Machine learning has evolved rapidly with the rise of large language models, AI-assisted development, and modern data pipelines. This three-day hands-on course teaches students how to build practical machine learning solutions using Python while integrating modern AI tools and workflows. Participants begin with a Python and data science refresher before exploring core machine learning techniques including regression, classification, and clustering. The course then moves into building complete machine learning pipelines, evaluating model performance, and improving models using ensemble techniques. Modern AI topics are woven throughout the course including AI-assisted coding, prompt-driven data analysis, and integrating large language models into Python applications. Students complete several hands-on labs where they build models, analyze datasets, visualize results, and integrate AI services into applications. By the end of the course, students will understand how to design, build, evaluate, and deploy practical machine learning solutions using Python and modern AI tooling.

Audience

• Python Developers
• Data Analysts and Aspiring Data Scientists
• DevSecOps Engineers
• AI-Curious Managers and Architects

Prerequisites

• Basic Python programming knowledge
• Familiarity with Python data structures
• Basic understanding of statistics and problem solving
• Prior exposure to pandas or numpy is helpful but not required

Learning Outcomes

• Evaluate and enhance model performance with ensemble methods.
• Integrate AI services and LLMs into applications.
• Design complete machine learning pipelines.
• Implement core ML techniques such as regression, classification, and clustering.

AI and ML Foundations with Python Outline

Python Foundations for Machine Learning

1. Review of Python fundamentals for data analysis
2. Working with lists, dictionaries, and arrays
3. Python workflows for data science projects

Working with Jupyter Notebooks and ML Environments

1. Setting up Anaconda and Jupyter
2. Notebook workflow best practices
3. Running Python experiments interactively

Core Python Libraries for Machine Learning

1. NumPy for numerical computing
2. pandas for dataset manipulation
3. matplotlib and seaborn for visualization

Introduction to Artificial Intelligence and Machine Learning

1. Understanding the AI landscape
2. Differences between ML, deep learning, and LLMs
3. Common AI use cases across industries

Machine Learning Concepts and Workflows

1. Training data and features
2. Labels and target variables
3. Model training and prediction

Data Preparation and Feature Engineering

1. Data cleaning techniques
2. Handling missing data
3. Managing inconsistent datasets

Feature Engineering Concepts

1. Feature scaling
2. Encoding categorical variables
3. Detecting and handling outliers

Supervised Machine Learning

1. Linear regression fundamentals
2. Multiple regression models
3. Practical regression use cases

Classification Models

1. Binary vs multi-class classification
2. Logistic regression
3. Decision trees

Unsupervised Machine Learning

1. Understanding clustering
2. K-means clustering
3. Real world clustering applications

Data Visualization for Machine Learning

1. Scatter plots and distribution charts
2. Correlation analysis
3. Feature importance visualization

Model Evaluation and Optimization

1. Training vs testing data
2. Accuracy, precision, recall, and F1 score
3. Avoiding overfitting and underfitting

Ensemble Learning

1. Random forests
2. Gradient boosting concepts
3. Ensemble model advantages

Machine Learning Pipelines

1. Data ingestion
2. Feature engineering
3. Model training and evaluation

Integrating Generative AI into Python Applications

1. Overview of LLM architectures
2. Using APIs for AI services
3. AI-assisted development workflows

Explainable AI and Responsible AI

1. Feature importance
2. Model transparency

Final Hands-On Project

1. Prepare a dataset
2. Train a machine learning model
3. Evaluate performance
4. Integrate AI features into the workflow

Labs

• Exploring and Analyzing a Dataset with Python
• Exploring Machine Learning Libraries
• Preparing a Dataset for Machine Learning
• Building a Regression Model in Python
• Implementing Classification Models
• Performing Customer Segmentation with Clustering
• Visualizing Model Results
• Evaluating Model Performance
• Building Ensemble Models
• Creating a Complete Machine Learning Pipeline
• Using Python to Interact with an LLM API
• Visualizing Model Explainability
• End-to-End Machine Learning Project