Python with Machine Learning
Module 1: Statistical Learning
- Statistical analysis concepts
- Descriptive statistics
- Introduction to probability and Bayes theorem
- Probability distributions
- Hypothesis testing & scores
Module 2: Python for Machine Learning
- Python Overview
- Pandas for pre-Processing and Exploratory Data Analysis
- Numpy for Statistical Analysis
- Matplotlib & Seaborn for Data Visualization
- Scikit Learn
Module 3: Introduction to Machine Learning
- Machine Learning Modelling Flow
- How to treat Data in ML
- Types of Machine Learning
- Performance Measures
- Bias-Variance Trade-Off
- Overfitting & Underfitting
Module 4: Optimization
- Maxima and Minima
- Cost Function
- Learning Rate
- Optimization Techniques
Module 5: Supervised Learning
- Linear Regression
- Case Study
- Logistic Regression
- Case Study
- KNN Classification
- Case Study
- Naive Bayesian classifiers
- Case Study
- SVM – Support Vector Machines
- Case Study
Module 6: Unsupervised Learning
- Clustering approaches
- K Means clustering
- Hierarchical clustering
- Case Study
Module 7: Ensemble Techquies
- Decision Trees
- Case Study
- Introduction to Ensemble Learning
- Different Ensemble Learning Techniques
- Bagging
- Boosting
- Random Forests
- Case Study
- PCA (Principal Component Analysis) and Its Applications
- Case Study
Module 8: Recommendation Systems
- Introduction to Recommendation Systems
- Types of Recommendation Techniques
- Collaborative Filtering
- Content based Filtering
- Hybrid RS
- Performance measurement
- Case Study