Machine Learning – Advanced
(11 weeks of Learning)
After Completion of this Course, You’ll be Skilled to Apply For:
Job Description – Requirement for Market Mix Modelling/ Web Analytics/ Prediction Models using Structured/ Unstructured Data
Primary Domain – E-Commerce/ Banking, financial services and insurance (BFSI)/ Telecom/ Pharma/ ITeS
Course Content
After the completion of 14 Modules of Level 1 – “Python for Data Science”; engage in:
Module 15: Advanced ML Techniques Part 1
- Tree Based Models
- Ensemble Methods
- Bagging And Boosting
- Bias Variance Tradeoff
- Python Implementation
Module 16: Advanced ML Techniques Part 2
- Anomaly Detection
- Regularization
- Optimization
- Linear Discriminant Analysis
- Python Implementation & Analysis
Module 17: Clustering Techniques
- What is clustering?
- Partitioning Based Clustering Methods
- Hierarchical Clustering
- Agglomerative Clustering Algorithms
- Divisive Clustering Algorithms
- Elbow Method
Module 18: Practise Session
- Problem Statement and Discussion
Module 19: Feature Engineering, Eigen Vectors, Dimensionality Reduction Techniques
- What is Feature Engineering?
- Steps involved and analysis methods
- Why Dimension Reduction?
- Common Techniques Used
- Eigen Vectors and Pricipal Component Analysis
Module 20: Python Implementation
Python Implementation of following:
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- PCA
Factor Analysis
Module 21: Case Study 3
Module 22: Case Study 4
