Learn AI In Python Online: Free Course with AI Tutor & Certificate
This free artificial intelligence in python course offers an optional paid certificate. Across 105 hours and 276 video lessons, you'll master machine learning, deep learning, and NLP. You will build real systems, like a fraud detection model for PayPal, preparing you for an AI developer role.
What you'll learn in this AI In Python course
Five concrete outcomes you'll walk away with, each mapped to modules in the curriculum below.
Develop and deploy AI models using Python, TensorFlow, and PyTorch.
Master data manipulation, visualization, and preprocessing techniques with Pandas and NumPy.
Implement machine learning algorithms and evaluate their performance using Scikit-Learn.
Build and optimize deep learning models for computer vision and natural language processing tasks.
Apply MLOps practices for model deployment, monitoring, and maintenance in production environments.
AI In Python course syllabus
01 Module 1: Getting Started with AI & Your Environment
- What is Artificial Intelligence?
- Differentiating AI, ML, Deep Learning, and Data Science
- Getting Started with AI and ML for Developers
- From Zero to Hero: The Machine Learning Paradigm
- Spotting and Solving Everyday Problems with ML
- Navigating the Data Science Landscape
- Introduction and History of the CRISP-DM Framework
- A Deep Dive into the Six Phases of CRISP-DM
- Adoption Challenges, Future Directions, and Q&A
- Setting Up Your AI Workbench with Anaconda
- Setting Up Your Python Environment with Jupyter and Anaconda
- Thinking in Python: Essential Concepts for Data Science
- Managing Your Workspace: Virtual Environments and Dependencies
- Unlocking the Power of Jupyter for Interactive AI Development
- Getting Started with Google Colaboratory
- Boosting Productivity with Advanced Colab Features
- Setting Up TensorFlow in Google Colab
- Debugging TensorFlow Projects with PyCharm
- Discovering Pre-trained Models with Kaggle
- Leveraging TensorFlow Hub for Real-World Impact
- Introduction to Unit Testing for Data Scientists
02 Module 2: Python Programming for AI
- Learning Python Fundamentals with ChatGPT
- Setting Up Your Environment and First Program
- Working with Operators and User Input
- Implementing Conditional Logic with If, Elif, and Else
- Automating Repetitive Tasks with For and While Loops
- Organizing Data with Lists, Tuples, and Dictionaries
- Writing Reusable Code with Functions
- Creating Flexible Functions with Optional Parameters
- Fundamentals of Objects and Classes in Python
- Practical Application: Building Interacting Classes
- Inheritance and Method Overriding
- Advanced Concepts: Class Attributes and Static Methods
- Handling Errors Gracefully with Try-Except
- Project: Building a Tic-Tac-Toe Game with AI (Part 1)
- Project: Completing the Tic-Tac-Toe AI Logic
03 Module 3: Mathematics for Machine Learning
- A Friendly Introduction to Linear Algebra for ML
- Understanding Vectors
- Visualizing Matrices as Linear Transformations
- Matrix Multiplication as Composition of Transformations
- Understanding Eigenvectors and Eigenvalues
- The Essence of Calculus
- The Paradox of the Derivative
- Visualizing the Chain Rule for Backpropagation
- Bayes Theorem Explained Geometrically
- The Central Limit Theorem: From Randomness to Bell Curves
- Applying the Gaussian Formula and Its Assumptions
04 Module 4: Data Wrangling & Visualization
- Introduction to NumPy and Array Creation
- Indexing, Slicing, and Array Operations
- Advanced NumPy Concepts and Practical Applications
- Understanding the NumPy Approach to Computation
- Getting Started: Pandas Setup, DataFrames, and Exploration
- Data Manipulation, Filtering, and Analysis in Pandas
- Aggregating and Grouping Data with Pandas GroupBy
- Combining Datasets by Joining and Merging
- Handling Missing and Incomplete Data
- Getting Started with Data Analysis using Pandas and NumPy
- Tackling Dirty Data: Practical Data Cleaning Techniques
- Making Pandas Fly: Optimizing for Speed and Memory
- Comprehensive Introduction to Data Manipulation with Pandas
- Introduction to EDA and Initial Data Exploration
- Data Preparation and Cleaning Techniques
- Visualizing Data with Matplotlib and Seaborn
- Getting Started with Matplotlib: Line and Scatter Plots
- Advanced Matplotlib: Bar Charts, Histograms, and Customization
- Visualizing Data Correlation
- Mastering Data Visualization for Exploration
- Web Scraping for AI Data Collection with Python
- Advanced Web Scraping with Selenium for Dynamic Content
05 Module 5: Classical Machine Learning with Scikit-Learn
- Introduction to Machine Learning Concepts
- Understanding Supervised vs. Unsupervised Learning
- A Crash Course in Supervised and Unsupervised Learning
- Understanding the Bias-Variance Tradeoff
- Solving Problems with Hill Climbing Search
- Finding the Optimal Path with A* Search Algorithm
- Predicting Continuous Values with Linear Regression
- Training and Testing Your First Regression Model
- Implementing Your First Model: Linear Regression in Python
- Winning with Simplicity: Implementing Linear Models
- Foundations of Logistic Regression: Theory and Mathematics
- Practical Guide: Implementing Logistic Regression in Python
- Classifying Data with K-Nearest Neighbors
- Implementing a KNN Classifier for a Real-World Dataset
- Implementing Decision Trees for Classification
- Mastering Gradient Boosting for High-Performance Models
- Discovering Patterns with Unsupervised Learning: Clustering
- Grouping Data with the K-Means Clustering Algorithm
- Finding Patterns: An Introduction to K-Means Clustering
- Evaluating Model Performance: Accuracy, Precision, and Recall
- Advanced Evaluation with ROC and AUC Curves
- Evaluating Model Performance: A Guide for Humans
- Evaluating Model Performance with a Confusion Matrix
- Validating Models with K-Fold Cross-Validation
- Introduction to Ensemble Methods: Random Forest
06 Module 6: Advanced ML: Ensembles, Boosting & Unsupervised
- Understanding Ensemble Methods: Bagging vs. Boosting
- Theoretical Foundations of Random Forests and Decision Trees
- Python Implementation: Data Preparation for Iris Flower Analysis
- Training, Predicting, and Evaluating the Random Forest Model
- Understanding and Implementing Random Forests
- Implementing Gradient Boosted Regression Trees with Scikit-learn
- Steering ML with Visual Diagnostics using Yellowbrick
- Handling Imbalanced Datasets in Machine Learning
- Understanding the K-Nearest Neighbors Algorithm
- Building a KNN Classifier: Diabetes Prediction Use Case
- Introduction to Unsupervised Learning and Hierarchical Clustering
- Applying K-Means Clustering and the Elbow Method
- Implementing K-Means Clustering in Python
- Optimizing Model Performance with PyTorch 2.0 Techniques
- Building a Song Recommender System from Scratch
- Time Series Forecasting with Statistical and ML Models
07 Module 7: Deep Learning Foundations
- A Comprehensive Introduction to Deep Learning
- The Intuition Behind Neural Networks
- How Neural Networks Learn: Cost Functions and Gradient Descent
- Analyzing Network Performance and Advanced Concepts
- Understanding Backpropagation Intuitively
- Building a Neural Network from Scratch: The Neuron
- Understanding Layers and the Dot Product
- Conceptual Overview of Neural Networks
- Building Your First Neural Network with TensorFlow and Keras
- A Gentle Introduction to Building Neural Networks in Python
- Getting Started with TensorFlow 2.0 for Deep Learning
- Building Your First Neural Network with Keras Sequential API
- Optimizing Models with TensorBoard Visualization
- Exploring Python Libraries for Deep Learning with Sequences
- Image Classification with Convolutional Neural Networks
- Processing Sequential Data with Recurrent Neural Networks
08 Module 8: TensorFlow & Keras In-Depth
- Understanding TensorFlow from the Ground Up
- Introduction to TF2 and Keras API Fundamentals
- Advanced Model Building and Custom Training Loops
- The TensorFlow Ecosystem: Data, Distribution, and Deployment
- The Core Keras Layer Abstraction
- Composing Models with Subclassing and the Functional API
- Advanced Functional API: Internals and Special Features
- Applying Modern Keras Design Patterns
- Building and Refining Your First Keras Model
- Project: Build a Neural Network in 4 Minutes
- Building Your First Convolutional Neural Network
- Introduction to Deep Learning with PyTorch
- Preparing and Loading Data in PyTorch
09 Module 9: Data Engineering for AI
- Overview of Loading Data with High-Level APIs
- Loading and Preparing CSV Data for Keras
- Preparing Text Data for Classification
- Simplifying Data Processing with Keras Preprocessing Layers
- Deep Dive into Keras Preprocessing Layers
- Loading and Preprocessing Video Data
- Building Performant Input Pipelines with tf.data
10 Module 10: Computer Vision with Deep Learning
- Introduction to Convolutional Neural Networks
- Fundamentals of Computer Vision with ML
- Project: Classifying Clothing with Fashion MNIST
- Project: Building a Rock, Paper, Scissors Classifier
- Workshop: Customizing Pre-trained Models for Your Data
- Project: Training a Custom Object Detection Model
- Leveraging KerasCV for Advanced Image Classification
- Improving Models with Data Augmentation in KerasCV
- Workshop: Anomaly Detection with Autoencoders
- Understanding Convolutions: Definitions and Applications
- The Fast Convolution Algorithm via Polynomials and FFT
- Building an Image Classifier with CNNs
- Applying Computer Vision: Building a Sign-to-Speech Prototype
- Project: Build a Real-Time Gesture Recognition System
- Introduction to GANs and Stable Diffusion
- Project: Generating Images with Stable Diffusion and KerasCV
11 Module 11: NLP & Text Processing
- Step 1: Text Tokenization
- Step 2: Creating Sequences from Sentences
- Text Preprocessing: Tokenization and Stop Words
- Normalizing Text with Stemming and Lemmatization
- Representing Text as Data: Bag-of-Words
- Advanced Text Representation with TF-IDF
- Introduction to Sentiment Analysis and Evaluating TextBlob
- Evaluating Vader Sentiment and Comparative Analysis
- Project: Building a Sentiment Analysis Model
- The NLP Landscape: Trends, Challenges, and Opportunities
- Building Practical NLP Solutions with spaCy and Prodigy
- Introduction to Text Mining and Pre-processing
- Demo: Processing the Brown Corpus with NLTK
- Advanced Sentence Structuring and Analysis
- Mastering Modern Natural Language Processing in Python
- Applying Word Embeddings with Gensim
- Project Intro: Building a Conversational AI Chatbot
- Structuring the Chatbot's Data
- Creating the Chatbot Training Database
- Training the Chatbot's Neural Network Model
- Bringing Your Chatbot to Life: Interaction and Deployment
12 Module 12: Modern LLMs & Generative AI
- Introduction to Generative AI
- Introduction to Large Language Models
- Understanding Large Language Models
- Foundations of Prompt Engineering
- Core Prompting Techniques and Methods
- Advanced Strategies and Best Practices
- Fundamentals of Prompt Engineering
- Crafting the Perfect Prompt for ChatGPT
- Interacting with LLMs via the OpenAI API
- Advanced AI Interaction with Function Calling
- Exploring and Using Google Gemini
- Working with Claude API and Anthropic AI Services
- Running Large Language Models Locally with Ollama
- Understanding Transfer Learning and Transformer Models
- Introduction to Transformers and Deep Learning Fundamentals
- Embeddings, Predictions, and the Softmax Function
- Self-Attention Mechanism: Queries, Keys, and Attention Patterns
- Applying Attention: Values, Multi-Head Attention, and Scaling
- Working with Hugging Face Models and Inference Providers
- Applied ML with KerasCV and KerasNLP
- Practical Text Classification with KerasNLP
- Project: Training an AI to Generate Poetry
- Building Semantic Search with Vector Databases
- Introduction to LangChain for Building LLM Applications
- Project: Build a Conversational Chatbot with LangChain
13 Module 13: PyTorch for Deep Learning
- Introduction to PyTorch: Tensors and Autograd
- Building, Training, and Deploying a PyTorch Model
- Mastering PyTorch Tensors
- Understanding Automatic Differentiation with Autograd
- Building Your First Model with PyTorch nn.Module
- Getting Started with PyTorch for Deep Learning
- Modeling Sequences with Recurrent Neural Networks in PyTorch
- Introduction to PyTorch 2.0 and the torch.compile API
- The New Developer Experience in PyTorch 2.0
14 Module 14: Distributed & Accelerated Training
- Introduction to Synchronous Data Parallelism
- Asynchronous Parallelism and Strategy Comparison
- Deep Dive into TensorFlow's tf.distribute.Strategy
- Demo: Distributed Training with MirroredStrategy on Google Cloud
- Asynchronous Training with Parameter Servers
- Introduction to XLA and JIT Compilation in TensorFlow
- XLA Limitations, Debugging, and Performance Benchmarks
- Leveraging GPUs and TPUs for Your ML Project
- Understanding Distributed Data Parallel in PyTorch
- Code Walkthrough: Multi-GPU Training with PyTorch DDP
15 Module 15: MLOps Fundamentals
- A Hitchhiker's Guide to the MLOps Lifecycle
- Saving and Loading Your Trained Models
- An Introduction to MLOps with TensorFlow Extended
- What is TFX and Why Do You Need It?
- Understanding TFX Pipelines and Components
- Overview of TensorFlow Serving for Production
- Making Predictions with TensorFlow Serving Clients
- Optimizing TensorFlow Serving Performance
- Deploying PyTorch Models with TorchServe
- LLM Evaluation Methods and Automated Testing
- Production LLM Monitoring with Tracing and Observability
- Versioning Your Models and Data with DVC
- Creating Interactive AI Dashboards with Streamlit
- Scaling Your Workloads with Dask
- Writing Clean and Maintainable Machine Learning Code
- Creating a Reproducible ML Pipeline with Scikit-Learn
16 Module 16: Advanced Deployment & Cloud
- Deploying Models as APIs with FastAPI
- Building an API for Your AI Model with FastAPI
- Containerizing AI Applications with Docker
- Containerizing AI Applications with Docker for Deployment
- Orchestrating Data Pipelines with Apache Airflow
- Deploying Models as Serverless APIs with AWS Lambda
- Deploying Your AI Application to Heroku
- Deploying Machine Learning Models on AWS EC2
- Building the Q&A Ingest Workflow
- Building and Deploying the AI Agent
- Steps 1 and 2: Discovering and Prioritizing AI Opportunities
- Steps 3 to 5: Mapping Processes and Building Prototypes
- Steps 6 and 7: Safeguarding, Launching, and Managing Expectations
17 Module 17: Responsible AI, Ethics & Explainability
- A Framework for Building Responsible AI Systems
- The Role of Responsible AI in Human-Centered Technology
- Ensuring Fairness: Methods to Create Unbiased Models
- Implementing Responsible AI and Fairness in Machine Learning
- Building with the Responsible AI Toolkit
- Practical Model Analysis with the Responsible AI Toolkit
- Opening the Black Box: An Introduction to Explainable AI
- Principles of Explainable AI and a Taxonomy of Methods
- Deep Dive: Understanding and Implementing Integrated Gradients
- Applications, Limitations, and the Future of Explainable AI
- Opening the Black Box with LIME and SHAP
- Keynote: The New Science of Cause and Effect
- Safe and Responsible Development with Generative Models
- Case Study: Building a Human-in-the-Loop Anomaly Detection System
18 Module 18: Capstone Projects & Career
- Project: Building a Movie Recommender System with Python
- Project: Stock Price Prediction Using Machine Learning
- Capstone Project: End-to-End Car Brand Classifier
- Your Career Path: The AI Engineer Roadmap for 2026
- Preparing for Interviews: Top Machine Learning Questions
- Exploring the Future of Artificial Intelligence
Real-world projects you'll work on
Practice the exact kind of work AI In Python professionals do on the job.
Building a Personalized Shopping Assistant for Amazon Using AI and Python
Developing a Real-Time Fraud Detection System for PayPal Transactions
Creating a Predictive Maintenance Model for Tesla's Electric Vehicles
Designing a Smart Healthcare Chatbot for IBM Watson Health
Skills you'll gain
Add these to your LinkedIn profile and resume the day you finish.
Who should take this AI In Python course?
And exactly what you need before starting (spoiler: almost nothing).
This course is for
- Software developers in India wanting to transition into AI engineering roles.
- Data analysts looking to expand their skill set with machine learning and deep learning.
- Computer science students needing practical, project-based experience with Python AI tools.
- Tech professionals aiming to understand modern LLMs and generative AI for business applications.
Prerequisites
- Basic familiarity with programming concepts is recommended before starting.
- A working computer with internet access to write and execute Python code.
- High school level understanding of mathematics and statistics helps with the machine learning modules.
How learning works on LearnTube
The structure of a bootcamp, the price of free.
AI curates your path
Our AI picks the top 1% of video content for each topic and arranges it into a structured, goal-based curriculum. No more tutorial roulette.
Learn, then get quizzed
After every lesson, your AI tutor asks you a question and gives personalised feedback on your answer, so gaps get fixed immediately.
Finish with proof
Complete the course, build the capstone project, and claim a certificate recognised by 900+ hiring partners.
A complete AI In Python course: free, structured and job-focused
This free python for artificial intelligence course lets you learn at your own pace, offering an optional paid certificate when you finish. LearnTube makes studying simple. We pair top YouTube video lessons with an AI tutor that gives you a quick quiz after every single lesson. You'll never get bored, and you can test your knowledge instantly as you progress through 276 video lessons.
The curriculum spans 105 hours across 18 modules, starting with a clear explanation of What is Artificial Intelligence?. You will progress from basic Python Programming for AI into Mathematics for Machine Learning and Data Wrangling & Visualization. From there, you'll tackle Classical Machine Learning with Scikit-Learn, Deep Learning Foundations, and Modern LLMs & Generative AI. We also cover essential production skills in the Data Engineering for AI, MLOps Fundamentals, and Advanced Deployment & Cloud modules. You will even study Responsible AI, Ethics & Explainability.
You won't just watch videos. You will complete four hands-on projects to prove your skills. These include Building a Personalized Shopping Assistant for Amazon Using AI and Python, Developing a Real-Time Fraud Detection System for PayPal Transactions, Creating a Predictive Maintenance Model for Tesla's Electric Vehicles, and Designing a Smart Healthcare Chatbot for IBM Watson Health. By the end, you will be ready to build and optimize deep learning models for computer vision and natural language processing tasks.
Jobs you can target after learning AI In Python
AI In Python is a gateway skill for some of the fastest-growing roles in tech.
Designs, builds, and deploys machine learning models and AI applications.
Creates algorithms that allow computers to learn from and make predictions on data.
Analyzes complex datasets to extract patterns and build predictive models for business decisions.
Develops systems that understand and process human language using text data.
Typical entry-level salary ranges in India as of August 2026. Actual compensation varies by city, company and experience.
Earn a AI In Python certificate employers recognise
Finish the course and claim a certificate you can share anywhere.
- Learn 100% free: every lesson, quiz and the AI tutor cost nothing
- Claim your certificate when you complete the course (an optional paid add-on)
- Recognised by 900+ hiring partners across India
- One click to share on LinkedIn and add to your resume
Why learners choose LearnTube
Real reviews from Google. You learn free, you get assessed free, and you pay only if you want the certificate.
Though I have 11 years of experience as an HRBP, the course helped me refresh my knowledge and gave me insights into new trends in the market. The modules were clear and easy to understand, and the quizzes were well designed. I have added the certificate to my LinkedIn profile.
I had a lot of doubts while learning, but the AI chatbot answered them in detail with simple summaries. The videos on the platform saved me a lot of time as I didn't have to search YouTube and watch random content.
Just earned my certificate in HR Management from LearnTube.ai! The platform is user-friendly and the assessment was a great way to validate my knowledge. Feeling proud of this accomplishment!
I enrolled in a LearnTube personalized course to get into a Data Analytics role. It helped me learn SQL in depth and gave me hands-on experience with Power BI. I attached the LearnTube certificate to my CV, it added credibility to my profile, and I'm currently in the interview process.
The interface is user-friendly and intuitive, making it easy to navigate the course. I encountered no errors during my time using the platform, which was a refreshing change from other online learning platforms.
Just cleared the Business Analyst assessment and got certified! Loved how the course simplifies big concepts like data-driven decision making into short, practical lessons. The automated quizzes kept me on track the whole time. Perfect boost for my resume!
The certificate gave me a real edge during my B.Tech. By the time I graduated, I had skills that set me apart from my peers, and the certification opened up more job opportunities. It gave me the confidence to stand out during interviews.
I manage many software projects as a General Manager and was looking for a good certification program to stay competitive. After researching many programs, I chose LearnTube and it was much better than I expected. The material is well-organized and practical, and what I liked most is the lifetime access.
Frequently asked questions
Everything learners ask before starting this AI In Python course.
What jobs can I get after this python ai course?
You can pursue roles like AI Engineer or Data Scientist. Entry-level salaries in India typically range from ₹5 to ₹12 LPA depending on your specific role and the company hiring you.
Can I take this ai in python course on my phone?
Yes. The LearnTube platform is fully mobile-friendly. You can watch the curated video lessons and complete the AI tutor quizzes directly from your smartphone, making it easy to study anywhere.
How long does it take to learn artificial intelligence with python?
This specific curriculum contains about 105 hours of video content across 18 modules. Most learners finish it in three to four months by dedicating a few hours of study time each week.
What will I actually build during this python ai training?
You will complete four major projects. These include a personalized Amazon shopping assistant, a real-time PayPal fraud detection system, a predictive maintenance model for Tesla vehicles, and an IBM Watson healthcare chatbot.
Is this a python ai course free with certificate?
The entire learning experience is completely free, including all 276 video lessons and quizzes. If you want a formal document to verify your new skills, the certificate is available as an optional paid add-on.
Will I learn deep learning frameworks like PyTorch?
Yes. The curriculum includes dedicated modules for both TensorFlow and PyTorch. You'll learn how to build and optimize neural networks for computer vision and text processing tasks using these industry-standard tools.
Do I need to know math to start this python ai bootcamp?
Basic math knowledge is helpful. Module 3 specifically covers Mathematics for Machine Learning, so you will review the essential linear algebra and calculus concepts needed to understand how these algorithms actually work.
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