CS50's Introduction to Artificial Intelligence with Python

This instructor-led program is based on the renowned Harvard CS50’s Introduction to AI with Python course, redesigned for interactive, cohort-based learning with guided instruction, hands-on labs, and real-world projects.

Participants will learn to design intelligent systems, understand fundamental AI concepts, and implement machine learning models using Python. The program strikes a balance between theory and practical programming exercises, enabling professionals and students to acquire industry-ready AI skills.

Overview

Duration & Delivery

• Format: Instructor-led training (Online or In-Person)

• Duration: 5 Days (recommended; can be adapted)

• Includes: Lectures, live coding, hands-on labs, real-world projects, course materials

• Instructor: Experienced AI/ML practitioner with Python expertise

Learning Objectives

By the end of this program, participants will be able to:

• Understand core AI concepts and definitions

• Model search problems and design search algorithms

• Represent knowledge and reasoning in intelligent systems

• Build machine learning models for classification and prediction

• Implement neural networks using libraries such as TensorFlow

• Apply natural language processing techniques

• Design intelligent agents and solve real-world problems

• Write and debug Python programs implementing AI techniques

Program Features

• Based on Harvard’s CS50 AI curriculum, adapted for instructor-led training

• Live coding sessions and hands-on labs

• Interactive problem-solving with real datasets

• Guided implementation of AI algorithms in Python

• Coverage of machine learning, neural networks, search algorithms, NLP

• Real-world project assignments

• Instruction by an experienced AI/ML professional

Detailed Course Schedule

Day 01: Foundations of Artificial Intelligence and Search Algorithms

• Introduction to AI concepts, history, and applications

• Modeling search problems

• Implementing uninformed and informed search algorithms (DFS, BFS, A*)

• Python exercises: Writing and testing search algorithms

• Real-world use cases of search in AI systems

Outcome: Ability to model and solve search problems using Python.

Day 02: Knowledge Representation and Reasoning

• Propositional logic and logical inference

• Constraint satisfaction problems (CSP)

• Knowledge representation in AI

• Python implementations of logical inference

• Case studies: Planning and scheduling problems.

Outcome: Skills to represent and reason about knowledge in intelligent systems.

Day 03: Machine Learning Fundamentals with Python

• Supervised learning concepts (classification, regression)

• Implementing ML models using Scikit-learn

• Training, testing, and evaluating models

• Feature engineering and preprocessing

• Hands-on labs: Building ML classifiers for real datasets

Outcome: Practical ability to build and evaluate machine learning models in Python.

Day 04: Neural Networks and Natural Language Processing

• Introduction to neural networks

• Using TensorFlow and Keras to build simple models

• Natural Language Processing (NLP) basics

• Text tokenization, vectorization, and sentiment analysis

• Labs: Building and training neural networks, simple NLP tasks

Outcome: Ability to implement neural networks and basic NLP solutions in Python.

Day 05: Intelligent Agents, Ethics, and Final Projects

• Designing intelligent agents

• Integrating AI components into larger systems

• Discussion of AI ethics, fairness, and societal impacts

• Capstone mini-project: Building an AI-powered Python application

• Review, Q&A, and certification guidance

Outcome: Confidence to design, implement, and discuss AI solutions using Python.

Hands-On Learning Approach

• Live coding and demonstrations

• Guided labs using Python, Scikit-learn, TensorFlow, Keras

• Real-world datasets and case studies

• Collaborative problem-solving

• Instructor support during exercises

• Final mini-project showcasing acquired skills

Transform your Python skills into practical AI solutions. Join us and unlock the power of artificial intelligence.

Prerequisites

• Familiarity with Python programming (variables, loops, functions, data structures)

• No prior AI or ML experience required

• Developers and programmers seeking a solid introduction to AI

• Data scientists and analysts expanding into AI

• University students and graduates in computer science or related fields

• Technical professionals preparing for advanced AI or ML certifications

• Anyone with basic Python experience interested in applied AI

Who Should Attend?

Certification Readiness

While this program does not award a formal Harvard certificate, it delivers the foundational knowledge and practical skills needed for:

• Advanced AI/ML courses

• Professional certifications in AI and machine learning

• Real-world AI development projects