Machine Learning with Python

Module 1: Python for Machine Learning

• NumPy arrays and vectorized operations
• Pandas data manipulation and cleaning
• Matplotlib/Seaborn visualization techniques

Module 2: ML Fundamentals and Workflow

• Supervised vs unsupervised learning paradigms
• Train-test-validation splits
• Overfitting prevention strategies

Module 3: Regression Algorithms

• Simple/multiple linear regression implementation
• Polynomial features and regularization
• Model diagnostics and residual analysis

Module 4: Classification Models

• Logistic regression and decision boundaries
• K-Nearest Neighbors algorithm
• Confusion matrix and classification metrics

Module 5: Tree-Based Methods

• Decision trees and pruning techniques
• Random Forest ensemble learning
• Feature importance evaluation

Module 6: Advanced Classification

• Support Vector Machines (SVM)
• Naive Bayes probabilistic classifiers
• Gradient Boosting Machines (XGBoost)

Module 7: Clustering and Unsupervised Learning

• K-Means algorithm and elbow method
• Hierarchical clustering dendrograms
• DBSCAN density-based clustering

Module 8: Dimensionality Reduction

• Principal Component Analysis (PCA)
• t-SNE for visualization
• Feature selection methods

Module 9: Model Evaluation and Validation

• Cross-validation techniques (K-Fold)
• ROC-AUC curves and precision-recall
• Learning curves analysis

Module 10: Hyperparameter Optimization

• Grid search and random search
• Bayesian optimization basics
• Pipeline creation with Scikit-learn

Module 11: Neural Networks Introduction

• Perceptrons and multi-layer networks
• Activation functions and backpropagation
• Keras Sequential model building

Module 12: Feature Engineering Mastery

• Categorical encoding strategies
• Scaling and normalization techniques
• Polynomial and interaction features

Module 13: Model Deployment Strategies

• Pickle/Joblib model serialization
• Flask API for model serving
• Docker containerization basics

Module 14: ML Project Lifecycle

• Business problem definition
• Data collection and exploration
• Model selection and documentation

Experience in the Industry Gain knowledge from experts with practical Machine Learning with Python project experience in a variety of sectors.

Backgrounds at the Top Prominent corporations such as HCL, TCS, Accenture, and Cognizant employ trainers.

Clear & Effective Teaching Excellent communication and real-world examples simplify complex subjects.

Hands-On Learning Focus Students can apply their abilities in real-world situations with the use of case studies and real-time projects.

Up-to-Date Knowledge Trainers stay current with the latest tools, techniques, and best practices.