Machine Learning Roadmap 2022 – Codelivly

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What is machine learning?

Roadmap for machine learning : Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to be more accurate in predicting results without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

Recommendation engines are a common use case for machine learning. Other popular uses include fraud detection, spam filtering, malware threat detection, business process automation (BPA) and predictive maintenance.

Why is machine learning important?

Machine learning is important because it gives organizations an overview of customer behavior trends and business operating patterns, and also supports the development of new products. Many of the leading companies today, such as Facebook, Google and Uber, are making machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies.

If you want to learn machine learning with Python. Expect you to be familiar with the following concepts:

  • Change
  • Mathematical operators
  • Control statements
  • Data structures (list, set, plywood, etc.)
  • Working with files
  • Functions
  • Object-oriented programming

If you are unfamiliar with Python, there are several ways to learn this simple and powerful language. You can take some courses in Udemy, Coursera etc.

lets start

Step 1: Choose a programming language and get started!

The first step to start learning machine learning is to pick up a programming language. There are different programming languages ​​on the market, but the most suitable for machine learning are Python and R.

I recommend Python. Why? Because it is popular, easy to learn and ready for the future

With Python, you can switch domains easily. Python offers popular frameworks like Django and Flask for back development, Tkinter for GUI development, Pygames for game development and so on.

If you go with Python, you must learn sklearn for machine learning. Scleran Is a modern machine learning library written in Python.

The best thing about sklearn is that most machine learning algorithms have already been written for you. It has a lot of useful departments for early processing of your data for further analysis

If you want to learn machine learning in Hindi you can learn from it End-to-end machine learning video A YouTube channel where he will guide you through the steps to deal with a machine learning problem from scratch.

You should also check out the Tensorflow module, which can help you build a neural network without much effort!

Step 2: Learn linear algebra

You need to learn linear algebra if you want to master machine learning and become a pro!

This is essential because if you want to tune your models with maximum flexibility, you need to know how they work, and knowing linear algebra is a must for that!

When you start, you need to focus on step 1, and while you are following step 1, you can start learning linear algebra in parallel. This is what I call the parallel occupation technique.

You start two similar things at the same time, focusing on the first and maintaining relatively less priority in the other tasks. It can help you stay enthusiastic and motivated.

One of the resources I found very helpful for revisiting the concepts of linear algebra was this pdf Remarks.

Step 3: Learn probability and statistics

A basic understanding of probability and statistics is important when it comes to controlling machine learning.

Here is one of the best resources for this: Statistics Update Notes by MathBox.

Because the basis of Machine Learning concepts is derived from statistics and probability, familiarity with them and mastery of statistics and probability helps a lot in understanding ML concepts. You can study them at KhanAcademy course. You should be familiar with the following concepts:

  • Categorical and numerical data
  • Average, condition and median
  • Standard deviation and variance
  • Co-variance
  • adapter
  • Distortion
  • Random variables
  • Distributions
  • Classical probability
  • Conditional probability

Step 4: Learn Core ML algorithms

Once you have an idea about using sklearn after learning python, you need to start testing how these machine learning algorithms work.

While using sklearn, the ML algorithm is a black box written by sklearn developers.

To get an idea of ​​how these machine learning algorithms work from the inside out, check out:

  • Decline in slope
  • slope
  • Supervised learning versus unsupervised learning
  • Study with reinforcements
  • Basic linear regression
  • Work of all such similar models
  • group

An amazing resource to learn about all this is a book called “Practical ML with Scikit Learning and Tensor Flow.” (Not affiliate link)

Try to grab a copy of this book. It will help you a lot.

There are also some other resources worth checking out:

How to read a book
  • Schedule your reading time
  • Try flipping the pages and looking for exercise questions
  • Now try to find the answers to these questions while reading
  • These are the points that the author of the book wants to focus on
  • Try using the Microsoft Edge voice reading feature. It works pretty well

Step 5: Learn Python directories

  • Learn Numpy
  • Learn pandas
  • All this will help to debug python / sklearn

Step 6: Learn Layout

To host your machine learning models with a strong rear edge, you will need to learn frameworks like Django and Flask.

Docker and Kubernetes can be of great help if you want to ship and deploy your models quickly!

Streamlit is worth checking out if you want to build custom web applications for machine learning and data science.

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Machine learning resources

These are the resources you can use to become a machine learning engineer or deep learning. All resources are available for free online. Please check their appropriate licenses.

Machine learning theory

Deep learning theory

Theory and code of forward and backward expansion

General machine learning with Python and Skit-learn

Inverted neural networks with TensorFlow / Keras

Strengthening learning theory

Reinforcement learning with TensorFlow / Keras

Repeated neural network theory

Recurrent neural networks with TensorFlow

Applied mathematics for machine learning

Deep learning environment

The best books

Personally, I found books to be the best source of knowledge after passing the courses. This is where you can reinforce your theoretical understanding of the concepts you use in your ML projects.

1 – A hundred-page machine textbook By Andrei Borkov
A very short book but with perfect knowledge. Andrei compressed all the essential points in AI / ML and put it in this 100-page book[138 to be precise].

2 – Practical machine learning with Scikit-Learn, Keras and Tensorflow 2.0 By Orlin Jaron – O’Reilly
In my opinion, this book is an alternative to Machine Learning and Deep Learning specializations by I prefer this book because it has perfect explanations and each concept has a good code to try side by side. You can also access open source code from this book at the following link –

3 – Deep learning book By Ian Godflo
If you want to delve deeper into the mathematical side of deep learning then this book has everything you need. It was released in 2015, so it’s relatively old but the content is great.

Bonus book

Life 3.0 By Max Tagmark
Life 3.0 is not intended for the study of artificial intelligence and ML, but it is a beautiful book that discusses the impact of artificial intelligence on the future of the human race and cosmic influence. The author’s opinions are interesting and it is indeed a great read.

Machine learning theory

As a machine learning engineer, you need to be a master of the following concept:

  • Clean data
  • Full of missing value
  • Drop any feature
  • Feature selection
  • Scaling
  • Regulation
  • Feature Engineering (Optional at First)
  • Regression algorithms
  • Simple linear regression
  • Ridge and Lasso
  • Multiple linear regression
  • Polynomial regression
  • XGBRegressor
  • Classification algorithms
  • KNN (K Nearest Neighbor)
  • Logistic regression
  • decision tree
  • Random forest
  • naivety
  • XGBClassifier
  • Cluster algorithms
  • K-Means
  • DBSCAN (Spatial Cluster of Density Based Applications)
  • Dimension reduction
  • PCA (Main Component Analysis)
  • LDA
  • t-SNE

Practical machine learning

You have to learn sklearn Apply all the concepts of the last step. There are also many courses for this library. You can also use sklearn Documentation.



Machine learning is a hot topic these days, but it can be difficult to know where to start. This road map will help! We will go through the various steps required for anyone who wants to become an expert in this field and take his career from “beginner” to the “expert” level!



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