Artificial Intelligence Tutorial

Welcome to the most comprehensive Artificial Intelligence learning hub. Whether you are a beginner, a developer, or an aspiring AI Engineer, this course will take you from the basics of AI to advanced concepts like Generative AI, MLOps, and Multi-Agent Systems.

Course Overview

Who is this for?

✓ Beginners looking to understand AI
✓ Software Engineers transitioning to ML/AI
✓ Students preparing for placements
✓ Data Scientists looking to master Generative AI

What you will learn?

✓ Core AI and ML Fundamentals
✓ Deep Learning & Neural Networks
✓ Natural Language Processing (NLP)
✓ Generative AI, LLMs & RAG

Master Syllabus

Module 1: Introduction to AI

What is Artificial Intelligence? A Complete Guide History of Artificial Intelligence: From Turing to Transformers Types of Artificial Intelligence: Complete Classification What is Narrow AI? (Artificial Narrow Intelligence)What is General AI? (Artificial General Intelligence)What is Super AI? (Artificial Superintelligence)AI vs ML vs DL: What is the Difference?Applications of Artificial Intelligence Across Industries Advantages of Artificial Intelligence Limitations of Artificial Intelligence The Future of AI: Trends and Predictions Careers in Artificial Intelligence: Roles, Skills, and Salaries

Module 2: Python for AI

Python Basics for Artificial Intelligence Python Variables: Storing Data for AI Models Python Data Types for Machine Learning Python Operators: Math and Logic for AI Python Loops for AI: For and While Loops Python Functions: Organizing Code for AI Python Lists: Handling AI Datasets Python Tuples: Immutable Data for AI Python Dictionaries: Key-Value Data for AI Python Sets: Unique Data Collections Object-Oriented Programming (OOP) in Python for AI Exception Handling in Python: Try, Except, Finally File Handling in Python: Reading and Writing Data Introduction to NumPy for Artificial Intelligence Pandas for Data Science: DataFrames and Series Matplotlib: Data Visualization for AI Seaborn: Statistical Data Visualization

Module 3: Mathematics for AI

Linear Algebra Basics for Artificial Intelligence Matrices and Vectors: Core Operations for AI Calculus Basics for Artificial Intelligence Derivatives in Machine Learning: Finding the Slope Partial Derivatives and Gradients in AI Probability Basics for Artificial Intelligence Bayes Theorem in Machine Learning Statistics Basics for Artificial Intelligence Mean, Median, and Mode in Data Science Variance and Standard Deviation in Machine Learning

Module 4: Machine Learning Fundamentals

Supervised Learning: Training AI with Labeled Data Unsupervised Learning: Finding Hidden Patterns Semi-Supervised Learning in AI Reinforcement Learning: AI Through Trial and Error Overfitting and Underfitting in Machine Learning The Bias-Variance Tradeoff in Machine Learning AI Evaluation Metrics: Accuracy, Precision, and Recall The Confusion Matrix in Machine Learning Cross-Validation in Machine Learning: K-Fold

Module 5: Supervised Learning Algorithms

Linear Regression in Machine Learning Logistic Regression: Binary Classification in AI Decision Trees in Machine Learning Random Forests: Ensemble Learning in AI Support Vector Machines (SVM) in AI K-Nearest Neighbors (KNN) in Machine Learning Naive Bayes Classifier in Machine Learning

Module 6: Unsupervised Learning Algorithms

K-Means Clustering: Algorithm, Math, and Implementation Hierarchical Clustering: Agglomerative, Divisive, and Dendrograms DBSCAN: Density-Based Spatial Clustering of Applications with Noise Principal Component Analysis (PCA): Dimensionality Reduction Anomaly Detection: Algorithms, Techniques, and Use Cases

Module 7: Deep Learning Fundamentals

What is Deep Learning? Neurons, Layers, and Architectures Activation Functions: Sigmoid, Tanh, ReLU, and Softmax Feedforward and Backpropagation: How Neural Networks Learn Optimization Algorithms: SGD, Momentum, RMSprop, and Adam Introduction to PyTorch: Tensors, Autograd, and Custom Models

Module 8: Natural Language Processing

Introduction to NLP: Tokenization, Stemming, and Lemmatization Text Vectorization: Bag of Words, TF-IDF, and N-grams Word Embeddings: Word2Vec, GloVe, and Semantic Space Recurrent Neural Networks (RNNs) for Sequence Modeling LSTMs and GRUs: Gated Neural Networks for Long-Term Memory

Module 9: Computer Vision

Introduction to Computer Vision: Pixels, Channels, and Color Spaces Image Filters and Convolution: Kernels, Blurs, and Edges Convolutional Neural Networks (CNNs): Architecture and Mechanics Pooling Layers and Classic CNN Architectures Transfer Learning: Fine-Tuning Pre-Trained Vision Models

Module 10: Generative AI & LLMs

Generative AI and the Transformer Architecture Large Language Models (LLMs): Pre-Training, Fine-Tuning, and RLHF Prompt Engineering: Techniques, Formats, and Best Practices Retrieval-Augmented Generation (RAG): Architecture and Vector DBs Building LLM Applications with LangChain and LlamaIndex

Module 11: MLOps & Production

Introduction to MLOps: Lifecycle, Pipeline, and Tools Model Serialization: Pickle, Joblib, and ONNX formats Deploying Models as APIs using FastAPI and Pydantic Docker for Machine Learning: Containerizing ML Apps Model Monitoring: Concept Drift and Data Drift

Module 12: Capstone Projects

Capstone Project 1: Tabular House Price Predictor Capstone Project 2: Real-Time NLP Sentiment Analyzer Capstone Project 3: Image Classifier using Transfer Learning AI and Machine Learning Interview Prep: Top 30 Q&A AI Careers: Roles, Portfolios, and Transition Roadmap