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All Projects → kabirnagpal → ML-Track

kabirnagpal / ML-Track

Licence: other
This repository is a recommended track, designed to get started with Machine Learning.

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Jupyter Notebook
11667 projects

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visualizationmachine-learningclusteringnumpyscikit-learnregressionpandasclassificationbias-variancedimesion-reduction

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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
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