AI Python Course

Lesson 1 • Setting Up Your Python Environment

Install Python, configure virtual environments, and choose an IDE suited for AI development. Establishes the technical baseline every subsequent chapter depends on.

Lesson 2 • Control Flow and Loops

Apply conditionals and iteration to automate repetitive AI data tasks. Directly enables batch processing patterns used throughout the course.

Lesson 3 • Debugging and Error Handling

Read tracebacks, use a debugger, and write try/except blocks to handle runtime errors. Reliable error handling prevents silent failures in AI workflows.

Lesson 4 • Core Python Syntax and Data Types

Cover variables, operators, strings, numbers, and booleans with AI-relevant examples. Provides the vocabulary for all data manipulation tasks ahead.

Lesson 1 • Working with Strings and Text

Apply string methods, formatting, and regular expressions to clean raw text data. Text preprocessing is a foundational step in every NLP and LLM pipeline.

Lesson 2 • File I/O and Data Serialization

Read and write text, CSV, and JSON files to persist and exchange AI data. Connects Python scripts to real-world data sources used in later chapters.

Lesson 3 • Dictionaries and Sets

Store key-value pairs and unique collections for fast lookup and deduplication. These structures underpin feature maps and vocabulary tables in NLP tasks.

Lesson 4 • Lists and Tuples in Depth

Manipulate ordered sequences using slicing, comprehensions, and built-in methods. Lists are the primary container for raw AI datasets and model outputs.

Lesson 1 • Linear Algebra with NumPy

Perform matrix multiplication, decomposition, and solving linear systems. These operations are the mathematical core of neural networks and dimensionality reduction.

Lesson 2 • Indexing, Slicing, and Reshaping

Select, filter, and reshape array data using advanced indexing techniques. Reshaping is required before feeding data into neural network layers.

Lesson 3 • Statistical Aggregations and Random Sampling

Compute descriptive statistics and generate reproducible random samples for AI experiments. Reproducible sampling ensures fair model evaluation and benchmarking.

Lesson 4 • Vectorized Operations and Broadcasting

Apply element-wise math and broadcasting rules to avoid slow Python loops. Vectorization is the performance foundation of all numerical AI computations.

Lesson 1 • Grouping, Aggregation, and Merging

Summarize data with groupby and combine datasets using merge and join operations. These skills enable feature engineering from multiple relational data sources.

Lesson 2 • Data Cleaning and Transformation

Handle missing values, duplicates, and incorrect types to produce clean datasets. Clean data directly determines the quality of trained AI models.

Lesson 3 • Feature Engineering with Pandas

Create new features through binning, encoding, and applying custom functions. Engineered features often have the largest impact on model accuracy.

Lesson 4 • Filtering, Sorting, and Querying

Apply boolean filters, query strings, and sort operations to isolate relevant data. Precise subsetting is essential for creating training and validation splits.

Lesson 1 • Matplotlib Fundamentals

Build line, bar, and scatter plots using Matplotlib's object-oriented API. A solid Matplotlib foundation supports every higher-level visualization library.

Lesson 2 • Exploratory Data Analysis Workflows

Combine Pandas and visualization tools to systematically profile a new dataset. Structured EDA reduces surprises during model training and evaluation.

Lesson 3 • Visualizing Model Performance

Plot confusion matrices, ROC curves, and learning curves to evaluate AI models. Performance visuals are essential for diagnosing underfitting and overfitting.

Lesson 1 • Unsupervised Learning and Dimensionality Reduction

Cluster data with K-Means and reduce dimensions with PCA for exploratory analysis. Unsupervised methods reveal hidden structure in unlabeled AI datasets.

Lesson 2 • The Scikit-Learn API and Workflow

Understand the fit/predict/transform pattern and build a complete ML pipeline. The unified API makes switching between models and preprocessing steps seamless.

Lesson 3 • Supervised Learning: Classification

Train logistic regression, decision trees, and ensemble classifiers on labeled data. Classification is the most common AI task in business and research applications.

Lesson 4 • Supervised Learning: Regression

Predict continuous targets using linear, polynomial, and regularized regression models. Regression underpins forecasting, pricing, and demand estimation use cases.

Lesson 1 • Training Loops and Optimization

Implement full training loops with loss functions, optimizers, and learning rate schedules. A well-structured training loop is the core of every deep learning experiment.

Lesson 2 • Building Neural Networks with nn.Module

Define custom network architectures using nn.Module, layers, and activation functions. Modular network design enables rapid experimentation with different architectures.

Lesson 3 • Convolutional Neural Networks for Images

Build CNNs with convolutional, pooling, and fully connected layers for image tasks. CNNs are the dominant architecture for computer vision AI applications.

Lesson 4 • Tensors and Autograd Fundamentals

Create and manipulate PyTorch tensors and understand automatic differentiation. Autograd is the engine behind gradient-based learning in all deep learning models.

Lesson 1 • LLM APIs and Prompt Engineering

Call LLM APIs, structure prompts, and parse responses for downstream tasks. Effective prompting dramatically improves output quality without any model training.

Lesson 2 • Hugging Face Transformers Library

Load pretrained models and tokenizers from the Hugging Face Hub for inference. The Transformers library provides access to thousands of ready-to-use AI models.

Lesson 3 • Retrieval-Augmented Generation

Combine vector search with LLM generation to ground responses in external documents. RAG reduces hallucination and enables LLMs to answer domain-specific questions.

Lesson 4 • Fine-Tuning LLMs on Custom Data

Prepare datasets and run parameter-efficient fine-tuning to adapt models to new domains. Fine-tuning yields higher accuracy than prompting alone for specialized tasks.