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Machine Learning Fundamentals 1 What is Machine Learning? 2 Types of Machine Learning 3 What is Supervised learning? 4 What is Unsupervised learning? 5 Machine Learning vs Artificial intelligence 6 Machine Learning Tutorial 7 History of Machine Learning 8 Reinforcement Learning 9 Machine Learning vs Deep Learning 10 Applications of Machine Learning 11 Machine Learning Workflow 12 Advantages and Limitations of Machine Learning 13 Git and Version Control for ML Projects 14 SQL for Machine Learning Data Access 15 Course Introduction and How to Use This Course 16 Prerequisites and Skill Self-Assessment 17 Learning Outcomes and Career Roadmap 18 Environment Setup (Python, venv, IDE, GPU/Colab) 19 Chapter Quiz and Knowledge Check

Structured Data Classification 1 Structured Data Classification 2 Hands on 3 Introduction to Classification 4 Binary Classification 5 Multi-Class Classification 6 Logistic Regression 7 Decision Tree Classifier 8 Random Forest Classifier 9 K-Nearest Neighbors (KNN) 10 Support Vector Machine (SVM) 11 Naive Bayes Algorithm 12 Hands-on Classification Project 13 Gradient Boosting: XGBoost, LightGBM, CatBoost 14 Hyperparameter Tuning (Grid, Random, Bayesian) 15 Chapter Quiz and Knowledge Check 16 - Structured Data Classification

Unstructured Data Classification 1 Unstructured Data Classification 2 Introduction to Unstructured Data 3 Text Classification 4 Image Classification 5 Audio Classification 6 Natural Language Processing Basics 7 Tokenization 8 Stop Words Removal 9 Stemming and Lemmatization 10 TF-IDF Vectorization 11 Sentiment Analysis 12 Hands-on NLP Project 13 Chapter Quiz and Knowledge Check - Unstructured Data Classification

Python for Machine Learning 1 Introduction to Python 2 Variables and Data Types 3 Operators and Expressions 4 Loops and Conditional Statements 5 Functions in Python 6 NumPy Basics 7 Pandas Basics 8 Matplotlib for Visualization 9 Seaborn for Data Visualization 10 Jupyter Notebook Introduction

Mathematics for Machine Learning 1 Linear Algebra Basics 2 Scalars, Vectors, and Matrices 3 Matrix Operations 4 Probability Basics 5 Statistics Fundamentals 6 Mean, Median, Mode 7 Variance and Standard Deviation 8 Correlation and Covariance 9 Calculus for Machine Learning 10 Gradient Descent

Data Preprocessing 1 What is Data Preprocessing? 2 Handling Missing Data 3 Feature Scaling 4 Normalization vs Standardization 5 Encoding Categorical Data 6 Train-Test Split 7 Feature Engineering 8 Handling Outliers 9 Data Cleaning Techniques 10 Dimensionality Reduction 11 Handling Imbalanced Data (SMOTE, Class Weighting) 12 Data Pipelines and Orchestration (Airflow) 13 Big Data Processing with Spark 14 Chapter Quiz and Knowledge Check 15 - Data Preprocessing

Structured Data Regression 1 Introduction to Regression 2 Linear Regression 3 Multiple Linear Regression 4 Polynomial Regression 5 Ridge Regression 6 Lasso Regression 7 Regression Evaluation Metrics 8 Hands-on Regression Project 9 Chapter Quiz and Knowledge Check 10 - Structured Data Regression

Clustering Algorithms 1 What is Clustering? 2 K-Means Clustering 3 Hierarchical Clustering 4 DBSCAN Clustering 5 Elbow Method 6 Silhouette Score 7 Customer Segmentation Project

Association Rule Learning 1 Introduction to Association Rules 2 Apriori Algorithm 3 Market Basket Analysis 4 FP-Growth Algorithm

Model Evaluation and Validation 1 Accuracy 2 Precision 3 Recall 4 F1 Score 5 Confusion Matrix 6 ROC Curve 7 AUC Score 8 Cross Validation 9 Bias vs Variance 10 Overfitting and Underfitting 11 A/B Testing and Online Experimentation 12 Model Interpretability (SHAP, LIME) 13 Chapter Quiz and Knowledge Check 14 - Model Evaluation and Validation

Deep Learning Fundamentals 1 Introduction to Deep Learning 2 Artificial Neural Networks 3 Activation Functions 4 Forward Propagation 5 Backpropagation 6 TensorFlow Introduction 7 Keras Introduction 8 Building First Neural Network 9 PyTorch Fundamentals 10 Tensors, Autograd and Training Loops 11 Transfer Learning and Pretrained Models 12 Chapter Quiz and Knowledge Check 13 - Deep Learning Fundamentals

Computer Vision 1 Introduction to Computer Vision 2 Image Processing Basics 3 Convolutional Neural Networks (CNN) 4 Image Recognition 5 Object Detection 6 Face Recognition Project

Natural Language Processing (Advanced) 1 NLP Overview 2 Bag of Words 3 Word Embeddings 4 RNN Basics 5 LSTM Networks 6 Transformer Models 7 Chatbot Development 8 Attention Mechanism and Transformer Internals 9 Encoder, Decoder and Encoder-Decoder Architectures 10 Hugging Face Transformers Library 11 Chapter Quiz and Knowledge Check 12 - Natural Language Processing (Advanced)

Time Series Analysis 1 Introduction to Time Series 2 Time Series Components 3 ARIMA Model 4 Forecasting Techniques 5 Stock Price Prediction Project

Recommendation Systems 1 Introduction to Recommendation Systems 2 Content-Based Filtering 3 Collaborative Filtering 4 Movie Recommendation System Project

Reinforcement Learning 1 Introduction to Reinforcement Learning 2 Agent and Environment 3 Q-Learning 4 Reward Function 5 Real-World Applications

Machine Learning Projects 1 House Price Prediction 2 Spam Email Detection 3 Fake News Detection 4 Image Classification Project 5 Customer Churn Prediction 6 Sales Forecasting 7 Chatbot Project 8 End-to-End Capstone Project (Brief to Deployment) 9 Portfolio Build and GitHub Presentation 10 Project Peer Review and Feedback

Model Deployment 1 Introduction to Model Deployment 2 Saving and Loading Models 3 Flask for ML Deployment 4 FastAPI for ML APIs 5 Deploying ML Model on Cloud 6 Docker Basics for ML 7 Kubernetes Orchestration 8 Cloud ML Platforms (AWS SageMaker, Bedrock)

MLOps Fundamentals 1 What is MLOps? 2 ML Pipeline 3 Model Monitoring 4 CI/CD for ML 5 Version Control for Models 6 Experiment Tracking (MLflow, Weights & Biases) 7 Data Drift and Concept Drift Detection 8 Feature Stores 9 Model Observability and Logging 10 Chapter Quiz and Knowledge Check 11 - MLOps Fundamentals

Generative AI and Large Language Models 1 Introduction to Generative AI 2 What are Large Language Models? 3 Prompt Engineering 4 Fine-Tuning Basics 5 Retrieval-Augmented Generation (RAG) 6 AI Agents Introduction 7 Parameter-Efficient Fine-Tuning (LoRA, QLoRA) 8 Vector Databases (FAISS, OpenSearch, Pinecone) 9 Embeddings and Semantic Search 10 LLM Evaluation (LLM-as-Judge, Hallucination Metrics) 11 Multimodal Models (Vision-Language) 12 LangChain and LangGraph Orchestration 13 Model Context Protocol (MCP) and Tool Calling 14 Chapter Quiz and Knowledge Check 15 - Generative AI and Large Language Models

Ethics and Responsible AI 1 AI Ethics 2 Bias in Machine Learning 3 Explainable AI 4 Data Privacy 5 Responsible AI Practices 6 Prompt Injection and OWASP LLM Risks 7 Input and Output Guardrails 8 AI Red Teaming

Career Services and Job Readiness 1 ML Interview Preparation (Theory and Coding) 2 Resume and LinkedIn Optimisation for ML Roles 3 Building a Public ML Portfolio 4 Take-Home Assignment Walkthroughs 5 Freelancing and Consulting in ML 6 Mock Interviews and Feedback

Course Completion and Certification 1 Final Assessment and Graded Exam 2 Capstone Submission and Evaluation Rubric 3 Certificate of Completion 4 Continued Learning Paths and Resources 5 Community Access and Alumni Network

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Parameter-Efficient Fine-Tuning (LoRA, QLoRA)

Rumman Ansari September 12, 2026 2 views Subject Details

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