding and algorithms. (more details in Python section)

Side tip: Probability is a measure of how likely a particular event will occur. To make data-driven decisions, you must understand the probability involved. Therefore, the fundamentals of independence, notation, continuous random variables, probability distribution, joint, and conditional should be on your list when defining the basics of ML. 

But why is the statistical and mathematical part critical? Well, Machine learning refers to the process of helping computer systems to learn through the data. So the base here is statistical techniques, and we don’t really want to program this data explicitly. And math will help you comprehend algorithms. 

Important: Not all business or domain problems will need to use optimised mathematical modelling. You might try to stimulate a readymade mathematical solution matching your situation.

Data Structure

There is not so much to learn here if you will depend on algorithms libraries. But we’ll give you a guide if you want to understand more:

  • Have the foundation for storing, retrieving, accessing and manipulating data.
  • How to read and ingest data from different formats
  • How to write and slice data to an output file. 

For geeks: you might need to know the pros and cons of some data structures to be able to spot the best one for your task. Otherwise, you can get away with available open-source and be open to honing your skills through your learning path.

Important: You will not be able to the ML code unless you take time to comprehend what Pandas and NumPy are doing. You’ll appreciate these efforts later when having a hand in an actual project because you’d have the answer to most problems popping up. 

Which one should you start with?

Start with mathematical modelling if you’re a newbie in this field. We know aspirants like you need to know everything at once. DON’T do that! 

Instead, be specific in certain areas and don’t go beyond the previous three fundamental principles. 

Remember, almost all these algorithms, codes and theoretical concepts are already implemented. Still, the theoretical part will help you understand the big ideas of machine learning, making you more professional when going to a job interview. 

Before you go to the next, we need to emphasise some essential points to cut off the hassle:

  • We suggest going in-depth in learning about ML algebra instead of algorithms if you’ve time to do so (or, in other words, if you have the luxury of spending money and effort on learning and you’re not obligated to pay for hefty bills)
  • Another approach can be implemented as well, which is exploratory or methodology. You can learn more about linear algebra by involving in LM projects and get their knowledge from the real world.
  • The best of both is to do both of them simultaneously. Lean, do, watch, take notes, and learn again.
  • Take extra time to test data analytical techniques to know what’s the best to elicit the high-quality data to create your base.
  • Know something about data mining technologies to create your own dataset.

Important: If you choose to go deeper in ML algebra, you’d have to understand its abstract concepts of matrix operations and vector spaces. So, have a good glimpse of linear transforms, nations, tensor and tensor ranks, algorithms in code, and matrix multiplication.

Bonus tip: As an AI or ML engineer, you have to leverage some soffit skills to prepare you better for this life-changing experience. We sum them up as follows:

  • Don’t stop asking questions: Specially WHY! Always say why. Find answers and search for answers to things that look like an answer. 
  • Be analytic: don’t just take the matter from the surface. Go beyond what you have till you find something that answers your questions.
  • Be patient: it’s a long journey, and it’ll consume your time, but fortunately, on something worthy. Also, don’t push yourself to work on something you have no interest in. Find what motivates you more.
  • Be selective: Not all tools will be beneficial for you. Before downloading or investing in applications or software, read more about it and see what will provide you with insightful conclusions.     
Machine Learning 1

You Don’t Need High-End Computers to Get Started 

After we cove the foundations of prerequisites for machine learning, it’s essential to be well-prepared with an excellent pc or laptop, which can help you upskill your education path in the era of machine learning with hassle-free tasks. 

You will need to use tools, download sources, and software programs using recent technology to fuel your learning and career growth. A powerful laptop in this stage is a must, and the last thing you need now is to have a poor device that might slow down your performance. This experience is enough to discourage you from resuming this journey.

So, what is the best laptop to break down the daunting curriculum and tasks?

If you’re on a tight budget, it could be challenging to spot a laptop with excellent features and affordable.

The good question you should answer now is what makes a laptop more qualified than others for Machine learning. Here are the criteria you are looking for:

  • Having the capability to tackle computational operations and use different deep learning techniques.
  • A good battery life with an average of 5h (the experts suggest that you should pick up the laptop with the longer hours of battery even if the processing quality might be lower. Beijing out of the battery while working on a significant project is harsh)

Side tip: if you aim to work indoors, then the battery falls behind the top priorities you should consider. 

  • Sufficient memory and storage is a matter of life and death since you will need multiple spaces to store data, files, and applications. We’re talking about memory not less than 8GB of RAM and 256GB of drive space. If you want to expand this, please do. Nothing is wrong with large capacity.
  • The processing power should be great enough for multitasking operations and long-run complex computing. Choose an Intel Core i7 or i9 processor or an AMD Ryden processor, and nothing less to be able to use the right technology. 

Side tip: If you are still a beginner in this area, Core i5 is good to kick a start. At the time, invest in high-end devices to improve your work or business functions.

  • The screen size should not be less than 14 inches. If you aim to spend more, a 17-inches laptop is optimal for great resolution without so many headaches. 

Side tip: if you can sacrifice portability, please do and invest in a premium HD display. You’ll appreciate it further when building ML models and getting visualised documents.

  • The graphics card and core level should be premium as well. The best available option to assist you in complex tasks is the NVIDIA Geforce RTX or GTX. This card can be found on gaming laptops. These cards’ features will provide superior performance when rendering, visualising and processing the data algorithms. Can’t afford it? No worries! An Intel Iris Core is good enough for beginners. 

Side tip: Don’t hesitate to customise your gaming laptop if you can do it.

  • Ubuntu Linux is the optimal operating system to manage your ML tasks because it supports many programming languages with high performance and advanced security features. Most importantly, the system is optimal for beginners with a user-friendly experience.

Side tip: Windows will help you learn to propose, and macOS laptops now come with high-tech features for developing and multitasking. 

Our recommendation for the best laptops for machine learning, running from

  • Dell Precision 7760 Workstation (£4090)

This device is one of the best laptops to start implementing machine learning tasks with superior visualising features, an excellent processor, and a perfect graphics card. It’s incredible if you’re looking for a portable device. 

It’s for high-advanced levels.

  • Asus Rog Strix SCAR 17 G733 (£1800)

It’s one of the best gaming laptops with high-end ML capabilities. With its AMD Ryzen 9 CPU processor paired with its NVIDIA Geforce RTX 3080 GPU processor, your job is going to be much fun, comfortable, and perfect for many data types.

It’s for those who are searching for complex computing operations with a medium budget.

  • Acer Nitro 5 (£800)

Acer Nitro 5 comes with an 11h battery life, the NVIDIA graphics card, and an upgradable Windows OS. It offers a broad collection of features for developers and engineers. But don’t expect to be a multitasker deceive or process computation modelling. 

It’s for those who want a hand on medium ML projects and affordable solutions.  

What is the Best for Machine Learning? Python or R

Another bomb is about to burst right in front of your eyes! Which language can you use for machine learning?

Open-source programming languages are an excellent place to begin your learning journey. That’s why the Python vs R debate inevitably comes back when people ask which codes are much better for beginners. 

Some will recommend learning Python just because if you’d want to dig deeper into artificial intelligence or get hired for a technical job, Python would be widely used. But, most importantly, it’s one of the more accessible coding languages to pick up. Also, it has the most convenient modules and support for machine learning. Once you’ve mastered Python, you’re ready to go on to the next.

On the other hand, others will guide you to go for languages like R. Their justification lies in being designed for machine learning like R.

In case you don’t know, where is the difference between Python and R to help you make the right decision.

The two languages are similar, free to download and adaptable for data science and machine learning tasks. These applications include data manipulation, big data, digitisation and business intelligence. The core difference is that Python is an all-purpose coding language; however, R is more dedicated to analytical tasks. So, the reasonable question is how to make use of each one and align it to specific purposes.

Python: is one of the most readable and common coding languages worldwide, with large white spaces making it a favourite for developers and programmers. Python is very general and broad and used for everything you can imagine, from the most brilliant to the most simple. At the time, Python also fits perfectly for machine learning applications backed by high-tech tools like sci-kit-learn, Keras and TensorFlow, which help data scientists to build their systems. Not just that, using other sophisticated technologies, everyone involved in the collecting data process can share documents, including comprehensible data, visualisation, equations and most importantly, live Python code. Google, Netflix and Facebook use it.

R: is a programming language for people who want something designed limited to only statistics analysis and data mining. It can be used for data analysis, data science, and visualisations. Developers in prominent companies like Google, Uber, and Facebook use R to handle their data tasks. Undoubtedly, R is fantastic for data analysis, with hundreds of packages and libraries purely for analytics. Also, it’s easy to build visualisation with this language.

So, what should you choose to learn to be a pro in machine learning?

The answer is: it depends. There are many considerations, which we will sum up right here.

  • What is your background? If you have statistical experience, then R should be your first choice. If you have some programming knowledge, Python is a keeper! Neither of them! Then Python will be more appropriate for starting your data science or cyber security career. 
  • What will you use it for? Statistics-oriented tasks or data exploration objectives can match perfectly with R. If you’re willing to a general-purpose direction working on a large scale, comprehensive production systems, or even machine learning applications, nothing can beat Python.
  • What do you expect from it in terms of visualisation? Charts, graphs, and any visualisation forms! R is more than perfect for getting this kind of data, which will be easier to analyse and use. 
  • What is your organisation’s scale? Python wins this round because it can be easily integrated with other applications across organisations.
  • How many hours are you willing to spend to learn it? R is more complex than Python. But R comes with more features, and Python is still developing.

“I am still confusing. Can you just give me a summary of what I should do?” So if that’s you— who are still asking about which the best language, here is the summary:

Important: You should try BOTH Python and R. Indeed, you need to do some hands-on practice to find out what is better for you. If you find online sources are not enough, take an elementary course to catch a glimpse of both and determine which works out for you. *if you have any other thoughts, please leave a comment below 🙂

Bonus tip: Coursera is a good place for beginners if you want to start learning about Python online, especially Python for Everybody Specialisation. It’s free and straightforward. FreeCodeCamp is also perfect, especially their YouTube channel. It’s something you shouldn’t miss if you’re interested in Python. 

Machine Learning Concepts Should Go Along With Coding Learning

After building a foundation of your desired coding language, what should I do?

No, it’s not a good question. So instead, ask: What other areas should I study while learning ML?

Now, it’s time to be more specific about the machine learning career by learning about data science tools and ML concepts. Set your learning plan based on studying concepts with codes because you need to apply theories to what you’re learning from programming. This approach will help you to understand this area thoroughly, and don’t worry too much about these concepts or if you couldn’t implement ML applications on what you’ve learnt from codes.

So, what are the most common machine learning concepts you should know?

  • What is the goal of ML? Machine learning is dedicated to generating algorithms that can learn patterns ingrained in data that can be used for creating specific tasks.

What types of ML? Even though a lot exists, we can divide machine learning into two types (supervised and unsupervised) based on what we need from data. The first is widely used to make future predictions depending on data we have assembled before. Here we analyse behaviours and characteristics. This data is stored ideally in a file called “the target file) and we can search for any historical archive to build up meaningful information. A good example of supervised ML is what Google does to classify malicious or specious emails on the “Spam” file. This classification relies on any triggers or tags used in the data that can be labelled under one title; for example, these messages appearing in spam include the same pattern, totally different from messages in Inbox and Sent.

On the other hand, unsupervised ML is not responsible for analysing or finding a specific pattern. Here the machine will help you collect and organise data for further use. And it’s commonly used when you need to manage your customer’s data to know who accepts the price and finds it not very good.

Important: Some has different opinion regarding how many types of machine learning. They assume that there are extra two; semi-supervised learning and reinforcement learning. The first one tends to use unstructured data with a small amount of structured data. This approach is less costly than supervised learning and, at the same time, helps machines to predict more accurate data based on labelled data. The second represents the optimal actions because the machine, in this case, will generate data through trial and error and store all learning behaviours regarding the current status. By that time, you will get the most accurate predicted output and maximise your chances of being rewarded in the future.  

  • What are ML features? All attributes will help us analyse and describe the data collected. For example, in the case of the customer portfolio, ML will define and classify data to the number of purchases, age, number of followers, and registration in the newsletter. Then the machine will provide us with a certain percentage, number, and charts reflecting the customer’s lifetime value. Machine learning will also find relationships between at least one or two of these factors to the customer value that will help you a lot when making decisions or improving your product or service.
  • Is ML Just About Analysing? Absolutely not! Machine learning does a great job in predicting behaviours to help you take the proper proactive action. For instance, ML will tell you if the customer will purchase from another competitor or if he is satisfied enough based on his behaviour. So, if you found that your customer will keep searching for another provider, you’d offer something valuable to entice him to buy from you, like special deals, a small gift, and so on. Also, the ML applications are provided with a regression feature which will provide you with numbers. What does it even mean? Okay, let’s say you have a website for booking hotels; using such an application will tell you several possible reservations in the next month or two or maybe more. So, you now have data about your potential profits and a good action should be in place.

Important: The regression feature deals with compounded numerical variables. It’d be helpful for many enterprises working on understanding their customers because it estimates stock price, market share, housing price, product price and so on. And to solve regression problems, a number of methods can be utilised, like Gaussian process regression and Kernel regression (the most costly approaches because of their higher accuracy), linear regression, support vector regression, and regression trees. Your data, budget and the scope of the project are what determine which way you should take.

  • Are There Different Analysing Processes I Can Choose From, or Is There Only One Standard? I am not sure I have got your point, but let’s assume you’re asking about the optimisation level. Yes, as brilliant as ML is, it enables you to choose whatever criteria and characteristics you need for your data analysis to uncover patterns. Of course, not all data can take the form of analytical information, and not all data can be defined in clear relationships to create a meaningful chart. This data can be considered noise. But it doesn’t after a selection feature, which will choose the best model to formulate valuable data. And then, you’ll have a new direction to make the most of your data, and this step is called feature engineering. Let’s pause here for a second; the previous process is dedicated to building a prediction model where data will be structured and organised. Engineering is to get rid of any useless data that can distract machines from setting up visual information. That’s why it’s one of the most costly machine learning processes. Just imagine what would happen if the machine kept useless data and built its patterns accordingly! You will get inaccurate yet poor data which means not making the right decisions. Yes, it’s just like cleaning noise from the signal. And after all, you’ll fail to improve the end-user experience. 
  • What is the Difference between Data Mining and Machine Learning? Data mining and machine learning work together— not against. However, the main difference is the objective of each concept. Data mining is about predicting totally new patterns. On the other hand, ML is about recreating relations between patterns defined before and then predicting behaviours, numbers and actions. The main goal of data mining is exploration— the primary function of machine learning is prediction. 
  • How Does ML Really Work? You can not start learning machine learning without answering this question. In short, ML builds a dataset (sometimes called raw material) for the prediction system used as a historical base to train the approach on how it should work and then predict patterns to design the algorithms. This set is composed of properties inferred from specific instances. These instances include different factors and patterns like the age of customers, their navigation journey through the website, and how many minutes they spend on each page. Then it establishes individual data flipped into meaningful information, which will be available for analysing. However, you have always the ability to enter new data. For example, you want to research the purchases placed on your website. ML results will help you know who purchases, who has a penchant for purchasing and others who will never buy, forming spreadsheets including rows and columns manifesting characteristics of previously defined instances. 
  • Can I Determine The Purpose of the Analysing Process? Of course, you can. Through a sophisticated ML system, you can set the objective to have the answer, like “the probability of success of a certain product”, and ML will find connections between historical data and recent reactions of customers and then predict how things are going to turn out.
  • What is the Most Important Step of the Whole Thing? Patterns Identification is the heart of ML because it will help you predict your customers’ behaviours and generate leads based on your customers’ preferences from previous purchases. 
  • How Can Machine Learning Find the Best Pattern For Any Data? It’s the job of the tunning feature. In this step, ML optimises the parameters included in the algorithms to create a well-defined combination involved in models for each specific data set. Consequently, each model consists of many parameters, and each parameter comprises several potential patterns. And no panic; the system will formulate these parameters automatically. This process is called hyperparameter.
  • But What Does Modle Look Like? It seems that the system has trained well to predict the patterns. So, nothing now is more important than creating models to predict. So, we can migrate and organise data inside these models according to patterns or categories. For example, suppose the system has been trained to anticipate the number of subscribers in your cloud services. In that case, the model will divide customers based on the previous behaviours of your customers to help you get the possible accurate outcome. And regarding the model look, it’s like a tree including branches which contains predictions of each pattern. That’s why it’s known as a decision tree. It brings us to define the model. 
  • What is the model? Also called a hypothesis, the model is a data presentation adopted by specific information we introduce to the machine by its algorithms. 
  • What after building models? The system will be ready for validation. In this process, we must define how the machine handles tasks. Back to the dataset to understand more— Okay, when establishing the dataset, we need to split it into two essential categories; one for models and the other for tasting. This tasting is dedicated to introducing the performance of your model and how it will be able to tackle the task. 
  • What are other terms I need to know in ML concepts? Target or label refers to the value or the output than can be predicted by the system. Let’s say the title of your module is to anticipate your eCommerce business sales to offer the best customer experience. The target here is the set of values you need to have a glimpse of, like the specific product you displayed on your website. In other term, training is to set features (input information) alongside target (predicted output) to create a model (hypothesis), which we use to draw a map to get the data. The process of aligning features with targets is exactly what training is all about. The final term of ML concepts is prediction, and in this step, the ML is ready to elicit outputs and give us prediction data.
  • What is the best way to practice ML? Actually, there’s a lot out there. Start with collecting, cleaning, integrating and processing. You know why you must collect data, but what about other steps? Not all data are created equal, and you here need just high-quality data. It doesn’t matter how much data you have successfully collected; your focus should be directed to what will be valuable for your task. Then, you have to decide which models will be appropriate for each job. There is no right or wrong here; you must try all possible and practise on your database to make the best decision. You’ll gain the intuition to determine which model is better than others and save tremendous time and effort (Learning the hard way is always the best approach to tackle problems further).
  • How to interpret the results? One of the critical concepts of ML is to learn how to interpret your outputs. Because your efforts will be in vain without taking advantage of what the machine system tells you. Nothing can be more effective than comprehending thoroughly the tuning parameters applied to various models.  

You have to read more about these concepts while implementing coding tasks to understand how machine learning works. It’s considered one of the most critical prerequisites for learning ML.

So, what are data science tools you should begin with?

Nothing more than Jupyter Notebooks and Anaconda. Continuum website is great for learning about Anaconda and has insightful materials for all levels. And to have an overview of Jupyter. Just check out their official website. 

Bonus tip: To compile some knowledge about machine learning concepts, you can begin with elementsofai.com offering a free course in which you can find some valuable lessons to have an overview of artificial intelligence. It’s brilliant if you want to take a step on ML and AI concepts.  

Machine Learning 1

Know What Libraries You Should Depend on When Using Machine Learning

Once you have fundamental Python skills, you need more knowledge about data science and machine learning libraries. We’ve mentioned libraries before in this blog, but What do libraries exactly mean?

It’s in-built ready packages of codes which will help you handle tasks and topics with minimum effort. 

Since machine learning gets insights out of the data, you need a tool to manipulate and work with these data. Indeed, you have to work with these three foundations, NumPy, Pandas, and Matplotlib.

Well, what are the differences between these three? Or They are all about?

NumPy: refers to Numerical Python, which, as its name suggests, will help you do any numerical operations in the excel spreadsheets if needed. It’s one of the fundamental and powerful libraries you should know and use while creating prediction models, and most likely, it’s written in C language. NumPy works on multiple dimensional equations and high-level complicated mathematical tasks. However, an essential role of Numopy is that it forms an easy framework when analysing data and objective-oriented operations.  

Pandas: is what you use to work with structured data, and it’s an abbreviation for Python Data Analysis Library. Imagine you’re working on spreadsheets in which rows and columns are filled with data. Pandas will help you change your data’s style, eliminating useless data and serving data you might be missing. It’s an open-source library which is exceptional, dedicated to data analysis and data manipulation. It strategically connects to NumPy as it’s set up upon Numpy packages. Pandas can handle Excel, CSV, and SQL documents making them readable and measurable, which will be super when building, connecting and merging datasets.

Well, what are the main takeaways for you from all of these libraries? 

Core differences between NumPy vs Pandas:

  • Pandas works on data collections presented in columns and tables. NumPy deals with numbers.
  • Pandas has two practical tools DataFrames or Series. NumPy has only one Arrays.
  • Pandas has a broader library because it embraces 73 company stacks with 46 developer stacks. The library of NumPy comprises 62 company stacks with 32 developer stacks.
  • Pandas occupies memory; however, NumPy is more efficient.
  • The performance of both depends on the tasks and functions you’d use. But it’s said that Pandas perform better when rows are 500k or more. On the contrary, NumPy will give you the best performance when rows are less than 500k.
  • The output of Pandas takes the 2 dimension shapes. Otherwise, NumPy creates multi-dimensional data.
  • In general, Pandas is slower than NumPy.
  • Use Pandas for analysing and visualisation. Use NumPy with computations and numeral operations.

Matplotlib: stands for math plotting library, and it’s powerful when transferring data into visuals, including static and animated. Using Matplotlib will make hard things handy and complicated information readable, and it will absolutely enable you to customise the layout and the style of visuals. 

Since Python is one of the best programming languages to put data science and software development into action, understanding how these libraries work will help you step inside and set up a foundation for what will come later. 

Bonus tip: Ready to improve your machine learning skills and dig deeper in this domain? Check out Applied Data Science With Python Specialization on Coursera—it’s FREE! Also, there are great tutorials to find out more about these Python packages, like the CodeBascis channel on Youtube. Otherwise, visit the official website of Jupyter to find free codes for Numpy. And finally, go straight to the Sentdex Youtube channel filled with brilliant tutorials about Matplotlib.

Check Out Scikit-learn

Before learning ML, it’s vital to get the fundamentals right to build upon. So, preparing the suitable sources before even choosing the analytical approach to decision-making will be a life-saver.

That’s why we’re about to explain Scikit-learn, an extraordinary open-source library in Python, including practical built-in machine learning algorithms.

So why it’s even important? Because Scikit-learn is not about learning these algorithms from scratch. What you need to do is to apply the re-built-in models or patterns to different machine learning tasks.

For example, if you have a classification problem, you must determine what sort of these algorithms should be placed in your system.

What can you expect from Scikit-learn?

You will find algorithms for such machine learning problems as regression, clustering, classification, reprocessing, and model selection. (more information is available in the LM terms section) It’s a straightforward and effective tool for data analysing depending on NumPy and Matplotlib.

What else? 

  • Help eliminate redundancy of data
  • Classify data to specific criteria to help your ML system identifies patterns
  • Predict instances in line with your objective.
  • Set the similar data in the same model
  • Taste and compare parameters and models to ensure the best performance meets your expectations.
  • Migrate data and make necessary changes to apply them to built-in algorithms.  

Bonus tip: In today’s disruptive atmosphere, it’s critical to minimise the efforts you have undertaken to do your job. Browse the Data School channel on Youtube, which will be more than enough to have a hand on how to figure out Ml algorithms on Scikit-learn.  

Machine Learning 2

Learning ML Algorithms is Not the Better Way 

Machine learning now is a very broad term, and many things are included under this umbrella. There are two main branches; ML and ML research. 

Good programming, algorithms, and mathematical skills are essential if you want to work on research. (However, you can choose one of them if you’d work with a programmer who can handle the rest)

If you want to be more specific about neural networks, the minimum programming skills are just fine to get started.

So, “Should I learn ML algorithms?” the short answer is NO! 

“But I see some people claim that algorithms are efficient.” yes, it’s. And of course, the more you know, the better you will be, especially if you want to get hired by a leading company. 

That’s said, algorithms will help you find the best model or function to give any data to enable the machine system to predict information related to the real world. That means the algorithms try to interpret the abstract data into tangible and valuable information. 

That brings us to a reasonable question!

But what’s the point of learning these algorithms when everything is built-in and free to download?

It depends on how big the projects you will work on are if you want to optimise specific codes to manage an extensive database. 

Because you will have to set up your own models, each hypothesis or model building in the distribution process belongs to the distribution family. This family includes a tremendous number of possible models or hypotheses. 

Suppose you have a robust base of understanding of the concept of these algorithms work. In that case, you are able to place the given data (a set of labels or tagged samples) in the space (using algebra techniques. Again, algebra is fundamental). Now the data has taken the form of vectors or matrices to manipulate to find the perfect hypotheses. 

But what if you’re a beginner in the space and running a huge database is not for you? Then, go ahead and don’t bother yourself with optimised algorithms. There are multiple machine learning algorithms in Python. You can rely on them with less effort. Instead of immersing yourself in something daunting without real value, learn how can you change the ML algorithms when setting up parameters, for example. That’s how you should think.

Anyway, machine learning is not about understanding algorithms mechanics that are already placed and building on them to make these codes suit your needs based on the nature of the project. And then, you might optimise the accuracy of the database according to your tasks.

Consequently, it depends on what you expect from ML. Are you serious about it, or do you think it’ll make you look smart just because many people want to learn it these days? Do you just want a job, or you’re more fascinated with designing a human-level AI application?

Remember, there is not only one way. ML is just a little branch of programming, just like biology as part of medicine. To be a doctor, you don’t need to know the whole physiological mechanism. Just pick what will be beneficial for your endeavours.   

Check Out Scikit-learn

Many newcomers to the technical industry suffer from the bottom-up curriculum most IT teachers introduce. Unfortunately, that makes the learning process frustrating to many of us.

It doesn’t matter whether you know or not that Arthur Samuel was the first one to coin and define the term “Machine Learning.” It doesn’t even matter if you remember this definition or not. What you should focus on is how this field of study will put you in the position to give computers the capability to process, execute and learn without human help or explicitly programmed system.

It also matters to be familiar with popular terms when you learn the mechanism of ML. We have a list of all terms you should be aware of:

  • Classification: It’s about predicting separate variables and categories. Your ML system will help you make data-driven solutions by identifying some problems and creating predicted labels. For example, the machine can indicate if a particular virus infects the patient or not. You can use plenty of ways to tackle classification problems, including high or moderate accuracy. If you are searching for optimal performance, opt for Kernel discriminant analysis, Artificial neural networks (ANN), random forests, support vector machine or K-nearest neighbors. Otherwise, boosted trees, naive Bayes, deep learning, or logistic regression.
  • Clustering: refers to grouping a bunch of data with the same patterns or features and finding a label to title each group. These features include product details or client characteristics. You can implement a mean-shift method for high accuracy, K-means, hierarchical clustering, or topic models to solve clustering issues. It’s one of the most manageable tasks when processing data, just if you know what you want to know about.
  • Multivariate Querying: suggests an approach to finding similar objects. Many applications can enable you to do so irrespectively of acquiring new skills. Just try one of these methods; farthest neighbors, nearest neighbors and range search.
  • Dimensionality Reduction: It’s a cleaning and filtering process to get rid of random and useless data and variables in a dataset. It includes two primary methodologies feature extraction and feature selection. This feature is critical when you have so many features which can make the predictive task challenging. But applying this approach will cut the money and efforts in half. The most popular methods are independent component analysis. Also, you need to know some techniques like compressed sensing and principal component analysis. Non-negative matrix factorisation is an excellent place to start as well. But manifold learning/KPCA can guarantee you more accuracy. 

Learn Deep Learning and Neural Networking

It seems that you have to learn everything before you even start to understand. Seriously, yes. That’s why we’ve told you before that learning AI is an ocean, and there are many topics you have to be aware of. You don’t need to be an expert in each one, but at least you know the fundamentals. And it will be a big payoff. Your career will go a long way in the right direction, and your business will pursue growth and stability.

So, What are deep learning and neural networking?

Deep learning specialises in remodelling computing capabilities to be similar to human reactions, behaviours, and maybe feelings. It’s dedicated to creating instances like our brain patterns to make the most accurate decision. It works best with unstructured data. It’s not necessary to have everything nice and neat shown in well-designed shapes. This data can be videos, images, or audio. And All can be tackled by deep learning applications. As a result, you’ll get a beautiful presentation learning that can be doable and applicable on different levels. 

Neural Networking is an artificial system which tries to mimic the human brain. That’s why it’s called neurons, the most complicated thing worldwide specialising in computing operations.

What is it supposed to mean in terms of machine learning?

Neural networking works perfectly in breaking things down into small pieces of data and then combining them back up to find the most delicate patterns. The concept behind neural networking is to interpret numerical patterns via machine capabilities and turn them into data that can be used for different purposes.

Seems similar, right? Yes, but it shouldn’t.

Hold on for a second to understand the connection between deep learning and neural networking. 

Neural networking consists of only three layers; output layer, input layer, and hidden layer. Neural networking with multiple hidden layers is known as a deep learning system. That’s why it’s called deep, which refers to endless hidden layers tucked away to create detailed data like a human brain. 

So far, so good?

Bonus tip: To know more about deep learning and neural networking, visit deeplearning.ai, on which you can find tons of FREE courses or have a look at fast.ai and register for several FREE tutorials about both disciplines.  

Write and Read About Artificial Intelligence 

machine learning artificial intelligence

Here is how experts became experts: put into practice all you’ve learned through your journey by implementing real projects. Even share with other people your output and highlights of what you’ve found out or studied through a video or a blog post.

Also, you can write an article on Medium.

It’s one of the most effective ways to solidify the skills you have just learned. 

Just like me, when I decided to learn more about AI, I came up with writing in-depth blogs about it.

In a nutshell, if you want to learn about something, you must learn how to communicate something. And pick the best way to help you feel more comfortable, whether in public or even record a video about your experience that no one will watch but you.

Also, if you have gotten tired of complicated curriculums, it would be perfect to find exciting books about AI; here is our favourite to start with:

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

By: Wes McKinney

Release year: 2011

Size: 724 pages

Price: £10

“Awesome Book to Gain Practical Data Skills with Python!” Comment on Amazon said.

The author is one of the founders of Python, making it one of the most outstanding books to enhance your knowledge about this programming language. This book will take you around Pandas and how to process and organise your data. In addition, it will introduce you to other valuable Python sources like NumPy, Matplotlib, and Scikit-learn. Finally, it’s incredibly useful to understand ML concepts paired with particle tools to execute what you’ve studied with simple and step-by-step examples to follow.

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

By: Geron Aurelien

Release year: 2017

Size: 722 pages

Price: £28 (get a kindle copy for affordable alternatives)

“This book is long and dense and serves as both a guide and a reference.” So a review on Amazon said.

This title rings true once you browse the book to check its content. You’ll have a decent hands-on experience without redundancy. It will give you insights about Scikit-learn. It’s a particle book that will show you how even zero-experienced developers can use machine learning applications to analyse data. You will find intensive examples with a shortcut of theories just to make sure you have the right concept about different tools loaded with various charts. It offers an insider guide to understanding how and when you can use potential tools that will help you solve any problems you might face.

Machine Learning for Hackers: Case Studies and Algorithms to Get You Started 

By: Drew Conway and John Myles White

Release year: 2012

Size: 332 pages

Price: £33 (don’t forget to check a kindle copy if it’s expensive— which is yes, it’s expensive)

“Fantastic book, a must for data scientists, highly recommended!” A reader on Amazon said.

This book is an outstanding introduction to machine learning without too much bland text, just on the point. The book reviews 10 case studies in ML and how to solve the problem in detail in R language code. This methodology could be through offering improved training about using this tailored language. So, buy this book immediately if you intend to get through this area by R, not Python. Otherwise, it wouldn’t be the best choice if you’re new to the whole thing or have no programming experience. Another thing: because it’s a case study-based book, it could turn on you as some codes are outdated in the first edition. So, be sure that you pick the latest copy. Anyway, if you consider yourself a hacker, you will get information to give your knowledge a boost to go in the right direction. 

The Hundred-Page Machine Learning Book

By: Andriy Burkov

Release year: 2019

Size: 100 pages

Price: £25 

“This is a superbly written book, and a must have for anyone interested in machine learning.” Someone commented on Amazon

This book is for everyone who has no time to read millions of pages just to get started or someone who is searching for the on-demand takeaway of how to leverage their artificial intelligence background. Or even anybody who still asks if this technology domain will threaten our humanity or natural stupidity is more excellent. But if possible, explain all you need to know about ML? Seems a big yes! Just have to know that this book has been endorsed by high-profile technology gurus, like Sujeet Varakhedi, Head of Engineering at eBay, and the Director of Research at Google, Peter Norvig. It will bestow the power to run tasks in an automated manner by appreciating and highlighting the critical areas of ML principles and digital transformation for individuals and enterprises. However, it’s just a good start, not the whole thing, and new books should be on the table.  

Are there any free books to start with? I have no money!

It seems it’s your lucky day! Yes, there are.

  • A. Aldo Faisal, Cheng Soon Ong: Download it from anywhere. It will take you through two sections; first, all mathematical operations you need to hone to learn ML, and second, it discusses matrix probability, decompositions, and statistics. (Remember, you don’t want to go so deep into algorithms, but it is just to have the whole picture)
  • Python for Everybody by Dr Charles R. Severance: It’s a must for everyone who wants to start from scratch in programming languages, especially Python and how it works. Again, it’s a free book that will help you understand coding, then take you to the next level to catch efficient information about advanced levels, and then you’ll find a few chapters about data science. If you want to self-teach yourself machine learning, nothing can be well more than this book. 
  • Linear Algebra by Jim Hefferon: Another free book about mathematical necessities. With a handy beginner guide, you will learn many things about linear systems, determinants, and vector spaces. Pick a pen and paper and write down all your notes and your reflection to be easier if you want to get it back.
  • Selfstudy vs Certifications

The internet makes learning full and free (simultaneously) of hassle. You might be an expert without even registering for a course. Or you might find yourself falling apart before even starting off. 

The thing here is to choose suitable sources. Like any space of specialism— cooking, for example, you have to assemble all ingredients before preparing your dish.

Yes, you don’t have to worry about your zero mathematical education or background.

This field is growing further that AI professionals haven’t graduated yet. There is no particular certificate required if you want to apply for a job right now. Even undergraduate folks can hold IT positions if they acquire the exact qualifications. And you should get credited for this before specialists in the field do so.

Simply, machine learning is for everyone who has passion enough to keep learning to understand and establish himself among other professionals by striking up queries and searching for answers.

The issue is what you can do, not what you can display on your resume.  

But the question remains outstanding, “Is self-study enough to be qualified in machine learning, or a certificated program would be much worth it?”

Only you have this answer!

Side tip: Having some much bunch of courses can get you far! Be specific. Start with only one course and then implement it directly. Then find another course, just follow the same procedure as before. 

If you’re the someone who starts something fed by super motivation and enthusiasm, and after just a couple of weeks or even months, this spark vanishes out of the blue, then self-studying is not for you, and you have to switch to another way. 

So, let’s break it down into two approaches- fanatical enthusiasts (people who should go for certificates) and normal enthusiasts (people who can handle it alone)

•    Fanatical Enthusiast

If you want them who want to get hired in a place that requires a specific certificate, then register for a reputable entity after doing good research and reading so many good reviews about it.

Be sure that the course will cover probability, calculus, linear algebra, programming skills, and statistics (remember, it applies to someone with non-experience in technical matters). 

Any academic degrees or certifications I should take?

Additionally, there are non-specific academic degrees to have. Still, we can recommend the best machine learning certifications like Machine Learning with TensorFlow by Google, Springboard’s Machine Learning Engineering Career Track, Professional Certificate Program in Machine Learning and Artificial Intelligence by The MIT faculty and more. (if you have any other recommendations for great courses, please leave a comment below)

Is there too late for someone who knows nothing about this field?

Absolutely not; there’s never a better time to enrol in a machine learning course to earn a certificate. This industry has just started, and you will definitely find a good place for you.

Bonus tip: Only advice here is to start with a free tutorial introduction about machine learning before spending thousands on something you know nothing about it. 

  • Normal Enthusiast

Overall, certifications are friendly but being someone who can tackle the whole experience on your own is something you need to be proud of. And, of course, you can be a self-taught AI expert.

All you need to do is certify your knowledge and skills by executing your projects and letting the employer see how brilliant you are, no matter your educational background. 

Actually, professional self-taught AI and machine learning specialists are around and can be a good example. 

Important: If you have an IT background, it would be better to consider learning about statistics and mathematical modules like we discussed in the prerequisites of machine learning.  

Well, here are the exact steps to follow to cut off the confusion and know what you should do right now:

  • Take advantage of the free courses we have mentioned before.
  • Build your own projects based on what you’ve learned.
  • Search for internships to have a hand in an actual workplace.
  • Search for people who can inspire you in your journey on LinkedIn or other platforms.
  • Don’t be shy to ask for help from experienced professionals. Many are happy to share their story with you.
  • Check the latest updates about software, codes, and libraries. It’s critical to be informed of technology trends. 
  • Share your progress with people and ask for feedback if that’s possible.
  • Once you have time and only, try to register for a superior fundamental course from a university. *note it’s not about gaining knowledge, but more about having a professional network to help you further, communicate when you need a referral and listen to other experiences.
  • Find a mentor to guide you in your learning journey. It could be a friend, a member of your family or even someone you don’t even know in the virtual world but intends to be involved with an insightful way to find out the best way to do things. And, of course, he should have some experience in machine learning.

Pro Tip: If you have some foundations in coding or mathematical operations, try to find someone who can hire you for a not-great-paid job and experience your knowledge through doing the task. Obviously, it’s the best approach to step inside the machine learning industry because you’ll never forget what you have learned through actual experiments, you have a great chance to surprise your boss and convince him to hire you for a full-time AI job, and (BOUNS) you’ll get paid while you’re actually learning. Remember that 🙂

Finally, don’t expect to remember or understand everything you’ll get read or study. Instead, practise what you have; things will be easier at advanced levels.

Important: Even fanatical enthusiasts will need to involve the previous steps for normal because a certificate or a course is far from enough. Let yourself immerse in more detail.

  • Become a Part of a Project that Interests you.

Machine learning is a lot like it sounds. It’s a world of various forms of technology, many subjects are involved, and many theories you have to be aware of. Consuming a long time in this part could lull you into a false belief that you’ve killed it. But once you go outside the marketplace, you’d be shocked that things are more complicated than they look. 

So, after you know all the fundamental prerequisites and basics to learning machine learning, you need to work through a real project to start hands-on practice.

You might be excited enough to apply the tutorial you’ve learned for long months. 

This step will fill with fun, challenges, and insights. But, except for everything, you need to be well-prepared to enjoy learning, progress faster, and stay motivated to do more.   

Important: some will encourage you to flip the curriculum upside down, start working on a project right away, and then get back to books and courses to implement the project or find a solution when things pop up. Yes, it’s the fastest way to get a solid grasp of ML, but only if you have enough background. No algebra, or programming foundation, then go back to the beginning of this post.

So, how can you get started on a project?

We outline what we think is the better way to be involved in ML projects for beginners: to watch an expert while working through a machine learning task. It might be an internship or an informal apprenticeship. But it would be an insider guide to know how to be in action.

This expert can be a friend, a coworker who works on such projects, or even someone on Linkedin who is willing to help you. 

Pro tip: You can start texting people who work in this field from now and communicate with them to ground a professional relationship to take advantage of later. Once you are ready to go out to improve your learning inputs by actual work, ask them if that’s possible. 

If you found this idea awkward, you might consider the second; check out public projects and run through programming codes and other data to learn how ML tasks can look like.

Every day, learn something from the codebase, watch the repeated patterns, take notes, and save this project because you might need it in the future to get inspired or conjure up some insights on how to solve a similar problem. 

GitHub or Kaggle are brilliant libraries to choose one project and apply what you’ve learned. In addition, these two websites give a platform for experts who can share knowledge and ML experience with a large number of people.

But please note that they’re brimming with tasks from machine learning, data analysis and an AI product. So, be selective and find small projects based on your level, interest and specialism. 

Search for keywords for topics of your interest and start moving along your learning path.

If you’re a newbie, you might wonder how to know what interests you more. Well, nothing but implementing different projects. Take time to check out other areas of tasks until you find something that energises you to immerse more in this process. It’s what so-called leaning circle. 

Thankfully, machine learning is an up-and-coming field with a wide range of applications in all sectors inducing education, entertainment, finance, health, and retail, among others. Therefore, there is no shortage of projects and industries that you can engage in.

And after you finish the first project, celebrate the moment and be ready to find another one. Keep doing this, and you’ll be more and more skilled in different topics. This way will enable you to have a better grasp of real-life projects. 

To make it super easy for you, we have compiled some ideas for projects you can go through to assess your skills:

Machine Learning 2

Take Advantage of Out-of-the-Box Models

It’s a simple way to work on the first ML project. You’ll learn and practice concepts and features to apply to different datasets.

What makes this project awesome is that it’ll give you the initiation to build a model fit to the problem. In addition, this approach will help you acquire new skills, which can be excellent for predicting missing data, which models fit project features and labels perfectly, and the concept you should think of when choosing the models.

Regardless of your previous knowledge about the subject, this hands-on practice is perfect because it will take you directly to place data on models without spending a great deal of time building hypotheses. 

Of course, you can check out the textboxes to find answers, but what’s fun about it. It would help if you tried hard to assess all possible models for the project to soak up the exact steps and upskill your mindset. That’s when the machine learning journey can begin. Remember, it’s always better to see in action than just knowing the answer.

Moreover, this kind of project will help you appreciate out-of-the-box models and the invaluable skills to dig through the internet to find such hypotheses which can ensure the growth and scalability of the business. It’d also be great to record all common patterns to enhance your background in prototyping models quickly. 

Important: It might be hard to explore which models can handle categorical features well in the real world without testing that. And because you already have models you should work through, this task will shed light on honing arranging and operating data steps.

Eventually, you can sum up what you will get from this experience in the following points:

  • How to manage the workflow of building models
  • How to spilt models into cross-validation sets to train and test data
  • How to focus on only important data
  • How to clean and get rid of invaluable data
  • How to reprocess data before going to the final step
  • What necessary transformations to master the results
  • What necessary engineering features you would need to implement real projects

Explore Movie Preferences 

No one doesn’t like movies, and when your work is based on listing movie recommendations for viewers, it’s one of the most rewarding experiences you can have.

The technology-enabled transformation of the entertainment sector has been dramatic. Everyone around the world uses the internet to watch movies and tv shows. These applications provide business owners with accurate and detailed data about viewers’ preferences, so they can invest more in this type of show to keep their users and guarantee the success of their production. In addition, it creates more customer touchpoints when using this service. 

So, you don’t have to search for interesting movies to watch on weekends. Most streaming applications cut off this daunting experience by recommending what you can watch next. Now, it does make sense when you use Netflix next time and find they already know what you really like by displaying it on the home page.

This feature lies in a looping multi-step-by-step process based on LM techniques.  

Thanks to millions of free datasets, try to handle this task as your first practice. It’s easy and interesting for ML learners. The movie project can be implemented by coding in either R or Python. 

Analysing Product Reviews

Collecting data is the first step for enterprises as they give them a track to monitor performance, drive leads and sales, and make more strategic business decisions.

But what should this data look like? How can they collect? And most importantly, what after gathering?

Today’s new methodology many businesses adopt is to analyse reviews on websites, social media, and anyone who says something about their product or service. In addition, business owners wield sentiment analysis to elicit sentiment in the customers’ texts to understand the customer experience, which will help businesses grow by augmenting physical offerings.

This analysis is an excellent example of ML applications widely used to improve the internal process by leveraging digital transformation and automation. 

We propose to go for a project like this one, which is in great demand and well suited for beginners.

Sources will make it easier for you to carry out previous projects:

  • Scikit-learn library for Python codes, and the caret package for R. (Have a look at the documentation pages for instructions and practice algorithms for classification, regression, and clustering.)
  • The Movielens Dataset (for the data of movies preference project)
  • If you have no idea how to use R for ML coding, check this presentation first by the author of the caret package.
  • Amazon product reviews data from Kaggle
  • US Airline data from Kaggle 
  • Searching for other topics to pique your curiosity! Check UCI Machine Learning Repository, one of the largest databases all around the world, covering thousands of industries.
  • Check up data.gov, including the official open database run by the US government. Many social science topics are available if you like these kinds of subjects.

Note: Kaggle has a bunch of different topics; feel free to opt for something else. 

  • Identify Your Weakness And Slove it

After this long long curriculum and long hours of handling projects, you must have a big picture of the skills, qualifications, tools, and knowledge you need to have or gain; you will be definitely surprised by what progress you’ve made. You can define your own specific goals, or you have already successfully achieved one of them (of course you’re, you’ve learnt one of the most ever-changing fields, and you can be involved in many industries).

At this point, implementing real projects has many advantages; one of them will inform you how little you know and how much you should know. You stumbled upon many issues while going through this journey, but it’s time to take your breath. 

Don’t be stressed trying to patch up all your skills to land a job or ground yourself in the employment market. Instead, calm down, and before working through another project, you need to write down your weekends or any gap in your knowledge. 

We know it’s not easy to face our defects. It requires honesty and transparency to admit and then overcome them.

But it’s not the case if you follow our tips to recognise all challenges you encounter along the way. 

  • Check out all projects you’ve crafted and conjure up all difficulties you found. (It’s better to do this step while working on the project and not to overlook any important skillset you ought to have)
  • You may do this evaluation after each project, but don’t go beyond three until you’ve identified the specific abilities you’ll need.
  • Compile a to-do list of all the skills you still need to master.
  • Divide your list into three columns; up to scratch, good but need improvements, and good enough for now.  
  • Rank them in order of priority or urgency (the urgency here might be a client project you have to finish or an interview test which you need to be well-prepared for)

Important: This step could be complicated more than it sounds because you’re most likely to wonder, “How I could even know what is more important than others”. Actually, you’re the only one who should tackle this and remember that defining what you should learn is the most valuable way to cultivate success in your career. (Check the bouns tips)

  • Now, don’t be surprised with what we’re asking you to do. “Write off all sets except the most important skill according to your rank. 

Why do we do that? First, eliminate any distractions or clutter caused by the influx of information and the high desire to know more. You need to be on focus as much as possible. And that can not happen in this case because being surrounded by such a list will add nothing but stress and strain. 

But what about other things I need to improve? Requirements change daily, and every time you process this evaluation, you will discover more about this domain and new trends to consider.

  • Dedicate 1 day or 1 week based on how far you want to go through this topic, and then assess your knowledge again with real projects
  • Continue this lopping process every time you practise on machine learning tasks.

Watch your progress at the end of each month. You’ll be impressed by your ability to write codes, build models, address problems, and efficiently run computing equations. Of course, the experience you’d gain will vary, but this approach will inject your technical skills more than you might think.

Pro tip: Check out the job description of positions you need to occupy with the same level of your expertise to have an idea about what requirements to get hired. Set up a plan and include these skills on the list.

Bonus tips:

  • Ask for feedback from other experienced AI engineers to help you determine which top skills are required in the job market.
  • Join professional groups and leave a question to discuss what you have to consider in the next round of your learning. (I usually use Quora)
  • Be updated with contemporary techniques that not everyone knows about it. (it will give you an advantage when applying for jobs)
  • See the most common techniques repeated in the public projects. It means it’s so essential that you can not discredit it.

“Accountablity is compounded by consistency and habit.”

  • It Might Take Months or Years

“I started learning machine learning two years ago. There were times I didn’t feel like studying,” Bourke said.

Yes, the path will be filled with bumps, and you have to adapt to the nature of a new lifestyle “Studying only for the sake of studying”.

In short, to know what it takes to be able to apply for ML jobs or build your portfolio to start freelancing services, you need to know how much you’re aiming to delve into this journey and from where you’ll begin. (Of course, non-experience and mathematical degree folks need to make extra efforts, among others) 

Generally, almost all LM courses take between 6 to 18 months, depending on the curriculum and the area of speciality you choose.

A six-month course is enough to gain the basic knowledge of machine learning, and you will be ready for an entry-level job. During these months, you will need to study as much as you can (6 hours daily at least) from mentioned materials or any specific toiled resources you’d discover through your path. But is that what you really aim for?

The most important thing is to be accountable for what you want to be.

Listen up: accountability matters a lot after a few weeks or months after you start out. At this point, your eunuchism and motivation might wear off, and your ultimate goal, “I want to work as an AI specialist at Google”, looks far. Your bed now seems more tempting to watch a movie and just be satisfied with your current career and salary. The whole situation will bother you, and you will not feel comfortable anymore.

It’s possible if you just let yourself be overwhelmed with jargon and concepts. That’s because of your high expectations that you can kill it in just a few months.

Instead, expect to spend long hours trying to debug your code and sometimes it doesn’t work out, and all you want is just to hit a brick wall and give up.

All of these feelings are NORMAL.

Be realistic! You’re about to learn about one of the most complicated industries in the world. It wouldn’t be accessible at all.

So, it’s important to remind yourself that it’s worth it. And don’t listen to your mind chatter when discouraging you by striking questions like, “maybe I just too dumb to think that I can do it!”

Here is how you can handle this:

  • Accept your sinking feelings. In the early stages, everyone experiences the avoidance of getting out of their comfort zone. You’ll feel weak, frustrated, and distracted. 
  • Be responsible and remember your dream. Writing down your goal and making it SMART (specific, measurable, attentive, realistic, and time-bound) will push you through these terrible ideas. That means it should be something like, “I want to roll in an introductory course about machine learning and finish it in 2 weeks.” Write it down in your own words and determine the timeframe that will make you feel more comfortable and confident in accomplishing this mission.
  • Don’t consume so much time just learning. It will drain your energy quickly without even getting close to your purpose. Supercharge your path by providing a straightforward solution for yourself to be stuck to your schedule. So, liven up your acquired knowledge by implementing. 
  • Be obligated to explain, justify and take responsibility for your actions to yourself. In other words, just create justification for yourself such that when you want to give up more than anything else, you simply don’t because you have strong reasons to resume. With this mindset, you will never leave the whole thing and go because you realise that the consequences of giving up are harsher than going through it.
  • Define what introspection means to you. That means figuring out what matters a lot to you. People vary and what they think is the best incentive can be weird. So, spend time weighing your options; is that money, prestige, or maybe family and your social life. And try to link this to what you love more.

Fun fact: For me, When I look back on my life and see all the things I’ve accomplished, it makes me feel better about myself. So, I keep working to keep this feeling going underneath my skin. 

  • Build your own desired habits by keeping studying machine learning on the same days at the same time. It’d be easier to adapt yourself to this new lifestyle.
  • Try to get involved in even small projects as an assistant, if possible. Once you get paid, it will motivate you to complete your learning process because you found it already paid off and can do it.  

In a nutshell, it depends on what’s your goal from learning something very advanced like machine learning. Don’t expect to be an expert in just a few months or even years. Or, to be honest, you should eliminate this world from your dictionary. No one can be an expert in something, especially in the ever-growing domain.

The question is, what does it take to be able to get hired as an ML engineer. Then the answer will be 6 months if you have a developing or programming background. No background, then it might be 3 years. (Or maybe more)

If your question is what it takes to learn machine learning, the answer is all your life. Although you’ll be far from saying something like, “That’s enough!” there are many resources, many information, many industries, and many techniques around.

Mastery is a lifelong process. 

  • You Might Not Find Your Desired Salary Package 

One of the most common factors that drive people to study machine learning is well-paid vacancies. You’ll find many satisfying results if your browser searches for the annual average of salaries for AI or ML engineers. 

Or maybe you come to this blog because you have done it before.

Let’s picture the scene before clicking through this link:

You: I want to learn new skills in high demand.

Then you are searching for the best job opportunities.

You: it’s tempting, then what about salaries for people in this industry.

$131,001! Wow!

Yeah, this is true, according to Glassdoor. And there is no surprise about it. It’s an enriching career path, and because many businesses are clung to introducing a new ear of digital transformation, numerous vacancies are being created.

This substantial shift in running the business makes machine learning one of the most indispensable tools to achieve goals. Ml helps companies remain competitive in their industry, and specialised engineers are in high demand.  

But is this really the case? Or how easy is it to get a job and win this unbelievable package?

The entry-level ML engineers can get $94,611, according to PayScale. Of course, it can vary based on your location and the company’s size, but it is still a lucrative deal. 

You can be a fresh graduate, you can be with zero experience and, although, you can land one of these jobs.

Despite that, most companies require many requirements, including programs, skills, and a solid knowledge of machine learning concepts. For example, a company like Google requires a PHD to be hired as an ML engineer. 

To put it another way, you’ll need to work hard to earn a fair salary.

So, what minimum skills do you need to get hired as a machine learning engineer?

According to PayScale, the top skills you ought to have, no matter what it’s your experience level, include:

  • Big Data Analytics
  • Data Analysis
  • Deep Learning
  • C++ Programming Language
  • Natural Language Processing
  • Python
  • Image Processing
  • Software Development
  • Computer Vision

It’s elementary to build a professional resume and portfolio, whether it includes the only own projects or other solutions you have made during your learning path.

After that, the main driver of the salary range is your experience level. Then you’ll be confident to apply for other jobs and negotiate the package salary you desire. 

  • Don’t Expect Life Balance

Machine learning engineers get well paid and can work in multi-international companies many people dreamed of. Machine learning engineers are often satisfied with their career path, social position, and other countless benefits. Machine learning engineers primarily deal with exciting and complicated areas such as artificial intelligence.

Then, how does their life compare to ours? Does it take much effort to be part of such a fast-paced job?

It’s one of the most controversial matters related to technical jobs, not just machine learning engineers.

“I work with data all day. I wrangle data all day. I massage data all day. I build pipelines for data all day,” Mike West, a machine learning engineer and data science analyst, said.

And if you decide to take the same track, don’t expect something but long hours of working, researching, mentoring, analysing data, model optimisation and development, designing datasets, assessing the results, checking matrix, and attending meetings.

And study time should be included in your list almost every day to learn new concepts and figure out creative tools. Of course, senior engineers have to undertake supervision duties, like enabling employees to use the best digital tools, providing the team with insightful feedback, reviewing the overall performance, and discussing what can be done for a better outcome.

You might need to work from 9:00 am to 10:00 pm. Most of the time, you’ll have to resume your workflow at home by double-checking the emails to reply to the client’s request or even taking instant action if needed.

What do you think about this lifestyle? Is it right for you?

Is this stressful? Yes, it’s. Is that rewarding? Of course, it’s. Once in a while, you can take time off. And if so, you’ll need to make time for studying.

It’s important to know that before even getting started because there is a common misconception about a machine learning career— some people think it’s just a computer programmer. Basically, it’s a bit part of the whole industry here. You’ll be able to teach a machine to do things for you. However, the amount of time and effort required to complete the given set of tasks is also substantial.

But if you’re insisted on balancing your life, listen to this, “If you desire a successful career, life balance is a myth,” Salah Abo El Magad, an Egyptian entrepreneur. 

  • When You Should Not Learn Machine Learning

For those still interested in learning more about machine learning, we’ve included this part as a rallying cry. 

Everyone encourages you to take an ML course and step inside this field because of its enormous potential. But no one will tell you when you should skip the whole idea of learning machine learning. 

Like in any other business, not all occupations are right for everyone, and you need to figure this out early on to avoid years of heartache later on down the road.

So when you shouldn’t learn ML:

  • When you don’t have a plan unless you need to give up quickly, the machine learning curve can take time to get to the peak, and with a plan, your journey will be a lot easier and more precise. 
  • If you don’t know what problems you need to solve, things will quickly get out of hand. It’s not about creating programs that assist machines in doing their job without being directed explicitly because many concepts of ML can use in different directions. Just focus on what you’re working on to use the best digital resources.
  • You wouldn’t start your career if restricted to working for big tech firms because you would have to invest heavily to get a reputable certificate like a PhD. And it makes sense that you can find a well well-paid job. But, dare I ask, are you up for it? (That’s what you’re trying to figure out!)
  • If all you want from getting into machine learning is to have a job, you’ll absolutely fail to find any job. Machine learning is not the easiest way to build a career. You might get fooled by the number of vacancies offered everywhere, and this demand will increase exponentially in the coming decades. Still, the barrier of entry for these jobs will not decrease either. It would be best if you were committed to going a long haul. Yes, the world has a lot for you but for those willing to offer a valuable thing. 
  • You cannot go far in this career if you’re not ready to get under high pressure. And not this kind of pressure you write in the resume, and you don’t mean it. It’s real stress, and there is no room to mess up. It will end up working for more than 80 hours, and your brain should remain active and focused on the tiniest of details. It’s not a good thing for most people, but others still have the robust will to do so.

“It doesn’t matter the information you have on your head if you don’t know how to implement it!”

Takeaways You Should Know Before you Go to Learn Machine Learning

  • If you have a clear notion of what topics you need to explore and what topics you can put off until you reach a more advanced level, studying machine learning may be a lot easier.
  • The best way to learn machine learning is non! Yes, that’s it. We’re unique individuals with unique learning requirements.
  • Start with what will feed your curiosity! (And yes, you know what it’s!)
  • If you find the best way to start reading about ML, go for it now. If you think you’ll like to be part of a group in a class, no one can doubt that. Online materials are super. Youtube channels are the best invention in the history of humanity. What matters is just get started now, not tomorrow, not the next hour! Just now!
  • “I can’t find what feeds my curiosity; what should I do?” That is a good question as well! So, try all of them till you find what fits you better. Not copy others, be yourself and find your unique way.
  • If you take the self-study approach, just remember that one online course will not be sufficient. It can give you one, two, or even three ways to do things, but there are still many ways you need to know. 
  • If you choose the certificate approach, the same thing applies to you (One certificate is not enough).
  • Practise, Practise, Practise. Immediately, put the knowledge you have earned into practice unless you want to forget how things should be implemented. Actually, project-based learning is where real learning begins.
  • Don’t allow the tremendous amount of online material to distract you. Start with any available online, free or paid. And if you choose to pay, pick the lowest price! Yes, it’s true. It doesn’t even matter from where you’ll learn. But it matters that you have something to bring to the real-life, not just theories and redundant information. 
  • 20% of people who started learning ML online have the ability to implement real projects! Don’t be one of them. 
  • Inject yourself with motivation and studying habits all the time, and you’ll be surprised with the results. Indeed, it will maximise your chances of successfully learning all the technical skills you are willing to earn.
  • To choose the best ML algorithms before you begin, you need to define the type of your data and the type of task you need to handle.  
  • Don’t be overexcited to start working on a machine learning project without understanding the principles we have reviewed above. Practice is a significant time investment, but if you start it very early, you might get frustrated because of many steps or tools you don’t have any idea about. Start step by step and have a hand on a project when you have at least a foundation of ML concepts. 


  • How to Know is just enough of what I have learned?

You can’t know! But it’s always preferable to learn minimum and do many projects.

Listen, if you’re interested in machine learning or any technological topics, you might be wondering whether or not be possible for you to be an expert in all machine learning applications, concepts, and tools. Technically, it’s not possible at the same time. There are not enough resources. There are too many of everything, even introductory courses, books, and videos.

It’s super easy to fall into the trap of the paradox of choices. As a result, you will be overwhelmed without taking any actual steps. “Forget the whole thing!” will be your attitude. 

So, start implementing what you learned from any course, choose a project to set up, build your CV even if you don’t have any experience, and apply for senior jobs or internships. 

  •  What are the Types of Statistical Models I Should Know?

Descriptive and inferential. The first is about numbers describing and summarising your data to be more meaningful.

Inferential data provides a conclusion from one sample or dataset instead of the entire data. What matters to ML engineers is to know these terms when handling their database; mean, median, standard deviation, histogram and outliers.

  •  What are the Different Probability Rules Every ML engineer Must Know?

Bayes theorem, product or chain rule and sum rule. Nothing more than that. Only a few machine learning engineers might work on more but stick with these first. 

  • What are the Basics of Calculus I Need to Know Before Learning Machine Learning?

It’s a critical part of ML algorithms. However, a coding library will be helpful for you to get engaged in a machine learning career; there are some concepts you might have something about it; partial derivations, gradient or slope, and basic knowledge of integration and differentiation.