AI Master Class: From Beginner to Professional Practitioner
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AI & Machine Learning
advanced
PRO

AI Master Class: From Beginner to Professional Practitioner

4.9
15,001 students
19h 0m
76 lessons

About this course

This comprehensive master class takes you from zero AI knowledge to building production-ready intelligent systems. Starting with mathematical foundations and Python essentials, you will progress through classical machine learning, deep learning architectures, and cutting-edge generative AI. The curriculum balances theory with hands-on practice: every concept is reinforced through interactive coding labs, real-world case studies, and guided projects. You will master the full ML lifecycle — from data preparation and model training to deployment, monitoring, and governance. By the end, you will have a portfolio of end-to-end projects spanning computer vision, NLP, reinforcement learning, and LLM applications, plus the MLOps skills to ship them reliably. Designed for aspiring ML engineers, data scientists, and software developers ready to specialize in AI.

Course Content

Foundations of AI & Machine Learning

  • What is AI? History, Types, and Modern Landscape15min
  • Optimization Fundamentals: Gradient Descent, Convexity, Learning Rates15min
  • Linear Algebra Essentials: Vectors, Matrices, Eigenvalues15min
  • Probability & Statistics for ML: Distributions, Bayes, Hypothesis Testing15min
  • Setting Up Your AI Development Environment (Conda, Jupyter, GPU)15min
  • Exploratory Data Analysis with Pandas & Visualization15min

Python for AI & Data Manipulation

  • NumPy Deep Dive: Broadcasting, Vectorization, Performance15min
  • Advanced Pandas: Time Series, MultiIndex, Large Data Handling15min
  • Data Cleaning & Feature Engineering Techniques15min
  • Working with Structured, Unstructured, and Streaming Data15min
  • SQL for Data Scientists: Joins, Window Functions, CTEs15min
  • Version Control with Git & DVC for Data & Models15min

Supervised Learning: Regression & Classification

  • Linear & Logistic Regression: Theory, Assumptions, Regularization15min
  • Tree-Based Models: Decision Trees, Random Forests, Gradient Boosting15min
  • Support Vector Machines & Kernel Methods15min
  • Model Evaluation: Cross-Validation, Metrics, Bias-Variance Tradeoff15min
  • Handling Imbalanced Data: Resampling, Cost-Sensitive Learning15min
  • End-to-End Tabular Project: Customer Churn Prediction15min

Unsupervised Learning & Dimensionality Reduction

  • Clustering Algorithms: K-Means, DBSCAN, Hierarchical, Gaussian Mixtures15min
  • Principal Component Analysis & Factor Analysis15min
  • Manifold Learning: t-SNE, UMAP for Visualization15min
  • Anomaly Detection: Isolation Forest, Autoencoders, One-Class SVM15min
  • Recommender Systems: Collaborative Filtering, Matrix Factorization15min
  • Project: Customer Segmentation & Market Basket Analysis15min

Deep Learning Fundamentals & Neural Networks

  • Perceptrons, Activation Functions, Universal Approximation Theorem15min
  • Backpropagation & Automatic Differentiation Deep Dive15min
  • Building & Training MLPs with PyTorch: Tensors, Autograd, Modules15min
  • Regularization: Dropout, BatchNorm, Weight Decay, Early Stopping15min
  • Optimizers Compared: SGD, Adam, RMSprop, Learning Rate Schedules15min
  • Hyperparameter Tuning: Grid, Random, Bayesian (Optuna)15min

Computer Vision with Convolutional Networks

  • Convolution Operations, Pooling, Receptive Fields15min
  • Classic Architectures: LeNet, AlexNet, VGG, ResNet, EfficientNet15min
  • Transfer Learning & Fine-Tuning Strategies15min
  • Object Detection: R-CNN, YOLO, SSD15min
  • Semantic Segmentation: U-Net, DeepLab, Mask R-CNN15min
  • Data Augmentation, Test-Time Augmentation, and CV Best Practices15min
  • Project: Medical Image Classification with Grad-CAM Explainability15min

Natural Language Processing & Transformers

  • Project: Sentiment Analysis & Text Classification Pipeline15min
  • Text Preprocessing: Tokenization, Subword (BPE, WordPiece), Embeddings15min
  • RNNs, LSTMs, GRUs, and Attention Mechanisms15min
  • Transformer Architecture: Self-Attention, Multi-Head, Positional Encoding15min
  • BERT & Encoder Models: Pre-training, Fine-tuning for Classification/NER15min
  • GPT & Decoder Models: Autoregressive Generation, Prompting15min
  • Hugging Face Ecosystem: Tokenizers, Trainer, Hub, Accelerate15min

Reinforcement Learning & Decision Making

  • MDPs, Bellman Equations, Value vs Policy Iteration15min
  • Q-Learning, SARSA, Deep Q-Networks (DQN)15min
  • Policy Gradient Methods: REINFORCE, Actor-Critic, A2C, A3C15min
  • Proximal Policy Optimization (PPO) & Trust Region Methods15min
  • Offline RL, Imitation Learning, and RLHF Fundamentals15min
  • Project: Training an Agent with Gymnasium & Stable-Baselines315min

MLOps: Deployment, Monitoring & Scaling

  • ML Lifecycle & MLOps Maturity Model15min
  • Containerizing ML Models with Docker & Multi-Stage Builds15min
  • Model Serving: FastAPI, Triton, TorchServe, Batching, GPU Sharing15min
  • Experiment Tracking & Model Registry with MLflow15min
  • CI/CD for ML: GitHub Actions, Testing, Automated Retraining15min
  • Monitoring: Data Drift, Concept Drift, Performance Degradation15min
  • Feature Stores & Data Versioning (Feast, DVC)15min

Generative AI: LLMs, Diffusion Models & Applications

  • Generative Modeling Landscape: VAEs, GANs, Flows, Diffusion15min
  • Diffusion Models: Forward/Reverse Process, U-Net, Classifier-Free Guidance15min
  • Stable Diffusion: Latent Diffusion, ControlNet, LoRA Fine-Tuning15min
  • LLM Architecture Deep Dive: Scaling Laws, Mixture of Experts, Quantization15min
  • Parameter-Efficient Fine-Tuning: LoRA, QLoRA, Adapters, Prompt Tuning15min
  • RAG Systems: Embeddings, Vector DBs, Retrieval, Reranking, Evaluation15min
  • Agentic Workflows: Tool Use, Planning, Multi-Agent (LangGraph, CrewAI)15min

AI Ethics, Safety & Responsible AI

  • Privacy-Preserving ML: Federated Learning, Differential Privacy15min
  • AI Governance: Regulations (EU AI Act), Model Cards, Audit Trails15min
  • Red Teaming LLMs: Jailbreaks, Prompt Injection, Harmful Outputs15min
  • Bias, Fairness Metrics, and Mitigation Strategies15min
  • Explainable AI: SHAP, LIME, Counterfactuals, Attention Visualization15min
  • Adversarial Robustness: Attacks (FGSM, PGD) & Defenses15min

Capstone Project: End-to-End AI Solution

  • Project Planning: Problem Definition, Success Metrics, Data Strategy15min
  • Data Pipeline: Ingestion, Validation, Labeling, Versioning15min
  • Model Development: Baseline, Iteration, Experiment Tracking15min
  • Model Evaluation: Slicing, Stress Testing, Human Evaluation15min
  • Deployment: Containerization, API, Canary Release, Monitoring Setup15min
  • Documentation & Portfolio Presentation: Model Card, Demo, Retrospective15min