robot Artificial Intelligence
English
Free Course
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Artificial Intelligence Mastery: Beginner to AI Engineer
A complete hands-on AI curriculum covering Python programming, mathematics, statistics, machine learning, deep learning, large language models, retrieval augmented generation, AI agents, computer vision, NLP, and real-world AI projects.
154 Chapters
154 Pages
154 Hours
Course Overview
This program turns beginners into AI engineers through progressive theory, coding labs, quizzes, assignments, and project-based learning.
Curriculum
What is Artificial Intelligence and Why It Matters
45 min
AI Career Paths and Engineer Roadmap
40 min
What is Artificial Intelligence?
90 min
History of AI
90 min
AI vs Machine Learning vs Deep Learning
90 min
Real-world AI Applications
90 min
AI Career Paths
90 min
AI Engineer Roadmap
90 min
Python Fundamentals for AI
90 min
Python for Data: NumPy, Pandas, Matplotlib
80 min
Datasets, Preprocessing, and AI Scripts
75 min
Python Variables and Data Types
90 min
Operators, Conditions, and Loops
90 min
Functions and Reusable Code
90 min
Classes and Object-Oriented Programming
90 min
NumPy Fundamentals
90 min
Pandas DataFrames and Data Cleaning
90 min
Matplotlib Data Visualization
90 min
Working with Datasets and CSV Files
90 min
Data Preprocessing Pipelines
90 min
Writing AI Automation Scripts
90 min
Linear Algebra for AI: Vectors and Matrices
60 min
Calculus and Probability for Optimization
65 min
Vectors and Vector Operations
90 min
Matrices and Matrix Operations
90 min
Dot Product Intuition
90 min
Eigenvalues and Eigenvectors
90 min
Functions and Derivatives
90 min
Gradients and Optimization
90 min
Probability Basics
90 min
Distributions for AI
90 min
Bayesian Thinking for Decisions
90 min
Descriptive Statistics and Correlation
55 min
Sampling, Hypothesis Testing, and Data Analysis
60 min
Mean, Median, and Mode
90 min
Variance and Standard Deviation
90 min
Correlation and Relationships
90 min
Probability Distributions in Practice
90 min
Sampling and Bias
90 min
Hypothesis Testing Fundamentals
90 min
Exploratory Data Analysis with Python
90 min
Machine Learning Foundations
70 min
Regression and Classification with Scikit-learn
120 min
Clustering, Feature Engineering, and Evaluation
110 min
What is Machine Learning?
90 min
Supervised Learning
90 min
Unsupervised Learning
90 min
Reinforcement Learning
90 min
Linear Regression
90 min
Polynomial Regression
90 min
Logistic Regression
90 min
Decision Trees
90 min
Random Forests
90 min
SVM and KNN
90 min
K-means and DBSCAN
90 min
Feature Engineering Workflow
90 min
Model Evaluation: Accuracy, Precision, Recall, F1
90 min
Project Lab: House Price Prediction
240 min
Project Lab: Spam Classifier
240 min
Project Lab: Customer Segmentation
240 min
Neural Network Fundamentals
90 min
TensorFlow and PyTorch Training Workflows
100 min
CNN, RNN, LSTM, and Transformers
120 min
Artificial Neurons and Layers
90 min
Activation Functions
90 min
Forward Propagation
90 min
Backpropagation
90 min
Loss Functions and Optimizers
90 min
TensorFlow Setup and Model Training
90 min
TensorFlow Model Saving and Loading
90 min
PyTorch Tensors and Modules
90 min
PyTorch Training Loops
90 min
CNN for Image Tasks
90 min
RNN and LSTM for Sequence Tasks
90 min
Transformers for Deep Learning
90 min
Project Lab: Image Classifier
240 min
Project Lab: Text Classifier
240 min
Project Lab: Digit Recognition
240 min
LLM Architecture, Tokens, Embeddings, and Attention
85 min
Prompt Engineering, Fine-Tuning, and LLM Evaluation
100 min
What are LLMs?
90 min
Transformer Architecture for LLMs
90 min
Tokens and Tokenization
90 min
Embeddings in LLM Systems
90 min
Attention Mechanism
90 min
Prompt Engineering Patterns
90 min
Fine-Tuning Strategies
90 min
LLM Evaluation and Benchmarks
90 min
Build with OpenAI API
90 min
Build with Hugging Face Open-Source Models
90 min
Project Lab: AI Chatbot
240 min
Project Lab: Document Assistant
240 min
Project Lab: AI Coding Assistant
240 min
RAG Foundations: Embeddings, Chunking, and Retrieval
95 min
RAG with LangChain, LlamaIndex, ChromaDB, and Pinecone
120 min
What is RAG and Why It Is Needed
90 min
Embeddings for Retrieval
90 min
Vector Databases Fundamentals
90 min
Document Processing Pipelines
90 min
Chunking Strategies
90 min
Retrieval Methods and Reranking
90 min
Context Injection for Generation
90 min
RAG Evaluation Metrics
90 min
LangChain and LlamaIndex Workflows
90 min
FAISS, ChromaDB, and Pinecone Integration
90 min
Project Lab: Personal Knowledge AI Assistant
240 min
Agent Architecture, Planning, and Tool Use
85 min
Multi-Agent Systems with AutoGen and CrewAI
100 min
What Are AI Agents?
90 min
Agent Architecture Basics
90 min
Planning and Reasoning Loops
90 min
Tool Usage and Function Calling
90 min
Memory Systems for Agents
90 min
Multi-Agent System Design
90 min
LangChain Agents
90 min
AutoGen and CrewAI
90 min
Project Lab: Research Assistant Agent
240 min
Project Lab: Coding Agent
240 min
Project Lab: Task Automation Agent
240 min
MCP Architecture and Tool Integration
90 min
What is MCP?
90 min
MCP Architecture
90 min
MCP Servers and Clients
90 min
Connecting Models to External Tools
90 min
Building MCP Tools
90 min
Project Lab: External-Service AI Assistant
240 min
Image Processing, Classification, and OpenCV
95 min
Object Detection, Segmentation, and OCR
105 min
Image Processing Fundamentals
90 min
OpenCV Essentials
90 min
Image Classification
90 min
Object Detection and YOLO
90 min
Face Recognition Concepts
90 min
Image Segmentation Workflows
90 min
Project Lab: OCR System
240 min
Project Lab: Object Detection App
240 min
Project Lab: Smart Camera
240 min
Text Preprocessing, Tokenization, and Classification
90 min
NER, Transformers, and BERT Applications
100 min
Text Preprocessing Pipeline
90 min
Tokenization Methods
90 min
Sentiment Analysis
90 min
Text Classification
90 min
Named Entity Recognition
90 min
Transformers for NLP
90 min
BERT in Practice
90 min
Project Lab: Sentiment Analyzer
240 min
Project Lab: Document Classifier
240 min
Project Lab: AI Summarizer
240 min
Project 1: AI Chatbot
360 min
Project 2: Recommendation System
420 min
Project 3: OCR Application
360 min
Project 4: AI Assistant with LLM + RAG + Agents
480 min
Project 4: AI Assistant
240 min
What You'll Learn
- Understand transformers
- Build RAG systems
- Evaluate model outputs
Prerequisites
- Python basics
- Statistics basics
- Laptop with internet