Machine Learning Tutorial 2022 – An Ultimate Beginners Guide


Artificial Intelligence, Machine Learning, and Data Science were the hot buzzwords that revolved around the past decade especially in the last few years. Following is a machine learning tutorial & resource guide that will help you start with ML concepts.

With the emergence of these technologies, many people around the globe are trying to get into these fields. Professionals ranging from engineers to entrepreneurs are keenly diverting their interests to these vogue tech professions. That is to say, we have to sync ourselves herein too! So, let’s understand the basis of terminologies, to begin this Machine Learning Tutorial.

What is Machine Learning?

Machine learning is essentially the study of computer algorithms that improve automatically through experience and by the use of data, often discussed as a subsection of Artificial Intelligence.

Though this term is prevalent for more than half a century, it has been gaining momentum only since the last decade. This is due to the rise of automation in the tech industry. Also, the recent reports show these jobs have a high growth factor and lucrative pay, as well.

Cracking the Machine Learning Interview on Twitter: "Salary compensation  variation of Computer Science jobs across different sectors in US.  #machinelearning #datascience #DataScientist #ai #artificialintelligence  #highestsalary #salary #compensation ...

The above image from 2019 from Business Broadway, shows that ML Engineer has the highest pay. The most important thing to keep in mind with learning these technologies is that these technologies are evolving rapidly. Still, unfortunately, so many colleges don’t have these technologies in their curriculum.

So in this article, we will walk you through how to master these technologies in 2021 which will not only help you in short term but also for a long term growth

Before starting into Machine Learning tutorial make sure you know about these topics

  • Roles in Machine Learning
  • Skills required for Machine Learning Jobs
  • Resources to learn Machine Learning
  • Skills to stand out from the crowd

Let’s dive in

Different Roles in Machine Learning

Generally, the words Artificial Intelligence, Machine Learning, and Data Science have been used interchangeably. However, the job descriptions might contain a few skills that might not be as common as others.

Well, before stepping into the world of Artificial Intelligence, Machine Learning, or Data Science, it is always better to be sure where exactly you find your interest? And what occupation, would you like to pursue? Aligning your interest and then making a move would be helpful later when you start applying for jobs!

Below are the major professional possibilities in the current tech industry which look out for Machine Learning skilled individuals.

1. Data Analyst

This role is closely related to the Data Scientist role but involves very little Machine Learning. That is to say, it majorly accommodates more of a Data Analysis and Data cleaning.


As a Data Analyst, your job will be to perform ETL(Extract, Transform, Load) tasks from Data Warehouses and Relational Databases, Create Interactive dashboards, perform statistical tests.

Required Skills

The essential skill required for this position is SQL, Python, Tableau/PowerBi, and Statistics.

2. Data Scientist

This role requires both Machine Learning and Data Analytics knowledge. Data Scientist performs analytics to drive better decisions which will help in Modelling.


As a Data Scientist, your job is to perform predictive analytics which will help the user needs, and also some analytics of the dataset that will help to drive proper business decisions.

Required Skills

The essential skills required for this position are Python, SQL, Machine Learning Libraries, and Statistics.

3. Machine Learning Engineer

ML Engineer is a person who is responsible for developing Machine Learning systems and infrastructure required for Machine Learning.


As an ML Engineer, your job is to develop Machine Learning algorithms, deploy, and monitor Machine Learning models.

Required Skills

The essential skills required are Python with Tensorflow/Pytorch, Java/Scala, C, Go, Docker, Jenkins, etc.

4. Machine Learning Research Scientist

Research Scientist is the people who work continuously on researching new Machine Learning techniques that will help in increasing the efficiency of ML Algorithms. This role is very different from other roles since it requires more math skills than any software engineering skills.


As a Research Scientist, your job is to research new and existing machine learning techniques, and publish State of the Art methods, later used in Industry and Academia.

Required Skills

The essential knowledge required is Python with ML libraries and Heavy Mathematics.

While having more roles can be confusing, these help us to clarify things and allow people to work upon their things.

These roles and descriptions are very general and some companies might have a different type of role as well, some startups might hire a person for a data scientist role. The work would be end to end. So, keep in mind that roles and titles don’t matter but skills and deliverables do!

Skills Required for Machine Learning

With the diversity of roles in Machine Learning, it can be overwhelming to learn Machine Learning. There are hundreds of courses available online to learn Machine Learning. In this section, we will look at what are all the skills that are essential for a Machine Learning tutorial? and where to learn them?

Generally, we can divide the ML skills into two, as follows:

  1. Programming skills
  2. Math and Statistics skills

While you can skip Math at the beginning you must not skip programming since most of the work would be on programming.

1. Programming Skills

When it comes to programming skills for Machine Learning, the essential programming languages are Python and R. Through Python vs R is a debatable topic.

We would recommend Python! And that is because it has wide community support and an ecosystem of libraries. The most important Python libraries in use in Machine Learning are Numpy, Pandas, Matplotlib, Scikit-Learn, Tensorflow, Pytorch, and the list goes on.

2. Maths and Statistics

Maths is very essential in Machine Learning. Machine Learning Algorithms are nothing but a combination of Linear Algebra formulas. Though you can skip Machine Learning at first, it is very essential to learn Math. The math skills requisite in ML are Linear Algebra, Calculus, Probability, and Statistics.

So, how to learn all these? Which one to learn first? Should we study Programming or Math first?

Though there are multiple roadmaps to learn Machine Learning, we would highly encourage you to create your path. Learning Programming and Maths side by side will help you a lot to improve your skills. This will dramatically save time, as well. Below are some resources that will help you to learn these skills.

Essential Resources For Programming Skills

Following are the important programs that you can employ to master the skill of programming. You can upgrade your knowledge in machine learning.

1. Python

Python is an essential language for learning ML. To start with Python, you can try out GUVI’s Advance Python Course. Get your hands dirty on essential concepts of programming such as OOP, try-catch exceptions as well.

2. Pandas, Numpy, Matplotlib and Scikit-learn

These are the essential libraries we generally use in any data science project. To learn about these libraries, we would highly recommend taking Kaggle’s micro-courses. These courses are short, concise, and up to the point. It will help you to initiate the learning with Machine Learning Tutorials.

3. Tensorflow, Pytorch

Tensorflow and Pytorch are the two major frameworks that we majorly use in the deep learning community. Third, on our list of machine learning tutorials, Tensorflow and Pytorch are two interesting courses recommended to all. To get started with Tensorflow there is an amazing Coursera course on tensorflow certification that you can check out. Therein, the trainer provides hands-on tensorflow practices. For learning PyTorch, its documentation is the best place to start with. It contains tutorials on all levels from beginner to intermediate.

4. Git, API, and Cloud– the essentials of machine learning tutorial

If we had written this blog before 2020, there won’t be this section. However, things have changed in this field and companies often require people who are more end to end. This is to say, employers look out for professionals who can do things starting from fetching datasets to deploying the model. Git and Github are version control platforms that will help to track changes in your codebase.

Well, to start learning to use git, this video by TechwithTim is really worth doing! Firstly, we use APIs mainly to extract data and use third-party services. To kick start your journey by mastering the APIs, we would highly recommend this tutorial.

Nowadays, for deploying and monitoring Machine Learning models, professionals highly use Cloud technologies. The major cloud providers in tech are AWS from Amazon, GCP from Google, and Azure from Microsoft. To start off choose anyone from above! Each website has its own tutorials which are better than any online courses.

Essential Resources For Mathematics and Statistics

Following are the resources that can help you build your knowledge base in mathematics and statistics. These are the foundations of machine learning.

1. Get Your Prerequisites Right

As we mentioned earlier, Maths is the basic underlining skill, any Machine Learning personal should hold. You won’t be able to understand the high-level stuff without grabbing the Maths skills. You can start Machine Learning with your high school maths understanding such as Linear Algebra, Calculus, and Statistics. We highly recommend the 3blue1brown channel on Youtube which teaches you the most essential mathematics in a very intuitive way with graphics and animations. For learning statistics, you can also check out Joshua Starmer’s channel– Statquest. The concepts provided in the channel are more than enough to understand the essentials.

You may follow Machine Learning Course by Andrew NG, as well. This is one of the fundamental courses in Machine Learning, taught by Andrew Ng. It is a watered-down version of CS-229, which is taught at Stanford University. You can access this course both on Youtube and Coursera. It is a great course and it teaches you the basics of Machine Learning — Regression, classification, various ML algorithms, etc. Released in 2011, it is one of the best courses of all time.

2. Deep Learning Specialization

In this five-course specialization taught by Andrew Ng, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. The course material will cover all the maths behind deep learning.

The programming assignments are structured from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory but also see how it is applied in the industry. You will practice all these ideas in Python and in TensorFlow.

Skills to get a job in Machine Learning

The field of Software Engineering, particularly Machine Learning is different from any other field, owing to the growing competition. New technologies are emerging every day. You need to stand out from the crowd, The certificates that you receive from the online courses won’t be enough to make a huge difference in getting a job. On the other hand, you will need to produce more real-time projects and portfolios demonstrating hands-on experience. Below are some tips and tricks that will help you to stand out from the crowd and get a job.

1. Personal Projects

Projects are the most important thing when it comes to evaluating your skills. Make sure you do projects which showcase your skills. See to it that the project should not be taken from existing GitHub repos or kaggle kernels. Try to do it on your own! Though you can refer to those existing methodologies, make sure you know what you do!!

2. Participate in Competitions

Competitions are the best way to evaluate our skills. External Assessments can help us harness the skills required. Moreover, It also helps you with networking opportunities where you meet new people who have similar interests as yours!

Below are some of the famous competition websites for Machine Learning,

Apart from these machine learning tutorials, we encourage you to participate in other portals that you find intriguing.

Final Words

So, these are the most important attributes that you must know before starting to learn Machine Learning.

We suggest you should make sure to give more importance to your personal projects and improve your programming skills and remember this is a long journey, so take some breaks and have some faith in yourself. To master any tech skill and kick start your rocking tech journey, this is the most visited place!

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Ram Kumar
Ram Kumar

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