Course Information
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Course Overview
Master Python, Math, ML, Deep Learning, NLP, Agents & MLOps in 156 classes designed to take you from beginner to AI Hero
The AI Hero Program is the most comprehensive 12-month AI course designed to take you from absolute beginner to AI expert through a structured, project-based learning path. With 156 AI classes, you’ll gain hands-on experience in Python programming, mathematics for AI, machine learning (ML), deep learning (DL), natural language processing (NLP), AI agents, reinforcement learning (RL), MLOps, and cloud deployment.
This isn’t just another crash course — it’s a complete AI curriculum that builds your skills week by week, ensuring mastery of both theory and practice.
What You’ll Learn
In the first quarter, you’ll build strong foundations in Python for AI, linear algebra, statistics, and data handling using NumPy, Pandas, and Matplotlib. These are essential skills every aspiring AI engineer needs to work with real-world datasets.
Next, we dive into machine learning algorithms such as linear regression, logistic regression, k-nearest neighbors (k-NN), decision trees, random forests, and support vector machines (SVM). You’ll learn how to apply these models to solve classification, regression, and clustering problems while mastering concepts like bias-variance tradeoff, hyperparameter tuning, and model evaluation.
By the third quarter, you’ll step into the world of deep learning with TensorFlow and PyTorch. You’ll explore neural networks, convolutional neural networks (CNNs) for computer vision, recurrent neural networks (RNNs) for sequential data, and transformers for advanced NLP applications. This module includes projects such as image recognition, chatbots, and sentiment analysis, preparing you to build industry-ready solutions.
Then, the focus shifts to generative AI, where you’ll experiment with autoencoders, variational autoencoders (VAEs), GANs (Generative Adversarial Networks), and diffusion models like Stable Diffusion. These cutting-edge techniques power today’s most exciting applications, from AI art to synthetic data generation.
In the final quarter, you’ll advance into AI agents and reinforcement learning. You’ll design Q-learning models, implement deep Q-networks (DQNs), and experiment with policy gradient methods like PPO. You’ll also explore tools like LangChain and AutoGPT to build intelligent AI agent systems capable of memory, planning, and reasoning.
Finally, you’ll master MLOps by learning model deployment with Flask and FastAPI, containerization with Docker, and cloud AI deployment on AWS, GCP, and Azure. The program concludes with a capstone project, where you’ll design, build, and present a complete end-to-end AI solution ready for your portfolio.
Why This Program?
- 156 AI classes structured over 12 months
- Hands-on projects in ML, DL, NLP, Generative AI, and RL
- Focus on real-world datasets and applications
- Covers both technical skills and business impact
- Portfolio-ready capstone project
Whether you’re a student starting from zero, a professional making a career transition, or an entrepreneur wanting to apply AI to business, this program gives you everything you need to become an AI Hero.
Join the AI Hero: 12-Month Journey from Zero to Expert today and transform your future with artificial intelligence, machine learning, and AI engineering skills that are in demand worldwide.
Course Content
- 53 section(s)
- 313 lecture(s)
- Section 1 Introduction to AI Hero: A 12-Month Journey Taking You from Zero to Expert
- Section 2 Week 1: Getting Started with Python & AI
- Section 3 Week 2: Mastering Python Data Structures
- Section 4 Week 3: Control Flow, Loops, and Functions
- Section 5 Week 4: File Handling, Modules & NumPy Basics
- Section 6 Week 5: Linear Algebra Foundations for AI
- Section 7 Week 6: Probability & Statistics for AI
- Section 8 Week 7: Calculus and Optimization for AI
- Section 9 Week 8: Applying Math with Python
- Section 10 Week 9: Data Handling with Pandas
- Section 11 Week 10: Data Visualization with Python
- Section 12 Week 11: Working with External Data Sources
- Section 13 Week 12: Exploratory Data Analysis & Features
- Section 14 Week 13: Data Cleaning & Analysis Project
- Section 15 Week 14: Introduction to Machine Learning
- Section 16 Week 15: Regression Models for Prediction
- Section 17 Week 16: Classification & k-NN Algorithms
- Section 18 Week 17: Naive Bayes & Model Evaluation
- Section 19 Week 18: Decision Trees & Ensembles
- Section 20 Week 19: Boosting & Support Vector Machines
- Section 21 Week 20: Clustering Algorithms & Insights
- Section 22 Week 21: Dimensionality Reduction & Segmentation
- Section 23 Week 22: Cross-Validation & Hyperparameter Tuning
- Section 24 Week 23: Overfitting, Scaling & Feature Selection
- Section 25 Week 24: ML Pipelines & Reusable Workflows
- Section 26 Week 25: Model Interpretability & Evaluation
- Section 27 Week 26: End-to-End ML Pipeline Project
- Section 28 Week 27: Fundamentals of Neural Networks
- Section 29 Week 28: Optimizers, Loss Functions & TensorFlow
- Section 30 Week 29: Building Neural Networks with PyTorch
- Section 31 Week 30: MNIST Handwritten Digit Project
- Section 32 Week 31: Introduction to CNNs
- Section 33 Week 32: Modern CNN Architectures & Transfer Learning
- Section 34 Week 33: Recurrent Neural Networks & LSTMs
- Section 35 Week 34: Transformers & Self-Attention
- Section 36 Week 35: Autoencoders & VAEs
- Section 37 Week 36: GANs: Generative Adversarial Networks
- Section 38 Week 37: Advanced GAN Architectures
- Section 39 Week 38: Diffusion Models & Stable Diffusion
- Section 40 Week 39: Generative AI Project Showcase
- Section 41 Week 40: Word Embeddings & Transformer Basics
- Section 42 Week 41: Fine-Tuning Models & Sentiment Analysis
- Section 43 Week 42: NER, Summarization & Translation
- Section 44 Week 43: NLP Project: Building a Chatbot
- Section 45 Week 44: Reinforcement Learning Foundations
- Section 46 Week 45: Deep RL with DQN & Policy Gradients
- Section 47 Week 46: Multi-Agent Systems & AI Agents
- Section 48 Week 47: Reinforcement Learning Project Showcase
- Section 49 Week 48: Introduction to MLOps & Deployment
- Section 50 Week 49: Deploying ML Models in the Cloud
- Section 51 Week 50: Scaling, CI/CD & AI Ethics
- Section 52 Week 51: Capstone Project: Training & Deployment
- Section 53 Week 52: Capstone Demo & Graduation
What You’ll Learn
- Understand Python programming fundamentals for AI applications.
- Apply math foundations: linear algebra, calculus, probability, statistics.
- Work with data using Pandas, NumPy, and visualization libraries.
- Build and evaluate core machine learning models for prediction and analysis.
- Apply deep learning with TensorFlow and PyTorch to vision and text tasks.
- Create generative AI models with GANs, VAEs, and diffusion techniques.
- Develop NLP solutions including embeddings, transformers, and chatbots.
- Implement reinforcement learning agents with Q-learning and PPO.
- Deploy models using Flask, FastAPI, Docker, and cloud platforms.
- Complete an end-to-end AI capstone project for portfolio and career growth.
Skills covered in this course
Reviews
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MMilena Kowalska
difficult to follow
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JJoshua Baridakara John
AWESOME!!! BEST AI COURSE EVER
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AAravind S
Very good but so fast
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BBadr_sleem
Seems to be an AI generated content