How to start Machine Learning career (ML)? | Complete Guide

So, You want to start machine learning career. But unfortunately, you have no idea of where can I start? How can I start a career in machine learning? 

I want to ensure you will get every possible information regarding machine learning, which will be quite helpful to start your career with this vast technology.

After reading this article, you have a clear view of Machine Learning. Accordingly, you can save your time.

Because on this page you will get to know about some resources such as, which books to read? What are the programming languages I should learn? What are the tools I can use for Machine learning? and Job responsibilities of an ML Engineer.

Also, do not forget to check notes and tips on this Page. 

So let’s start our journey with the most common and important question.


What is Machine Learning?


Machine learning is the domain of Computer Science that gives the ability to the electronics machine to learn without being programmed.

I want to clarify the meaning of “Not being programmed”.

The electronic machine is not being programmed to do the task, it learns from the scratch. That type of electronics machine we simply know as Artificial Intelligence.

In other words, Machine Learning Provide the data to the Artificial Intelligence to learn the things.

For example, like we humans learn from our past experience. Identically, machines are also able to learn from experience.

But, for the machine’s data work as an experience.

If you want to know more about the Artificial Intelligence then click here



Types of Machine Learning


Machine Learning typically divided into three categories.

1. Supervised Learning:

You have to train your machine with every possible input with corresponding output.

The training process continues until the machine achieves the desired level of accuracy on the output.

After the sufficient amount of training with data, the machine can generate an output according to any new input. This method of learning is called as supervised machine learning.

Example of Supervised Learning is student-teacher relation. In other words, the teacher in the classroom teaches students every possible solution to the questions.

2.Unsupervised Learning:

In this learning process, we give only some sets of inputs to the machine.

After some sort of time machine is able to generate its own logic and structure between different inputs to solve the problems. That is called unsupervised machine learning.

Example of Unsupervised Learning is a friend to friend relation. Like one of your friends gives lots of advice to you. Then it’s up to you which advice you will apply to solve your problem.

3.Reinforcement Learning: 

In this learning method, you only have to provide the task to the machine.

Machine try to solve this task on its own based on the previous experience. After achieving the task output, we give feedback to the machine that how good the decision was.

Machine stores that feedback on its memory, whether it is good or bad. The process of learning through rewards and recognition is known as reinforcement machine learning.

The best example of reinforcement learning is parents child relation. Whenever kid faces a new problem, the kid tries to take a decision on his own using past experiences.

As parents, we appreciate the kid and tells him about the decision was right or wrong.




An algorithm is a set of rules. In the algorithms, these rules are mention step by step.

With the help of algorithms machine able to know, what is the next possible move I can take to solve the problems?

If you want to start a career in machine learning then you should know about these 10 common algorithms for machine learning.

  1. Linear Regression 
  2. Logistic Regression
  3. Support vector machine (SVM)
  4. Random Forest
  5. Decision Trees
  6. K-Means
  7. Gradient Descent
  8. Naive Bayes
  9. Dimensionality Reduction Algorithms
  10. K-Nearest Neighbors(KNN)





  • Programming Languages: Python and R are two leading Language for machine learning. Both are good languages, you can choose any based on your preference.
  • Maths: Mathematics plays an essential role in ML Engineer. You must have to cover these topics before starting the journey of Machine learning.
Linear Algebra
  • Cloud Computing
  • Algorithms


Tips: I will personly recommend you to go for Python, based on my research.

Python is the most used language for machine learning by professionals.

That means, if you got stuck in any problem then chances of getting your solution is more.



Tools use by ML Engineers


  • scikit-learn
  • TensorFlow
  • Keras
  • Theano
  • Spark
  • hadoop
  • Amazon Web Services(AWS)
  • Microsoft Azure

Note: Google TensFlow is the most used framework.



Job Responsibilities 


  • Explore & extract insights from a massive range of structured and unstructured data.
  • Apply data mining and machine learning to improve content understanding.
  • Develop and Improve the accuracy of machine learning algorithms.
  • Analyze source data and data flows.
  • Design and implementation of machine learning models.
  • Explore data assets available and identify the right data sets.
  • Quality assurance and testing of analytical routines and data frameworks.


Note: To pursue this career, the minimum qualification is bachelor’s degree. Bachelor’s degree in Computer Science or Maths have some advantages over other candidates.


Cheat Sheets 


Cheat Sheet is a supportive guide for the Machine Learning Engineers because it is not possible to carry all the heavy and thick books all the time.

The cheat sheet is in soft documentation format and contains very few pages. So its make easy to find the solution as compared to books.

Machine Learning/Data Science is a very big field and it is growing fast. It is obvious, that we cannot remember all the algorithms, tools, formula and functions. That is the reason, we need a cheat sheet.

You can download all the Cheat Sheet to click here



These Books helps you to start machine learning career


These books would be a great help to strong machine learning skills. 

  • R Machine Learning By Example by Raghav Bali, Dipanjan Sarkar

This book provides information about mining the data from state-of-the-art techniques to make data-driven decisions. Case study based approach helps you to understand how to use machine learning in various areas. You will learn the basics of machine learning to the advanced concept.


  • Machine Learning with R written by Brett Lantz

This book helps you understand the application of Machine-Learning methods using R. It enables you to get an insight into the real world applications through its elaborate examples and illustrations. Most of the algorithms in this book are explained in the simplest and easy-to-understand way. 


  • Pattern Recognition and Machine Learning by Christopher Bishop

 If you want to learn machine learning to its depth then this book is a good choice for you. It uses graphical models to describe probability distributions. It gives you a purely mathematical perspective of looking at ML.

Note: This book is not for the beginner, I would refer you to go to other books to start your career in Machine Learning.


  • Data Science from Scratch: First Principles with Python Book by Joel Grus

In this book, you will find out the most fundamental data science tools and algorithms. Implement models such as Naive Bayes, k-Nearest Neighbors, linear and logistic regression, neural networks, clustering and decision trees. You will also learn the basics of linear algebra, statistics and probability theory.


You can also read those free eBooks


  • Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David. Cambridge University Press

This book helps you to provide a comprehensive theoretical account of the basic ideas of machine learning and the mathematical derivations that transform these principles into the actual algorithms.

Download from here


  • Machine Learning Yearning by Andrew Ng 

In this book, you will find some useful knowledge such as Debugging inference algorithms, Error analysis etc.

Download from here


  • Think Stats: Probability and Statistics for Programmers by Allen B. Downey

Think Stats gives information about Probability and Statistics for Python programmers. In this book, you will find real datasets, which is helpful to understand the real-time industries problems.

Download from here


I hope this article was a great value-add to your knowledge. And gives you proper guidance to start machine learning career. If you have any doubt related to this career option you can personally contact me. 



Let me know what you think about Machine Learning(ML). Share your opinions and experiences in the comments below!

Mohammad Irfan

I started blogging because of my passions for learning and sharing. Before creating, I worked as Network Security Expert.

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