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