Course at a Glance
The most practical deep learning course available. Learn to build state-of-the-art models using fastai and PyTorch. Top-down approach gets you building immediately.
4.8/5
Rating
7 weeks
Duration
Intermediate
Level
Free
Price
Jeremy Howard
Fast.ai
Our Verdict
Fast.ai is the most practical deep learning course available. The top-down approach means you build working models in the first lesson, then gradually learn the theory behind them. Jeremy Howard's teaching style is engaging and no-nonsense. If you want to build real AI applications quickly rather than spending months on theory, Fast.ai is unbeatable. The only downside: the course assumes you can figure some things out on your own, which may frustrate absolute beginners.
Score Breakdown
We completed Fast.ai's Practical Deep Learning for Coders course and built 3 real-world projects using their top-down teaching approach. With 340,000+ students and a cult following in the AI community, Fast.ai is known for getting you building state-of-the-art models from lesson one. Here's our honest take.
Pros & Cons
Pros
- Taught by Jeremy Howard on Fast.ai
- 340K students enrolled
- 4.8/5 star rating from 7K reviews
- 7 weeks of content with 42 lessons
- Free access with no certificate
- Learn Deep Learning
- Learn PyTorch
Cons
- Some prior knowledge recommended
- No formal certificate without payment
- Content may become outdated as AI evolves rapidly
- Self-paced format requires discipline
Start Fast.ai Practical Deep Learning — Free
Free · 7 weeks · Intermediate · 42 lessons
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The Fast.ai Philosophy: Top-Down Learning
Traditional AI courses teach you theory for months before you build anything. Fast.ai flips this: you build a working image classifier in lesson 1, then learn how it works in subsequent lessons. This approach keeps you motivated and gives you immediately useful skills.
- Lesson 1: Build an image classifier with 99% accuracy
- Lesson 2: Understand how the model actually works
- Lesson 3: Data cleaning and model improvement
- Lesson 4: NLP and text classification
- Lesson 5: Tabular data and collaborative filtering
- Lesson 6: Deep dive into neural network foundations
- Lesson 7: Advanced techniques and state-of-the-art models
Start Fast.ai Practical Deep Learning — Free
100% free. All materials open source. Active forum community.
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What You'll Build
By the end of the course, you'll have built a portfolio of real projects including a bear detector, a sentiment analysis tool, a recommendation system, and a custom image classifier. These are genuinely impressive projects you can show to employers.
| Project | What It Does | Skills Learned |
|---|---|---|
| Bear Detector | Classifies images as grizzly, black, or teddy bear | Computer vision, CNNs |
| Sentiment Analyzer | Determines if movie reviews are positive/negative | NLP, text classification |
| Movie Recommender | Suggests movies based on user preferences | Collaborative filtering |
| Custom Classifier | Your own image classification project | Transfer learning, deployment |
Pro Tip
Pro Tip: Fast.ai has one of the most helpful online communities. The forums are incredibly active, and Jeremy Howard himself often answers questions. Don't skip the community aspect — it's a huge part of the learning experience.
Join Fast.ai — Build Real AI Projects Today
340K+ students. Free forever. Open source.
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Watch Lesson 1 — Free
Jeremy Howard's top-down approach means you build a working AI model in the very first lesson. Watch it here before you commit to the full course.
Practical Deep Learning for Coders — Lesson 1 by Jeremy Howard
Watch the full Lesson 1 of Fast.ai's Practical Deep Learning for Coders. Jeremy Howard gets you building a state-of-the-art image classifier in the first session — no theory overload, just real code that works.
Course Curriculum
1Lesson 1: Getting Started4 lessons
- Setting up your environment
- Your first image classifier
- How to use the fastai library
- Deploying your first model
2Lesson 2: Under the Hood4 lessons
- How neural networks work
- Stochastic gradient descent
- The MNIST dataset
- Building a neural network from scratch
3Lesson 3: Data and Validation4 lessons
- Data cleaning and preparation
- Overfitting and validation
- Data augmentation
- Improving your model
4Lessons 4-7: Advanced Topics5 lessons
- Natural language processing
- Tabular data and embeddings
- Collaborative filtering
- Deep learning foundations
- State-of-the-art techniques
Skills You'll Learn
Who Is This Course Best For?
Start Learning Fast.ai Practical Deep Learning — Free
Free · 340K students enrolled · 7 weeks
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