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IntermediateAI & Machine Learning

~32 Hours 11 Lessons100% Free

Artificial Intelligence & Machine Learning Engineering

Neural networks, PyTorch, Large Language Models (LLMs), Embeddings, and RAG Architecture.

From Python data foundations to cutting-edge GenAI: NumPy & Pandas dataframes, Supervised/Unsupervised Learning with Scikit-learn, Neural Networks & Deep Learning with PyTorch, LLM APIs, Vector Embeddings, Vector Databases (pgvector, Pinecone), and Retrieval-Augmented Generation (RAG).

What You Will Learn

NumPy Vectorized Computing & Pandas Data Wrangling
Deep Learning Architectures & PyTorch Tensors
Large Language Models (LLM) Prompt Engineering & APIs
Vector Embeddings & Semantic Cosine Similarity Search
Production Retrieval-Augmented Generation (RAG) Systems

Prerequisites & Environment

Python Fundamentals
Basic Linear Algebra

Complete Course Curriculum (11 Modules)

11 In-Depth Lessons

Module 01

Module 1: AI & Machine Learning Foundations

1 Lessons

AI vs ML vs Deep Learning vs GenAI, datasets, training/test splits, and loss functions.

1.1AI & Machine Learning Foundations

Module 02

Module 2: Python Data Science Stack (NumPy & Pandas)

1 Lessons

NumPy vectorized n-dimensional arrays, matrix math, Pandas DataFrames, filtering, and aggregation.

2.1Vectorized Computing with NumPy & Pandas DataFrames

Module 03

Module 3: Data Visualization & Exploratory Analysis

1 Lessons

Matplotlib plotting, Seaborn statistical heatmaps, feature correlations, and outlier detection.

3.1Exploratory Data Analysis (EDA) & Visualization

Module 04

Module 4: Supervised Learning (Regression & Classification)

1 Lessons

Linear Regression, Logistic Regression, Decision Trees, Random Forests, and Scikit-learn.

4.1Supervised Learning: Regression, Classification & Random Forests

Module 05

Module 5: Unsupervised Learning & Clustering

1 Lessons

K-Means clustering, Principal Component Analysis (PCA) dimensionality reduction, and anomaly detection.

5.1K-Means Clustering & Dimensionality Reduction (PCA)

Module 06

Module 6: Neural Networks & Deep Learning Basics

1 Lessons

Artificial neurons, Perceptrons, activation functions (ReLU, Sigmoid), forward pass, and backpropagation.

6.1Artificial Neurons, Activation Functions & Backpropagation

Module 07

Module 7: Deep Learning with PyTorch

1 Lessons

PyTorch Tensors, GPU acceleration (CUDA), torch.nn.Module, loss functions, and Adam optimizer.

7.1PyTorch Tensors, Autograd & Building nn.Module Networks

Module 08

Module 8: Natural Language Processing & Transformers

1 Lessons

Tokenization, self-attention mechanism, transformer architecture (Encoder/Decoder), and BERT vs GPT.

8.1The Transformer Architecture & Self-Attention Mechanism

Module 09

Module 9: Large Language Models (LLMs) & Prompt Engineering

1 Lessons

Autoregressive text generation, temperature, top-p sampling, Few-Shot prompting, and Chain-of-Thought (CoT).

9.1LLM APIs, Sampling Parameters & Prompt Engineering

Module 10

Module 10: Vector Embeddings & Vector Databases (pgvector, Pinecone)

1 Lessons

Dense vector representations, cosine distance vs dot product, pgvector PostgreSQL extension, and HNSW indexes.

10.1Vector Embeddings & Vector Databases (pgvector)

Module 11

Module 11: Retrieval-Augmented Generation (RAG) Architecture

1 Lessons

Document ingestion pipelines, chunking strategies, semantic retrieval, context re-ranking, and hallucination reduction.

11.1Production Retrieval-Augmented Generation (RAG) Systems