AI Course Syllabus 2026: 20-Week Intensive Curriculum with 8 Projects

Artificial Intelligence is no longer futuristic—it’s reshaping every industry right now. If you’ve watched ChatGPT, Gemini, and Claude evolve in 2024-2025, you’ve seen AI capability explode exponentially. But understanding AI beyond the surface requires structured learning. Random YouTube videos won’t cut it. You need a comprehensive roadmap: from “What is AI?” to “I can build AI applications.”

This is a complete 20-week intensive AI course syllabus designed for 2026 learners. It takes you from absolute beginner to AI-capable professional. Whether you want to understand AI for your job, build AI applications, or become an AI engineer, this curriculum covers it.

Best part? Most resources are free or under $50/month. You don’t need expensive bootcamps—just 25-35 hours/week and this roadmap.

Related Guides: Learn the 15 Essential Tech Skills to Learn in 2026 for a broader skill context, or explore Branches of Artificial Intelligence to understand which AI specialization fits your career path.

COURSE STRUCTURE: 4 Phases, 20 Weeks, 8 Projects

Phase Weeks Duration Focus Projects
Phase 1: Foundations 1-4 4 weeks Python, Math, ML Basics 2
Phase 2: Core AI & ML 5-10 6 weeks Algorithms, Supervised/Unsupervised, NLP, Vision 3
Phase 3: Deep Learning 11-15 5 weeks Neural Networks, RNNs, Transformers, LLMs 2
Phase 4: Applications 16-20 5 weeks Advanced NLP, Vision, Ethics, Capstone 1
TOTAL 20 weeks Complete AI Education 8 Projects

Time Commitment: 25-35 hours/week (4-5 hours daily) for 5-month intensive completion.

PHASE 1: FOUNDATIONS (WEEKS 1-4)

Build the mathematical and programming base. Everything in AI rests on Python, linear algebra, statistics, and basic ML concepts.

Week 1-2: Python for AI & Data Science (15-20 hours)

Topics: Python basics, NumPy arrays, Pandas DataFrames, data visualization

Best Resources:

  1. freeCodeCamp Python for Data Science (Free, YouTube) – 4-hour comprehensive intro

  2. DataCamp Python Track ($30/month) – Interactive, 15-20 hours

  3. Pandas/NumPy Official Docs (Free) – Reference material

Project #1: Dataset Analysis & Visualization

Load a real Kaggle dataset—clean messy data. Analyze patterns. Create visualizations with Matplotlib/Seaborn. This teaches practical data handling before AI.

Deliverable: Jupyter notebook with cleaned data, analysis, 5+ visualizations

Key Skills Gained:

  • Load/clean/explore datasets

  • Write clean Python code

  • Visualize data patterns

Week 2-3: Mathematics for Machine Learning (15-20 hours)

Topics: Linear algebra (vectors, matrices), calculus (derivatives, gradients), statistics (probability, distributions)

Why This Matters: Neural networks are matrix operations. Gradient descent uses calculus. ML relies on probability.

Best Resources:

  1. 3Blue1Brown Essence of Linear Algebra (Free, YouTube) – Visual, intuitive, 15 videos

  2. StatQuest with Josh Starmer (Free, YouTube) – Statistics explained simply

  3. Mathematics for Machine Learning Book (Free, online) – Reference

Project #1b: Linear Algebra Visualization

Implement matrix operations with NumPy. Visualize vector transformations. Create animations showing matrix multiplication. This makes abstract math concrete.

Deliverable: Jupyter notebook with visualizations, animations, and intuitive explanations

Week 4: Machine Learning Fundamentals (15-20 hours)

Topics: Supervised vs unsupervised learning, training/testing splits, Scikit-Learn, evaluation metrics (accuracy, precision, recall, F1)

Best Resources:

  1. Scikit-Learn Official Tutorials (Free) – 10-15 hours

  2. Kaggle Learn: Intro to ML (Free) – Interactive course

  3. Andrew Ng ML Course Week 1-2 (Coursera, $50) – Mathematical foundations

Project #2: Your First Machine Learning Model

Build a linear regression on a simple dataset. Then logistic regression. Evaluate both. Understand why accuracy alone isn’t enough. This demystifies ML.

Deliverable: Jupyter notebook with two models, evaluation metrics, train/test analysis

Expected Outcome After Phase 1:

  • ✅ Write clean Python code confidently

  • ✅ Load, clean, explore real datasets

  • ✅ Understand linear algebra and calculus conceptually

  • ✅ Built 2 ML models and evaluated them

PHASE 2: CORE AI & MACHINE LEARNING (WEEKS 5-10)

Now dive deep into algorithms, techniques, and real-world applications. This phase covers the skills discussed in our guide to in-demand tech skills (especially Machine Learning Fundamentals and DevOps concepts).

Week 5-6: Supervised Learning Algorithms (20-25 hours)

Topics: Decision trees, random forests, SVM, gradient boosting (XGBoost), feature engineering, hyperparameter tuning

Best Resources:

  1. Hands-On Machine Learning (Free online, $50 book) – 30-40 hours of material

  2. Kaggle Competitions (Free) – Real problems, real data

  3. Fast.ai Practical Deep Learning (Free) – Applied approach

Project #3: Kaggle Competition

Enter a real Kaggle competition (Housing prices, Titanic, or current competition). Build 3-4 models (trees, RF, XGBoost). Compare performance. Learn from top solutions.

Deliverable: Kaggle submission, GiCode repo with code, analysis of approach

Week 7: Unsupervised Learning (15-20 hours)

Topics: Clustering (K-means, DBSCAN), dimensionality reduction (PCA), anomaly detection

Best Resources:

  1. StatQuest Unsupervised Learning (Free, YouTube) – Clear explanations

  2. Scikit-Learn Clustering Guide (Free) – Reference

  3. Hands-On ML Chapters 8-9 (Free online)

Project #3b: Customer Segmentation

Use clustering to segment customers in an e-commerce dataset. Analyze each segment. Find actionable insights. This teaches practical unsupervised learning.

Deliverable: Jupyter notebook with clustering analysis, segment profiles, and business insights

Week 8: Natural Language Processing Basics (15-20 hours)

Topics: Text preprocessing, tokenization, embeddings (Word2Vec, GloVe), sentiment analysis, text classification

Best Resources:

  1. Hugging Face NLP Course (Free) – Excellent, comprehensive

  2. SpaCy Tutorials (Free) – Industry-standard NLP library

  3. Fast.ai NLP Course (Free) – Applied approach

Project #4: Sentiment Analysis

Build a sentiment classifier for movie reviews or tweets. Use TF-IDF or embeddings. Train model. Test on new data. Evaluate accuracy, precision, and recall.

Deliverable: Jupyter notebook with model, evaluation metrics, and example predictions

Week 9: Computer Vision Basics (15-20 hours)

Topics: Images as data, CNNs conceptually, transfer learning, image classification, object detection

Best Resources:

  1. Fast.ai Computer Vision Course (Free) – Top-down learning

  2. Stanford CS231N Lectures (Free, YouTube) – University-level

  3. PyTorch Vision Tutorials (Free) – Practical implementation

Project #5: Image Classification with Transfer Learning

Use a pre-trained ResNet model. Fine-tune on your dataset (cats vs dogs, medical imaging, or custom). Understand why transfer learning is powerful.

Deliverable: Jupyter notebook with trained model, accuracy metrics, and example predictions

Week 10: Advanced Topics & Recommender Systems (15-20 hours)

Topics: Content-based filtering, collaborative filtering, matrix factorization, deep learning for recommendations

Best Resources:

  1. Coursera Recommender Systems (Free audit) – Comprehensive

  2. Fast.ai Collaborative Filtering (Free) – Practical approach

  3. Papers with Code Recommendations (Free) – State-of-the-art

Expected Outcome After Phase 2:

  • ✅ Understand 10+ ML algorithms

  • ✅ Know when to use each algorithm

  • ✅ Feature engineering skills

  • ✅ Built 3+ projects on real data

  • ✅ Competitive experience from Kaggle

PHASE 3: DEEP LEARNING & NEURAL NETWORKS (WEEKS 11-15)

Understand how neural networks actually work—the foundation for ChatGPT and modern AI. For a deeper dive into AI specializations, see our comprehensive guide to AI branches.

Week 11-12: Neural Networks from Scratch (20-25 hours)

Topics: Neurons, forward propagation, backpropagation, activation functions, gradient descent, optimization (SGD, Adam)

Critical Concept: How networks learn through calculus and gradient descent.

Best Resources:

  1. 3Blue1Brown Neural Networks (Free, YouTube) – Genius-level explanations

  2. Fast.ai Deep Learning (Free) – Applied understanding

  3. Andrew Ng Deep Learning Specialization (Coursera, $50) – Mathematical rigor

Project #6: Neural Network from Scratch

Build a neural network using ONLY NumPy. No TensorFlow/PyTorch. Implement:

  • Forward propagation

  • Backpropagation

  • Stochastic gradient descent

  • Train on MNIST digits

This teaches accurate understanding vs framework abstraction.

Deliverable: Python code from scratch, training curves, and accuracy metrics

Week 13: Convolutional & Recurrent Neural Networks (20-25 hours)

Topics: CNNs (convolutions, pooling, architectures), RNNs, LSTMs, attention mechanisms, transformers

Best Resources:

  1. Stanford CS231N (Free, YouTube) – Computer vision deep dive

  2. Fast.ai Course (Free) – Applied CNNs and RNNs

  3. “Attention Is All You Need” Paper (Free, PDF) – Foundational transformer paper

Project #7: LSTM Text Generation

Build an LSTM that generates text character-by-character. Train on Shakespeare, Python code, or news articles. Generate novel text.

Deliverable: Jupyter notebook with LSTM model, generated text samples, and training analysis

Week 14-15: Transformers & Large Language Models (20-25 hours)

Topics: Self-attention, multi-head attention, positional encoding, transformer architecture, GPT vs BERT, how ChatGPT works, fine-tuning

Best Resources:

  1. Hugging Face Course (Free) – Industry standard

  2. Stanford CS224N (Free, YouTube) – University-level NLP

  3. Andrej Karpathy’s Neural Net Zero to Hero (Free, YouTube) – Building GPT from scratch

Project #8: Fine-tune a Language Model

Fine-tune GPT-2, BERT, or Llama on custom data (domain-specific documents, code, or domain knowledge). Use the Hugging Face Transformers library.

Deliverable: Trained model, inference code, example outputs

Expected Outcome After Phase 3:

  • ✅ Understand neural networks mathematically

  • ✅ Know how backpropagation works

  • ✅ Understand CNNs, RNNs, and transformers

  • ✅ Can fine-tune modern language models

  • ✅ Built 2+ deep learning projects

PHASE 4: APPLICATIONS & CAPSTONE (WEEKS 16-20)

Move from learning to building production AI systems.

Week 16: Advanced LLM Applications (20-25 hours)

Topics: RAG (Retrieval-Augmented Generation), vector databases, LLM chains, prompt engineering, LLM evaluation, building chatbots

Projects:

  • Build a RAG chatbot (Q&A over documents)

  • Create an LLM agent with planning capabilities

Week 17: Production Computer Vision (20-25 hours)

Topics: Object detection, segmentation, video processing, optimization for real-time inference, edge deployment

Projects:

  • Build real-time object detection

  • Deploy model to edge devices (phones, cameras)

Week 18: AI Safety & Ethics (15-20 hours)

Topics: Bias detection, fairness, transparency, privacy, AI alignment, responsible AI

Expected Outcome: Understand implications of AI systems. Build ethically accountable models.

Week 19-20: Capstone Project (25-30 hours)

Choose One:

  1. Health Tech: Disease prediction from medical imaging

  2. Finance: Fraud detection with ensemble models + LSTM

  3. Recommendation: Build a music/movie recommender with collaborative filtering + deep learning

  4. NLP: Domain-specific chatbot with RAG and fine-tuning

  5. Computer Vision: Object detection and tracking system

Deliverables:

  • ✅ Clean GitHub repository

  • ✅ Jupyter notebooks with full methodology

  • ✅ Deployed model (Hugging Face, Docker, or cloud)

  • ✅ Medium article explaining approach

  • ✅ Portfolio page showcasing project

TIMELINE OPTIONS

Option Duration Pace Hours/Week Best For
Intensive 5 months 20 weeks 30-35 Career switchers, dedicated learners
Standard 10 months 40 weeks 15-20 Working professionals
Flexible Self-paced No deadline 10-15 Casual learners

Recommendation: Start with intensive or standard. AI moves fast—structured learning beats random exploration. Want to learn multiple skills in 2026? Check our 15 Essential Tech Skills guide for comparison.

RESOURCE BUDGET: Free vs Paid

Completely Free:

  • 3Blue1Brown ($0)

  • Fast.ai ($0)

  • Kaggle ($0)

  • Hugging Face ($Codeapers with Code ($0)

  • Stanford/MIT YouTube ($0)

  • GitHub open source ($0)

Affordable Paid:

  • Coursera ($50-200)

  • DataCamp ($30/month)

  • Udemy courses ($15 each)

  • Cloud compute ($0 free tier, then $10-50/month)

Total Budget: $0- $ 500 for 20 weeks (mostly free, with optional paid options).

CRITICAL SUCCESS FACTORS

1. Code Every Day
Don’t just watch. Build. Get stuck. Debug. Real learning happens through doing.

2. Join Communities
Reddit (r/MachineLearning, r/learnmachinelearning), Discord, Kaggle. Ask questions. Help others.

3. Build Projects
A single Kaggle competition teaches more than 10 lectures. Build real things.

4. Read Papers
Read conclusions/introductions of famous papers: “Attention Is All You Need,” BERT, GPT papers. Understand cutting-edge research.

5. Share Your Progress
Blog/Twitter/GitHub. Document what you’re learning. This clarifies thinking and attracts opportunities.

6. Stay Updated
Subscribe to: The Batch (Andrew Ng), Import AI, AI Alchemist. AI evolves monthly.

FAQ

Q1: Do I need a CS degree?
A: No. This curriculum teaches everything needed. Some math background is helpful but not required.

Q2: Can I skip weeks?
A: Not recommended. Each phase builds on previous ones. Skip only if you already know the content.

Q3: What hardware do I need?
A: Laptop with 8GB+ RAM. GPU helpful (Google Colab free GPUs). Most can run on a CPU.

Q4: Will I be job-ready upon completion?
A: Yes. With a capstone project and portfolio, you’re competitive for junior ML engineer or data scientist roles.

Q5: How is this different from other AI courses?
A: Most courses teach isolated topics. This is a comprehensive, structured framework: foundations → algorithms → deep learning → production applications.—acomplete education.

Q6: What about AI moving so fast?
A: This curriculum teaches fundamentals (math, algorithms, neural networks) that don’t change. Specific frameworks change, but core concepts last.

NEXT STEPS: Start Phase 1

This Week:

  1. Pick your pace (intensive, standard, or flexible)

  2. Install Python and Jupyter

  3. Start Week 1: Python + NumPy

  4. Join r/learnmachinelearning or the scord community

By Week 4: Finish foundations. Have two projects. Understand ML fundamentals.

By Week 20: Complete capstone. Have a production-ready portfolio. Be job-ready for AI roles.

AI is reshaping the world. This curriculum gives you the skills to shape it. Start today.

RELATED RESOURCES

Learning Paths:

Getting Started: