AI and ML in Python programming moduleAI & ML in Python Programming

Learn the basics of programming computers with Python. After this course you are ready for more advanced programming courses for IOT and Data Analytics.

Book an appointment Call 847-350-9034

14 course modulesHands-on labsFinal project

Python foundations: Modules 1 to 7

From your first program to object-oriented programming.

Module 1: Introduction to Python

✓What is Python and its role in AI/ML✓Installing Python, Jupyter, and IDEs (VS Code, Anaconda)✓Writing your first Python program✓Understanding programming concepts and flow

Module 2: Variables and Data Types

✓Basic data types: int, float, str, bool✓Variable declaration, naming conventions✓Type conversion and type checking✓Simple operations and string manipulation

Module 3: Control Structures

✓Conditional logic: if, elif, else✓Logical operators: and, or, not✓Loops: for, while, and nested loops✓User input and basic validation

Module 4: Data Structures in Python

✓Lists and list operations✓Dictionaries, sets, and tuples✓Iterating through data structures✓List comprehensions and basic error handling

Module 5: Functions and Modules

✓Defining and calling functions✓Parameters, return values✓Scope and variable lifetime✓Importing built-in and custom modules

Module 6: File Handling and Exceptions

✓Reading and writing files (text and CSV)✓Using with statements✓Exception handling: try, except, finally✓Common file I/O use cases in data tasks

Module 7: Object-Oriented Programming

✓Classes and objects✓Attributes, methods, and constructors✓Inheritance, encapsulation, and polymorphism✓Real-world examples (e.g., data models)

AI and machine learning: Modules 8 to 14

Libraries, data preparation, learning algorithms and a final project.

Module 8: Python Libraries for AI/ML

✓NumPy for numerical computing✓Pandas for data manipulation✓Matplotlib and Seaborn for visualization✓Scikit-learn basics

Module 9: Data Handling and Preprocessing

✓Loading datasets (CSV, Excel, web data)✓Handling missing values, duplicates✓Encoding categorical variables✓Feature scaling (Normalization and Standardization)

Module 10: Machine Learning Fundamentals

✓What is Machine Learning? Supervised and Unsupervised✓Train-test split✓Model evaluation: accuracy, confusion matrix, precision, recall

Module 11: Supervised Learning

✓Linear Regression✓Logistic Regression✓k-Nearest Neighbors (k-NN)✓Decision Trees and Random Forests

Module 12: Unsupervised Learning

✓Clustering: k-Means✓Hierarchical Clustering✓Dimensionality Reduction: PCA (basic)

Module 13: AI Concepts and Use Cases

✓What is Artificial Intelligence?✓AI vs ML vs Deep Learning✓Real-life applications of AI/ML in Python✓Basics of Natural Language Processing (NLP)

Module 14: Final Project

✓Choose a real-world dataset✓Apply data cleaning, visualization, model building✓Evaluate and present results✓Peer code review and feedback

Support and salient features

Career support and classroom features that come with the course.

Staffing support

✓Resume preparation✓Mock interview preparation✓Phone interview preparation✓Face to face interview preparation✓Project and technology preparation✓Internship with internal project work✓Externship with client project work

Our salient features

✓Hands-on labs and homework✓Group discussion and case study✓Course project work✓Regular quiz and exam✓Regular support beyond the classroom✓Students can re-take the class at no cost✓Dedicated conference rooms for group project work✓Live streaming for remote students✓Video recording to catch up on a missed class

Explore more training

See other programs, funding options and the class schedule. All training programs Education grant program Upcoming classes

Ready to start learn Python and AI?

Call 847-350-9034 or book an appointment to talk about this module and funding options.Book an appointment Call 847-350-9034 Education grant program

Python foundations: Modules 1 to 7

From your first program to object-oriented programming.1

Module 1: Introduction to Python

✓What is Python and its role in AI/ML✓Installing Python, Jupyter, and IDEs (VS Code, Anaconda)✓Writing your first Python program✓Understanding programming concepts and flow2

Module 2: Variables and Data Types

✓Basic data types: int, float, str, bool✓Variable declaration, naming conventions✓Type conversion and type checking✓Simple operations and string manipulation3

Module 3: Control Structures

✓Conditional logic: if, elif, else✓Logical operators: and, or, not✓Loops: for, while, and nested loops✓User input and basic validation4

Module 4: Data Structures in Python

✓Lists and list operations✓Dictionaries, sets, and tuples✓Iterating through data structures✓List comprehensions and basic error handling5

Module 5: Functions and Modules

✓Defining and calling functions✓Parameters, return values✓Scope and variable lifetime✓Importing built-in and custom modules6

Module 6: File Handling and Exceptions

✓Reading and writing files (text and CSV)✓Using with statements✓Exception handling: try, except, finally✓Common file I/O use cases in data tasks7

Module 7: Object-Oriented Programming

✓Classes and objects✓Attributes, methods, and constructors✓Inheritance, encapsulation, and polymorphism✓Real-world examples (e.g., data models)

AI and machine learning: Modules 8 to 14

Libraries, data preparation, learning algorithms and a final project.1

Module 8: Python Libraries for AI/ML

✓NumPy for numerical computing✓Pandas for data manipulation✓Matplotlib and Seaborn for visualization✓Scikit-learn basics2

Module 9: Data Handling and Preprocessing

✓Loading datasets (CSV, Excel, web data)✓Handling missing values, duplicates✓Encoding categorical variables✓Feature scaling (Normalization and Standardization)3

Module 10: Machine Learning Fundamentals

✓What is Machine Learning? Supervised and Unsupervised✓Train-test split✓Model evaluation: accuracy, confusion matrix, precision, recall4

Module 11: Supervised Learning

✓Linear Regression✓Logistic Regression✓k-Nearest Neighbors (k-NN)✓Decision Trees and Random Forests5