Reading Time: 2 minutes

I often see many questions by people (mostly self-learners) newly coming into this grossing field that “how can we start to learn Machine Learning?” and “What is the curriculum that every self-learner must follow?”.

I already created a separate post for how can I learn machine learning? and this is the post for second question i.e. what is the curriculum of machine learning for self-learners?

There are plenty of curriculums already made over the internet but I connected with various data scientists in my connections having rich experience in this field.

So after summing up all of the discussion. I am creating this curriculum for you. You can follow that in order to master machine learning.

Note: This covers only Machine Learning not deep learning

I have divided the curriculum into three levels.

• Level 1 (Complete Novice).
• Level 2 (Knows Mathematics but not Data Science basics).
• Level 3 (Knows Mathematics, Data Science basics but not ML).

According to your level of expertise you can iterate between different levels accordingly.

I am specifying a high-level curriculum for Level 1 and Level 2. As it is the prerequisites required for learning machine learning.

Level 1:

The first level involves learning mathematics.

Learning Mathematics:

1. Linear Algebra
2. Calculus
3. Probability and Statistics

If you are wondering where to learn. Please follow this [Link]

Level 2:

The second level involves learning Data Science.

1. Numpy [Link]
2. Pandas [Link] [Chapter 1] [Chapter 2] [Chapter 3]
3. Matplotlib [Link]
4. Seaborn [Link]

For additional resources , Please follow this [Link]

Level 3

Machine Learning Curriculum

1. Importing the Dataset + Practice
2. Exploratory Data Analysis + Practice
3. Data Preprocessing + Practice
4. Handling Missing Data + Practice
5. Feature Scaling and Selection + Practice
6. Scikit Learn Library
7. Bias Variance Tradeoff
8. Introduction to Supervised Learning + Practice
9. Linear Regression with one variable + Practice
10. Linear Regression with multiple variables and Regularization + Practice
11. SVMs + Practice
12. Logistic Regression + Practice
13. Naive Bayes + Practice
14. Decision Tree + Practice
15. Introduction to Ensembles + Practice
16. Random Forests + Practice
17. K-Nearest Neighbour + Practice
18. PCA + Practice
19. Introduction Unsupervised Learning
20. Clustering – DBScan, KMeans + Practice
21. Cross-Validation and Grid Search CV + Practice
22. Stochastic Gradient Descent for Classification and Regression + Practice
23. Time Series Analysis + Practice
24. Bagging and Boosting Techniques + Practice
25. XGBoost, CATBoost, LighGBM + Practice
26. Kaggle Ex-Competition Practice

References: [SciKit Learn Documentation]

I will post the series of Intuitions and Jupyter notebooks in upcoming posts using the prefix “ML-Series” or you can also find them by looking into the category “Machine Learning Series”. Stay Tuned for more updates.

If you have any queries or suggestions, please feel free to drop a comment below.