Machine Learning

Module 1: Introduction to Machine Learning

• What is Machine Learning? Overview and applications
• Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
• Understanding AI, ML, and Deep Learning differences
• Setting up the ML environment (Python, Jupyter, Anaconda)

Module 2: Data Preprocessing & Feature Engineering

• Collecting and cleaning data
• Handling missing values, outliers, and categorical data
• Feature scaling and transformation techniques
• Feature selection and dimensionality reduction (PCA, LDA)

Module 3: Supervised Learning – Regression & Classification

• Regression models: Linear Regression, Polynomial Regression, Ridge & Lasso Regression
• Classification models: Logistic Regression, Decision Trees, Random Forest, SVM, KNN
• Model evaluation techniques (Confusion Matrix, Precision-Recall, ROC-AUC)
• Hyperparameter tuning and optimization

Module 4: Unsupervised Learning – Clustering & Association

• K-Means Clustering & Hierarchical Clustering
• DBSCAN and Gaussian Mixture Models
• Principal Component Analysis (PCA) for dimensionality reduction
• Market Basket Analysis & Association Rule Learning (Apriori, FP-Growth)

Module 5: Neural Networks & Deep Learning

• Introduction to Artificial Neural Networks (ANN)
• Building Deep Neural Networks (DNN) using TensorFlow and Keras
• Convolutional Neural Networks (CNN) for image processing
• Recurrent Neural Networks (RNN) for time-series and NLP

Module 6: Reinforcement Learning & Advanced ML Techniques

• Introduction to Reinforcement Learning (Markov Decision Processes)
• Q-Learning and Deep Q-Networks (DQN)
• Generative Adversarial Networks (GANs)
• Transfer Learning & AutoML

Module 7: Natural Language Processing (NLP)

• Text preprocessing and tokenization
• Sentiment analysis and text classification
• Named Entity Recognition (NER)
• Transformers & Large Language Models (BERT, GPT)

Module 8: Model Deployment & Real-World Applications

• Deploying models using Flask & FastAPI
• Using TensorFlow Serving & Docker for deployment
• ML model deployment on cloud (AWS, GCP, Azure)
• Real-world case studies (finance, healthcare, e-commerce)

Module 9: Capstone Project & Certification Preparation

• End-to-end ML project with real-world data
• Model evaluation, fine-tuning, and performance optimization
• Preparing for industry certifications (TensorFlow Developer, AWS ML, Microsoft AI)
• Resume building & job interview preparation

Why Enroll in This Course?

✅ Hands-on learning with Python, TensorFlow, and Scikit-learn
✅ Real-world datasets and industry-focused projects
✅ Learn from experts and gain global certification readiness
✅ Deploy AI models for practical business solutions

🚀 Start mastering Machine Learning today and build a future in AI!