This repository is a recommended track, designed to get started with Machine Learning.
Last updated Nov 21, 2022
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SoA-ML-14 ==========
Week 1: Intro to Numpy and Pandas
(Anaconda, spyder, jupyter)
Getting Familiar with:
Link to Week 1's Jupyter Notebook- Numpy
- Pandas
- Matplotlib
Week 2: Basic Data pre-processing:
Link to Week 2's Jupyter Notebook- One Hot encoding
- Label Encoding
- Normalization
- Dealing with Missing values
- Introduction to Machine learning
- Types of Learning (Supervised, Unsupervised and Reinforcement)
- Application of Machine Learning
Week 3: Regression Algorithms:
Link to Week 3's Jupyter Notebook- Linear Regression
- Multiple Linear Regression
- Polynomial Regression
Week 4:Classification Algorithms:
Link to Week 4's Jupyter Notebook- Logistic Regression
- K-Nearest Neighbours
- Support Vector Classifier
- Decision Tree
- Random Forest
- Voting Classifier
Week 5: Bias vs Variance Trade off
Link to Week 5's Jupyter Notebook- OverFitting
- UnderFitting
- Regularization
- Support Vector Machines
Week 6:Clustering Algorithms:
Link to Week 6's Jupyter Notebook- K-means Clustering
- Hierarchical Clustering
Week 7: Dimensionality Reduction:
Link to Week 7's Jupyter Notebook- PCA
- LDA
- Kernel PCA
- Model Selection:
- K-fold Cross Validation
- Parameter Tuning
- Grid Search
Week 8: An introduction to Boosting
Link to Week 8's Jupyter Notebook- Gradient Boosting
- XGBoost
