9 Best Resources to Learn Machine Learning (from a FAANG Interview Journey)
Source: Dev.to
When I started learning machine learning (ML), I was overwhelmed. The field felt like a vast ocean — dense with math, theory, frameworks, and best practices. I remember struggling to connect abstract algorithms with real‑world applications. Over the years, through trial, error, and countless projects, I curated a set of resources that transformed my understanding.
Whether you’re a beginner or prepping for ML system‑design interviews at FAANG companies, these nine resources helped me level up. This isn’t just a list — it’s a story about how each resource made a tangible difference in my learning path.
1. Andrew Ng’s Machine Learning Course
Why it works – Simplifies complex math with intuitive explanations.
Content – Covers fundamentals such as supervised/unsupervised learning, linear regression, and neural networks.
Pro tip – Complement the videos with coding exercises in Python or Octave to solidify concepts.
Immediate takeaway – Understanding foundational ML algorithms early builds confidence for advanced topics.
2. Hands‑On Machine Learning Book (Aurélien Géron)
Why it works – Bridges the gap between concepts and practical implementation.
Content – Explores classical algorithms and deep learning, with code examples in Python.
Real‑world application – My side project to predict housing prices started here.
Lesson – Hands‑on coding makes ML concrete; don’t just read, build projects.
3. “Become a Machine Learning Engineer” (FAANG interview‑focused course)
Why it works – Targets the ML concepts most asked in interviews.
Content – System design for ML pipelines, popular algorithms, and coding challenges.
Bonus – Interactive text‑based lessons ideal for on‑the‑go learning.
System‑design insight – Learn how to architect scalable ML systems while balancing latency and accuracy trade‑offs.
4. Deep Learning with PyTorch (coding‑first approach)
Why it works – Focuses on a coding‑first approach using PyTorch, minimizing heavy math.
Content – Covers image recognition, NLP, and tabular data.
Community – Active forums for peer mentoring and discussion.
Pro tip – Experiment with notebooks on free GPUs via Google Colab.
5. Google Machine Learning Crash Course
Why it works – Combines theory, code, and interactive visualizations with no fluff.
Content – Covers data pipelines, training, evaluation, and TensorFlow basics.
Takeaway – Understanding Google’s ML tooling and best practices is invaluable for production ML.
6. ML System‑Design YouTube Channel
Why it works – Breaks down system‑design concepts with approachable visuals.
Content – Videos on ML pipelines, data versioning, and model monitoring.
Use case – Helped me design scalable recommendation‑engine architectures.
Visual callout – Diagramming your solution before coding simplifies complexity.
7. Kaggle (datasets & competitions)
Why it works – Offers thousands of datasets and competitions to practice ML skills.
Content – Community‑shared kernels (notebooks) showcasing diverse techniques.
Motivation – Competing builds grit, a crucial trait for ML engineers.
Pro tip – Start by replicating top solutions, then innovate.
8. ML System‑Design Interview Course (e.g., Educative / DesignGurus.io)
Why it works – Provides step‑by‑step walkthroughs of high‑level design problems.
Content – Distributed training, feature stores, data‑labeling workflows.
Interview insight – Helps anticipate design trade‑offs between scalability, latency, and cost.
9. “Pattern Recognition and Machine Learning” by Christopher Bishop
Why it works – Offers a thorough statistical perspective on machine learning.
Content – Covers graphical models, Bayesian networks, and kernel methods.
When to use – Ideal after grasping the basics; perfect for researchers or advanced engineers.
Framework – Decompose complex problems into probabilistic models for clearer intuition.
My Learning Journey (in brief)
- Started with Andrew Ng’s course for fundamentals.
- Built projects using Géron’s hands‑on book.
- Prepped interviews with Become a Machine Learning Engineer and ML System‑Design Interview Course.
- Expanded system‑design skills via the ML System‑Design YouTube Channel.
- Practiced coding and experiments on Kaggle.
- Adopted Google’s crash course best practices for production.
- Dived into theory with Bishop’s book once comfortable.
Machine learning isn’t just about memorizing algorithms; it’s a craft forged by:
- Building projects
- Understanding system trade‑offs
- Continuous iteration and learning
If I could tell my past self anything, it would be: Stay curious, embrace failure, and lean into practice. You’ll be surprised how quickly you connect the dots.
You’re closer to mastering machine learning than you think. Use these resources as your compass, but write your own story through action. 🚀