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?”, “what is the best machine learning curriculum for Beginners? “or “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 the 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.

### Machine Learning Curriculum for Beginners

- 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:**

- Linear Algebra
- Calculus
- Probability and Statistics

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

## Level 2:

The second level involves learning Data Science.

For additional resources, please follow this [Link]

## Level 3

Machine Learning Curriculum

- Importing the Dataset + Practice
- Exploratory Data Analysis + Practice
- Data Preprocessing + Practice
- Handling Missing Data + Practice
- Feature Scaling and Selection + Practice
- Scikit Learn Library
- Bias Variance Tradeoff
- Introduction to Supervised Learning + Practice
- Linear Regression with one variable + Practice
- Linear Regression with multiple variables and Regularization + Practice
- SVMs + Practice
- Logistic Regression + Practice
- Naive Bayes + Practice
- Decision Tree + Practice
- Introduction to Ensembles + Practice
- Random Forests + Practice
- K-Nearest Neighbour + Practice
- PCA + Practice
- Introduction Unsupervised Learning
- Clustering – DBScan, KMeans + Practice
- Cross-Validation and Grid Search CV + Practice
- Stochastic Gradient Descent for Classification and Regression + Practice
- Time Series Analysis + Practice
- Bagging and Boosting Techniques + Practice
- XGBoost, CATBoost, LighGBM + Practice
- 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.

How to handle missing values in Categorical data