AI/ML ENGINEERING FULL COURSE

About Course

This course provides a structured pathway into machine learning and AI engineering, starting from core programming and statistical foundations and progressing toward practical model development and deployment. Learners will work with Python, data processing tools, and machine learning libraries to build a strong technical base for modern AI systems.

The program covers supervised and unsupervised learning, model training workflows, feature engineering, and evaluation techniques using real datasets. It also introduces deep learning concepts and foundational neural network architectures, with a focus on understanding how models learn from data rather than only applying frameworks.

A key part of the course focuses on machine learning engineering practices, including pipeline design, reproducibility, model tracking, and version control. Learners will also explore deployment fundamentals, including how models are prepared and served in real-world environments.

By the end of the course, students will have built multiple machine learning projects and gained practical understanding of how AI systems are developed, tested, and maintained in production settings.

Course Content

FUNDAMENTALS OF PYTHON PROGRAMMING FOR DATA SCIENCE

Introduction to Jupyter Notebooks and Python Programming

00:00
Variables, Data Types, and Type Conversion in Python

00:00
Operators in Python.

00:00
Basic Data Structures I (Lists)

00:00
Basic Data Structures In Python II (Tuples)

00:00
Basic Data Structures In Python III (Dictionaries & Sets)

00:00
Control Flow in Python I: Conditional Statements

00:00
Control Flow in Python II: For Loops.

00:00
Control Flow in Python III: While Loop.

00:00
Comprehensions in Python

00:00
Functions In Python.

00:00
Exception Handling In Python.

00:00
Lambda Functions and Functional Tools: map, filter, and reduce

00:00
From Jupyter Notebooks to Modular Python Projects
Object Oriented Programming (OOP) I

00:00
Object Oriented Programming (OOP) II (Encapsulation & Inheritance)

00:00
Python Environment Management and Development Tooling
Fundamentals of Unit Testing with Pytest

EXPLORATORY DATA ANALYSIS WITH PYTHON

FUNDAMENTAL STATISTICS AND PROBABILITY FOR MACHINE LEARNING.

Introduction to Statistics For Data Science And Machine Learning
Types of Data and Measurement Scales
Descriptive Statistics: Summarizing Data
Understanding Data Distribution
Probability Fundamentals
Sampling and Data Collection.
Hypothesis Testing 1 (Introduction)
Hypothesis Testing 2 (Tests of Difference)
Hypothesis Testing 3 (Tests of Association)
Confidence Intervals
Time Series Analysis
Applying Statistics in Real-World Data Workflow
Common Statistical Mistakes in Data Workflows.

CLASSICAL MACHINE LEARNING

Introduction To Machine Learning.
Understanding Linear Regression

00:00
Understanding Logistic Regression.

00:00
Decision Trees.

00:00
Bagging With RandomForests Algorithm.

00:00
Gradient Boosting With XGBoost.

00:00
Cross-Validation with K-Fold and Stratified K-Fold

00:00
HyperParameter Optimization I : GridSearch

00:00
Hyperparameter Optimization II: Randomized Search

00:00
Hyperparameter Optimization III: OPTUNA

00:00
Unsupervised Learning I: K-Means Clustering

00:00
Unsupervised Learning II: DBSCAN Clustering

00:00
Dimensionality Reduction with Principal Component Analysis (PCA)

00:00

DEEP LEARNING

Introduction to Deep Learning and Neural Networks with PyTorch
PyTorch Fundamentals: Tensors, Autograd, and GPU Acceleration
Building Neural Networks with torch.nn
Training Loops and Optimization in PyTorch
Data Handling with Dataset and DataLoader
Convolutional Neural Networks for Computer Vision
Transfer Learning in Computer Vision (ResNet, EfficientNet, VGG)
Sequence Models: RNNs & LSTMs (Text)
Sequence Models II: RNNs & LSTMs (Time Series)
Transformer Architecture Fundamentals
Model Evaluation, Regularization, and Debugging Deep Learning Models
End-to-End Deep Learning Project with PyTorch: Vision
End-to-End Deep Learning Project with PyTorch: Text

LLM ENGINEERING & GENERATIVE AI SYSTEMS

Introduction to Large Language Models and Generative AI Systems
Prompt Engineering and Instruction Design
Working with LLM APIs and Open-Source Models
Embeddings and Vector Databases
Retrieval-Augmented Generation (RAG) Systems
Fine-Tuning Large Language Models with LoRA and QLoRA
AI Agents, Tool Calling, and Multi-Step Reasoning Systems
Evaluation and Testing for LLM Applications
Deployment, Optimization, and Inference Engineering for LLMs
Building End-to-End Generative AI Applications

MODEL DEPLOYMENT & MLOPS

Introduction to MLOps and Model Deployment Lifecycle
Orchestration and Experiment Tracking with MLflow, ZenML, and Weights & Biases
From Notebook to Production: Structuring ML Projects for Deployment
Model Serialization and Packaging for Deployment
Building ML APIs with FastAPI
Interactive ML Applications with Streamlit
Git and GitHub for Version Control and Collaboration
CI/CD Pipelines for Machine Learning Systems
Model Monitoring, Logging, and Observability with Pydantic Logfire
Scalable Model Serving and End-to-End Deployment Architecture

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About Course

Course Content

FUNDAMENTALS OF PYTHON PROGRAMMING FOR DATA SCIENCE

Introduction to Jupyter Notebooks and Python Programming

Variables, Data Types, and Type Conversion in Python

Operators in Python.

Basic Data Structures I (Lists)

Basic Data Structures In Python II (Tuples)

Basic Data Structures In Python III (Dictionaries & Sets)

Control Flow in Python I: Conditional Statements

Control Flow in Python II: For Loops.

Control Flow in Python III: While Loop.

Comprehensions in Python

Functions In Python.

Exception Handling In Python.

Lambda Functions and Functional Tools: map, filter, and reduce

From Jupyter Notebooks to Modular Python Projects

Object Oriented Programming (OOP) I

Object Oriented Programming (OOP) II (Encapsulation & Inheritance)

Python Environment Management and Development Tooling

Fundamentals of Unit Testing with Pytest

EXPLORATORY DATA ANALYSIS WITH PYTHON

Numpy Fundamentals

Introduction to Pandas for Data Manipulation

Data Cleaning and Preparation with Pandas.

Fundamentals of Data Visualization with Matplotlib, Seaborn, and Plotly

Advanced Visualization with Matplotlib, Seaborn and Plotly.

Complete EDA Workflow Project

FUNDAMENTAL STATISTICS AND PROBABILITY FOR MACHINE LEARNING.

Introduction to Statistics For Data Science And Machine Learning

Types of Data and Measurement Scales

Descriptive Statistics: Summarizing Data

Understanding Data Distribution

Probability Fundamentals

Sampling and Data Collection.

Hypothesis Testing 1 (Introduction)

Hypothesis Testing 2 (Tests of Difference)

Hypothesis Testing 3 (Tests of Association)

Confidence Intervals

Time Series Analysis

Applying Statistics in Real-World Data Workflow

Common Statistical Mistakes in Data Workflows.

CLASSICAL MACHINE LEARNING

Introduction To Machine Learning.

Understanding Linear Regression

Understanding Logistic Regression.

Decision Trees.

Bagging With RandomForests Algorithm.

Gradient Boosting With XGBoost.

Cross-Validation with K-Fold and Stratified K-Fold

HyperParameter Optimization I : GridSearch

Hyperparameter Optimization II: Randomized Search

Hyperparameter Optimization III: OPTUNA

Unsupervised Learning I: K-Means Clustering

Unsupervised Learning II: DBSCAN Clustering

Dimensionality Reduction with Principal Component Analysis (PCA)

DEEP LEARNING

Introduction to Deep Learning and Neural Networks with PyTorch

PyTorch Fundamentals: Tensors, Autograd, and GPU Acceleration

Building Neural Networks with torch.nn

Training Loops and Optimization in PyTorch

Data Handling with Dataset and DataLoader

Convolutional Neural Networks for Computer Vision

Transfer Learning in Computer Vision (ResNet, EfficientNet, VGG)

Sequence Models: RNNs & LSTMs (Text)

Sequence Models II: RNNs & LSTMs (Time Series)

Transformer Architecture Fundamentals

Model Evaluation, Regularization, and Debugging Deep Learning Models

End-to-End Deep Learning Project with PyTorch: Vision

End-to-End Deep Learning Project with PyTorch: Text

LLM ENGINEERING & GENERATIVE AI SYSTEMS

Introduction to Large Language Models and Generative AI Systems

Prompt Engineering and Instruction Design

Working with LLM APIs and Open-Source Models

Embeddings and Vector Databases

Retrieval-Augmented Generation (RAG) Systems

Fine-Tuning Large Language Models with LoRA and QLoRA

AI Agents, Tool Calling, and Multi-Step Reasoning Systems

Evaluation and Testing for LLM Applications

Deployment, Optimization, and Inference Engineering for LLMs