Learn AI with Python
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53 Courses
Every course in the Learn AI with Python learning path.
Introduction to Programming
This lesson introduces the basics of programming and explains why Python is an ideal language for beginners. You'll also learn how to set u…
- What is Programming?
- Why Learn Python?
- Setting Up Your Environment
- +2 more
Python Basics
Learn the foundational concepts of Python programming, including variables, data types, and basic operations. This category sets the stage…
- Variables and Data Types
- Input and Output
- Basic Arithmetic and Operators
- +1 more
Conditionals and Iteration
Dive into decision-making and looping in Python. Learn how to write programs that can react to different conditions and repeat tasks effici…
- Understanding Conditionals
- Boolean Logic and Comparisons
- Looping
- +1 more
Functions and Modular Programming
Functions are the building blocks of reusable code. This category covers how to create and use functions effectively, and introduces modula…
- What is a Function?
- Return Values
- Scope and Lifetime of Variables
- +2 more
Data Structures
Explore Python's core data structures such as lists, tuples, sets, and dictionaries, and learn how to manipulate and organize data effectiv…
- Lists
- Tuples
- Sets
- +2 more
Object-Oriented Programming
Discover the principles of object-oriented programming, a powerful paradigm for building reusable and scalable applications.
- Classes and Objects
- Attributes and Methods
- Inheritance
- +3 more
File Handling
Learn how to read from and write to files in Python, enabling you to work with real-world data and persist information.
- Reading and Writing Files
- Working with CSV Files
- Handling Exceptions in File I/O
Python for Data Science Essentials
Set up a proper data science environment with virtual environments, Jupyter notebooks, and Python tooling.
- Virtual Environments and pip
- Jupyter Notebooks for Data Science
- Python Data Types for Data Science
- +1 more
Error and Exception Handling
Gain the skills to handle errors gracefully in Python programs, making your code more robust and user-friendly.
- Understanding Errors
- Using try, except, and finally
- Raising Exceptions
- +1 more
Advanced Python Features
Explore advanced Python concepts to write more efficient, compact, and Pythonic code.
- Decorators
- Generators
- Context Managers (with Statements)
- +2 more
Working with Libraries
Python’s extensive library ecosystem enables you to accomplish tasks ranging from data analysis to web development. This category introduce…
- Data Analysis with Pandas
- Data Visualization with Matplotlib
- NumPy for Numerical Computations
- +2 more
Testing and Debugging
Learn techniques for testing and debugging to ensure your Python code works correctly and efficiently.
- Unit Testing with unittest
- Debugging Techniques
- Using Debugging Tools
Python for Automation
Automate repetitive tasks and boost productivity by writing Python scripts for common tasks.
- Automating Tasks with Scripts
- Working with os and shutil
- Automating Emails and Reports
Statistical Foundations for AI
Build the statistical intuition behind AI algorithms — distributions, hypothesis testing, and probability.
- Descriptive Statistics and Distributions
- Probability and Bayes Theorem
- Hypothesis Testing
- +1 more
Data Cleaning and Preprocessing
Transform raw, messy datasets into clean, model-ready data using scikit-learn and pandas pipelines.
- Outlier Detection and Removal
- Encoding Categorical Variables
- Feature Scaling: Normalization and Standardization
- +1 more
Exploratory Data Analysis
Systematically explore any dataset to discover patterns, relationships, and anomalies before modeling.
- EDA Workflow and Data Profiling
- Univariate Analysis
- Bivariate and Multivariate Analysis
- +1 more
Regular Expressions for Text AI
Clean and extract text data with Python's re module — a critical preprocessing skill for NLP and AI.
- Regex Patterns and Character Classes
- re Module: search, match, findall, sub
- Capturing Groups and Named Groups
- +1 more
Databases for AI Projects
Store, query, and manage AI datasets using SQLite and PostgreSQL from Python.
- SQLite with Python's sqlite3 Module
- Pandas and SQL Integration
- Storing and Querying ML Results
- +1 more
AI Project Structure and Git Workflow
Organize professional AI projects with proper directory structure, Git versioning, and reproducibility practices.
- Professional AI Project Directory Structure
- Git for AI Projects
- Reproducibility: Seeds, Configs, and Environments
- +1 more
NumPy Deep Dive
Master NumPy arrays, broadcasting, and linear algebra operations for efficient numerical computation.
- Array Creation and Properties
- Indexing, Slicing, and Fancy Indexing
- Broadcasting and Vectorized Operations
- +1 more
Pandas Advanced Operations
Go beyond basics with groupby, pivot tables, multi-index, and advanced merging operations.
- GroupBy and Aggregation
- Pivot Tables and Cross-Tabulation
- Advanced Merging and Joining
- +1 more
Introduction to Artificial Intelligence
This category introduces the fundamental concepts of artificial intelligence, including its definition, importance, and historical mileston…
- What is Artificial Intelligence?
- Types of Artificial Intelligence
- History of Artificial Intelligence
- +2 more
Web APIs and Data Collection for AI
Collect real-world data from REST APIs, paginate results, and store structured datasets for AI projects.
- REST API Fundamentals for Data Collection
- Paginating and Collecting Large Datasets
- Storing Collected Data Efficiently
- +1 more
Model Evaluation and Hyperparameter Tuning
Rigorously evaluate ML models and find optimal hyperparameters using cross-validation and search strategies.
- Cross-Validation Strategies
- Classification Metrics Deep Dive
- Grid Search and Random Search
- +1 more
Applications of AI
Discover the diverse real-world applications of artificial intelligence in industries like healthcare, finance, e-commerce, gaming, and aut…
- AI in Healthcare
- AI in Finance
- AI in E-Commerce
- +2 more
Feature Engineering Techniques
Create powerful new features that dramatically improve model performance through domain knowledge and automation.
- Feature Selection Methods
- Creating Interaction and Polynomial Features
- Target Encoding and Advanced Categorical Handling
- +1 more
Support Vector Machines
Master SVMs for classification and regression with kernel tricks for non-linear decision boundaries.
- SVM Theory: Margins and Support Vectors
- Kernel Trick: RBF, Polynomial, and Sigmoid
- SVMs for Classification with sklearn
- +1 more
Time Series Analysis and Forecasting
Analyze temporal patterns and build forecasting models from ARIMA to LSTM for time series prediction.
- Time Series Components and Stationarity
- ARIMA and SARIMA Models
- Prophet for Automated Forecasting
- +1 more
Preparing for AI with Python
Get ready to build AI solutions by mastering essential Python tools and libraries. This category covers setting up your development environ…
- Python Libraries for AI
- Python Data Types and Structures
- File Operations in Python
- +2 more
Recommendation Systems
Build personalized recommendation engines using collaborative filtering, content-based methods, and matrix factorization.
- Collaborative Filtering: User-Based and Item-Based
- Matrix Factorization with SVD
- Content-Based Filtering
- +1 more
Data Manipulation
Learn how to prepare and manipulate data effectively for AI projects. This category introduces different types of data, techniques to handl…
- Types of Data
- Handling Missing Data
- Data Normalization
- +2 more
Serving AI Models with FastAPI
Package trained ML models into production REST APIs with FastAPI, Pydantic validation, and Docker.
- FastAPI Basics for ML Engineers
- Pydantic Schemas for Request and Response
- Loading and Serving ML Models
- +1 more
Large Language Models with Python
Integrate GPT-4, Claude, and Gemini into Python applications using their APIs for text generation and analysis.
- OpenAI API: chat.completions and Streaming
- Anthropic Claude API in Python
- Function Calling and Tool Use with LLMs
- +1 more
Data Visualization
Understanding data is easier when you can visualize it. This category teaches you how to create insightful visualizations using Python libr…
- Introduction to Data Visualization
- Line Charts
- Histograms and Scatter Plots
- +2 more
LangChain and RAG Systems
Build retrieval-augmented generation systems that ground LLM responses in your private knowledge base.
- LangChain Architecture and LCEL
- Document Loading, Splitting, and Embedding
- Vector Stores: Chroma and FAISS
- +1 more
Advanced Supervised Learning
Master tree-based ensemble methods — decision trees, random forests, and gradient boosting with XGBoost.
- Decision Trees: Theory and Implementation
- Random Forests and Bagging
- Gradient Boosting: GBM and XGBoost
- +1 more
Supervised Learning: Basic Algorithms
This category explores the foundational algorithms of supervised learning, such as linear regression, logistic regression, and evaluation m…
- The Concept of Linear Regression
- Implementing Linear Regression in Python
- The Concept of Logistic Regression
- +2 more
Advanced NLP with Word Embeddings
Go beyond bag-of-words with Word2Vec, GloVe, and fine-tuned BERT embeddings for NLP tasks.
- Word2Vec: Skip-gram and CBOW
- GloVe and FastText Embeddings
- Text Classification with BERT
- +1 more
Transfer Learning with Keras and TensorFlow
Adapt pretrained computer vision models to your own datasets with fine-tuning and feature extraction.
- Transfer Learning Concepts and Strategies
- Using VGG16 and ResNet50 as Base Models
- Fine-tuning: Unfreezing and Retraining
- +1 more
Unsupervised Learning
Unsupervised learning focuses on discovering hidden patterns in data. This category introduces clustering techniques like K-Means and dimen…
- Introduction to Clustering Algorithms
- K-Means Clustering
- K-Means Clustering Project
- +2 more
MLOps Fundamentals
Track experiments, version models, and build reproducible ML pipelines using MLflow and modern MLOps practices.
- Experiment Tracking with MLflow
- Model Registry and Versioning
- Building Reproducible ML Pipelines
- +1 more
Computer Vision with PyTorch
Build image classification and object detection models from scratch and with pretrained PyTorch models.
- PyTorch Tensors and Autograd
- Custom Datasets and DataLoaders
- Building and Training CNNs in PyTorch
- +1 more
Generative AI: VAEs and GANs
Understand and implement variational autoencoders and generative adversarial networks for data generation.
- Autoencoders for Representation Learning
- Variational Autoencoders (VAE)
- GANs: Generator and Discriminator
- +1 more
Artificial Neural Networks
Dive into the world of artificial neural networks, the backbone of modern AI. This category explains the architecture of neural networks, a…
- Introduction to Neural Networks
- Activation Functions
- Feedforward Neural Networks
- +2 more
AI Model Deployment at Scale
Deploy ML models to production using Docker, cloud platforms, and scalable model serving infrastructure.
- Containerizing ML Models with Docker
- Cloud Deployment: AWS SageMaker
- High-Performance Serving with Triton Inference Server
- +1 more
Responsible AI and Explainability
Build trustworthy AI systems by detecting bias, measuring fairness, and making model decisions interpretable.
- Bias Detection in ML Models
- SHAP Values for Model Explainability
- LIME: Local Interpretable Explanations
- +1 more
Natural Language Processing (NLP)
Explore how AI processes and understands human language. This category covers text preprocessing techniques, tokenization, sentiment analys…
- Working with Text Data
- Tokenization and Normalization
- N-Gram Models
- +2 more
AI Systems Architecture and Design
Design end-to-end AI system architectures that are scalable, reliable, and maintainable in production.
- AI System Architecture Patterns
- Scalable ML Pipelines with Airflow
- Feature Stores: Feast and Tecton
- +1 more
Image Processing
Learn how AI interprets and manipulates visual data in this category. Starting with the basics of image representation, you’ll progress to…
- What is Image Data?
- Image Processing with OpenCV
- Convolutional Neural Networks (CNN)
- +2 more
Advanced Reinforcement Learning
Implement modern RL algorithms — policy gradients, PPO, and actor-critic methods — using stable-baselines3.
- Policy Gradient Methods: REINFORCE
- Actor-Critic Methods (A2C)
- Proximal Policy Optimization (PPO)
- +1 more
Graph Neural Networks
Apply GNNs to node classification, link prediction, and graph classification tasks using PyTorch Geometric.
- Graph Theory for Machine Learning
- Graph Convolutional Networks (GCN)
- Node Classification with GNN
- +1 more
Reinforcement Learning
Reinforcement learning enables AI agents to learn from their environment by receiving rewards or penalties for their actions. This category…
- Basic Concepts in Reinforcement Learning
- Q-Table Concept
- Implementing Q-Table in Python
- +2 more
Distributed Training and Large-Scale ML
Train models across multiple GPUs and machines using PyTorch DistributedDataParallel and modern tooling.
- Multi-GPU Training with DataParallel
- DistributedDataParallel (DDP)
- Mixed Precision Training with AMP
- +1 more
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