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.
The first level involves learning mathematics.
- Linear Algebra
- Probability and Statistics
If you are wondering where to learn. Please follow this [Link]
The second level involves learning Data Science.
For additional resources , Please follow this [Link]
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.