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Advanced Analytics & ML on Python - 2 (ML002)


Description

This is the second course in the series of Advance Analytics & ML on Python. On a high level, we will cover the following techniques in this course -

1. Segmentation Techniques

2. Dimensionality reduction

3. Model Ensembles

4. Support Vector Machines (Optional)

Content
  • Clustering and Dimensionality Reduction Techniques
  • Hierarchial Clustering
  • K-means clustering
  • Clustering with DBSCAN, Clearly Explained
  • Principal Component Analysis (PCA), Step-by-Step
  • PCA main ideas in only 5 minutes
  • PCA - Practical Tips
  • PCA in R
  • PCA in Python
  • Linear Discriminant Analysis (LDA) clearly explained.
  • MDS (Multi-Dimensional Scaling) and PCoA (Principal Coordinate Analysis)
  • t-SNE, Clearly Explained
  • Clustering and Dimensionality Reduction Techniques
  • MCQ Assignment 1
  • Model Ensembles
  • AdaBoost, Clearly Explained
  • Gradient Boost Part 1 (of 4): Regression Main Ideas
  • Gradient Boost Part 2 (of 4): Regression Details
  • Gradient Boost Part 3 (of 4): Classification
  • Gradient Boost Part 4 (of 4): Classification Details
  • XGBoost Part 1 (of 4): Regression
  • XGBoost Part 2 (of 4): Classification
  • XGBoost Part 3 (of 4): Mathematical Details
  • XGBoost Part 4 (of 4): Crazy Cool Optimizations
  • XGBoost in Python from Start to Finish
  • Learning Material and Working Examples - Jupyter Notebook
  • MCQ Assignment 2
  • Coding Assignment - GBM
  • Support Vector Machines (OPTIONAL)
  • Support Vector Machines Part 1 (of 3): Main Ideas
  • Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)
  • Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)
  • Support Vector Machines in Python from Start to Finish
  • Learning Material and Working Examples - Jupyter Notebook
  • Test
  • MCQ Test 1
  • MCQ Test 2
  • Test - 1
  • ML-2 Training Feedback
  • ADDITIONAL REFERENCE LINKS
  • Machine Learning
  • 2019 Mathematics of Machine Learning Summer School
  • Numerical Computing with Python
  • MIT 9.520 Statistical Learning Theory and Applications
  • Mathematics - Optimization
  • Constrained and unconstrained optimization
Completion rules
  • All units must be completed
  • Leads to a certificate with a duration: Forever
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