Roadmap to Learn Machine Learning

In Machine Learning, we use data and algorithms to build intelligent systems. It doesn’t matter if you want to become a Machine Learning Engineer, Data Scientist, or Data Analyst. If you want any job in the Data Science field, you need to know about Machine Learning. So, if you are looking for a Machine Learning roadmap to learn Machine Learning step by step, this article is for you. This article will take you through a complete roadmap to learn Machine Learning with learning resources.

Roadmap to Learn Machine Learning

Below is a complete step-by-step roadmap for learning Machine Learning. You will also get the best resources you can follow at each step of the roadmap to learn Machine Learning.

Start with the Fundamentals

Start with the fundamentals of Machine Learning. The more time you will spend learning the fundamentals better you will be while choosing the best Machine Learning approach to solve a problem. In the fundamentals of Machine Learning, you will learn about:

1. Types of Machine Learning techniques
2. Types of Machine Learning Algorithms
3. How do different types of Machine Learning algorithms work?
4. The mathematics behind Machine Learning Algorithms and many more.

Below are some of the best resources you can choose from to learn the fundamentals of Machine Learning:

1. Understanding Machine Learning: From Theory to Algorithms
2. Mathematics for Machine Learning
3. Machine Learning Crash Course

Learn Python Libraries for Machine Learning

The next step in the Machine Learning roadmap is to learn the essential Python libraries for Machine Learning. Learning Python libraries for Machine Learning is valuable before getting your hands dirty with Machine Learning.

To implement Machine Learning, we need data and algorithms, so we need to learn a programming language like Python and essential Python libraries to implement algorithms on data. So, below are all the Python libraries you need to implement Machine Learning algorithms:

1. NumPy
2. Pandas
3. Matplotlib
4. Seaborn
5. Scikit-learn
6. Tensorflow

After learning all these Python libraries, you can move to the next step of the Machine Learning roadmap.

Learn and Implement Machine Learning Algorithms

After learning the essential Python libraries for Machine Learning, your next step should be to learn Machine Learning algorithms and implement them using Python. Learning and implementing algorithms will help you to understand how algorithms work and how to input the parameters of every algorithm. Below are some of the most important Machine Learning algorithms you should learn step by step:

1. Linear Regression
2. Logistic Regression
3. Passive Aggressive
4. Naive Bayes
5. Support Vector Machines
6. Decision Trees
7. K-Nearest Neighbors
8. Random Forests
9. K-Means
10. DBSCAN
11. PCA
12. Kernel PCA
13. t-SNE
14. Apriori
15. Neural Networks

Below are some of the best resources to learn and implement Machine Learning algorithms using Python:

1. Machine Learning Algorithms (Book)
2. Machine Learning Algorithms Full Course
3. 100+ Machine Learning Algorithms & Models

Work on Projects

After learning Machine Learning algorithms and their implementation using Python, start working on projects where you can use all the skills and knowledge you have gained. Working on projects will improve your ability to choose Machine Learning algorithms for different kinds of data and problems. You can find a list of Data Science and Machine Learning projects solved and explained using Python here.

Summary

So here’s a complete roadmap you can follow to learn Machine Learning step by step:

1. Start with the fundamentals of Machine Learning
2. Learn Python libraries for Machine Learning
3. Learn and implement Machine Learning algorithms
4. Work on Machine Learning projects

I hope you liked this article on a complete roadmap to learn Machine Learning. Feel free to ask valuable questions in the comments section below.