Learn Python programming and Artificial Intelligence step-by-step through fun projects, games, and real AI models. This course takes students from coding basics to building their first Machine Learning systems.
to know more or enquire about the sessions
This course introduces students to Python programming and Artificial Intelligence in a practical, project-based way.
Students begin with Python fundamentals such as variables, loops, conditions, and functions, then gradually move into data handling, visualization, and Machine Learning concepts.
Throughout the course, learners will build mini projects and AI systems, including games, prediction models, computer vision applications, and chatbots.
By the end of the course, students will understand how real AI systems work and will complete a final capstone AI project such as:
• AI Chatbot
• Face Detection System
• AI Game Bot
• Voice Assistant
The course focuses on logical thinking, computational problem solving, and real-world AI applications, making it perfect for beginners who want to explore Artificial Intelligence through Python.
Lecture 1.1 Introduction to Python, variables, and basic data types used in programming.
Lecture 1.2 Understanding input and output operations for user interaction.
Lecture 1.3 Type conversion techniques and why data type handling matters.
Lecture 1.4 Practice session with basic exercises to strengthen foundational concepts.
Lecture 2.1 Understanding conditions using if-else statements for decision-making.
Lecture 2.2 Applying decision logic to real-life scenarios for better problem-solving.
Lecture 2.3 Nested conditions and use of logical operators in complex situations.
Lecture 2.4 Practice challenges and puzzle-solving to build logical thinking.
Lecture 3.1 Introduction to loops (for and while) for repeated task execution.
Lecture 3.2 Understanding loop control and pattern-based logical thinking.
Lecture 3.3 Combining loops with conditions to solve structured problems.
Lecture 3.4 Mini Project: Build a Number Guessing Game using loops and logic.
Lecture 4.1 Introduction to lists and their role in storing multiple data values.
Lecture 4.2 List operations and basic data handling techniques.
Lecture 5.1 Introduction to functions and how they organize reusable code blocks.
Lecture 5.2 Importance of functions in AI programs and large applications.
Lecture 5.3 Functions with parameters and return values for flexible programming.
Lecture 5.4 Practice exercises to build modular and structured programs.
Lecture 6.1 Designing logic-building challenges using structured programming.
Lecture 6.2 Mini Project: Building a Smart Calculator with multiple operations.
Lecture 6.3 Debugging, optimizing, and improving project code quality.
Lecture 6.4 Project extension activities and feature enhancement practice.
Lecture 7.1 Introduction to Python’s random module and randomness concepts.
Lecture 7.2 Generating random values for simulations and simple games.
Lecture 7.3 Game logic design and creating scoring systems.
Lecture 7.4 Mini Project: Rock-Paper-Scissors AI Game development.
Lecture 8.1 Introduction to Artificial Intelligence and its evolution.
Lecture 8.2 Understanding differences between AI, Machine Learning, and Deep Learning.
Lecture 8.3 Real-world AI systems and applications across industries.
Lecture 8.4 Activity: Identifying and designing AI ideas from daily life problems.
Lecture 9.1 How machines learn: training vs prediction concepts.
Lecture 9.2 Importance of data and introduction to datasets.
Lecture 9.3 Structured vs unstructured data with practical examples.
Lecture 9.4 Activity: Exploring datasets and understanding data patterns.
Lecture 10.1 Introduction to NumPy arrays and numerical data handling.
Lecture 10.2 Introduction to Pandas for dataset loading and analysis.
Lecture 10.3 Graph basics and importance of data visualization.
Lecture 10.4 Using Matplotlib to plot graphs and visualize patterns.
Lecture 11.1 Understanding Machine Learning, features, and labels.
Lecture 11.2 Types of ML: Supervised and Unsupervised learning methods.
Lecture 11.3 Building first ML model using Scikit-Learn for predictions.
Lecture 11.4 Mini Project: Student Marks Prediction using Linear Regression.
Lecture 12.1 Classification models and Decision Trees for categorization tasks.
Lecture 12.2 Introduction to Computer Vision and image processing using OpenCV.
Lecture 12.3 Building AI applications: Chatbots, Voice Assistants, and Face Detection.
Lecture 12.4 Final Capstone Project: Student builds an AI project and presents it for evaluation.
I am a Robotics Teacher with experience in teaching STEM concepts through hands-on, engaging lessons designed for young learners. I specialize in simplifying complex topics into practical, easy-to-understand activities that emphasize creativity, critical thinking, and real-world problem solving. I have successfully taught 200+ students aged 10–13 across India, the US, UK, and Canada using platforms such as Arduino, Raspberry Pi Pico, and Micro:bit. Along with teaching, I also contribute to curriculum design to make learning accessible and impactful.
Passionate about making technology learning exciting and future-ready, I am committed to preparing students through interactive methods and practical projects. My student-centric and detail-oriented teaching approach ensures that every learner feels supported and inspired.
Educational Qualification – B.Tech in Computer Science and Engineering from Lovely Professional University
Experience – 2+ year in NaivoTech, delivered live online and offline robotics sessions
Everything you need to know about this course
You need a working internet connection to watch videos, join online classes, and get help when needed.
A personal computer is essential for hands-on practice and project work.
Basic understanding of English is required, as the course content and instructions are primarily in English
A structured path designed to take you from basics to mastery with clarity, confidence, and real-world impact.
Start with fundamental concepts and build a strong foundation.
Expand your knowledge and start building practical projects.
Dive deeper into specialized areas and master complex techniques.
Achieve expert-level proficiency and innovate with your skills.
Join industry-focused courses with expert guidance, practical learning, certification, and career growth opportunities.
Our curriculum is meticulously designed in collaboration with industry leaders to ensure every skill you acquire is not just current, but in high demand.
Our mentors are seasoned professionals and thought leaders who provide unparalleled guidance and personalized feedback.
Our vibrant community of professionals offers continuous support, mentorship, and a platform for lifelong career acceleration.
.png)
Discover more similar content to expand your knowledge and sharpen your skills.
.png)
.png)
.png)
.png)
.png)
.png)
.png)
.png)
Learn Python programming and explore Artificial Intelligence by creating smart applications, machine learning models, and real-world AI projects.
Join 50k+ engineers leveling up today.
Join 50k+ engineers leveling up today.
Whether it’s about our courses, admissions, or anything else—just ask!
A clear, step-by-step milestone path to take you from the basics to building real-world AI solutions.
Your journey starts with understanding what Artificial Intelligence is and how machines can learn from data. You will also get introduced to Python, one of the most popular programming languages used in AI.
In this stage, you will learn the core building blocks of Python such as variables, conditions, loops, and lists. These concepts help you develop logical thinking and problem-solving skills needed for AI development.
Once you understand the basics, you will practice writing programs and solving coding challenges. You will learn how to structure code using functions and build small Python programs.
Artificial Intelligence works with data. In this step, you will learn how to handle datasets using tools like NumPy and Pandas and visualize information using graphs and charts.
Now you will step into Machine Learning. You will understand how AI models learn patterns from data and build simple prediction models using Python libraries.
In the final stage, you will apply everything you learned to create real AI projects such as prediction systems, simple AI games, or computer vision applications and present your final project with confidence.
Our academic counselors are available 24/7 to help you choose the right path for your career goals.
Your information is 100% secure with us.
