Kicking Off My ML Journey with Aurélien Géron’s Book – Chapter 1: The Machine Learning Landscape

As someone passionate about Machine Learning and its real-world applications, I recently began reading Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron. My aim is to delve deeper into Machine Learning, expand into Deep Learning, and eventually master Reinforcement Learning.

My ML Background So Far:

• Completed the Machine Learning A-Z™ 2025 course.

• Studied Introduction to ML as part of my college curriculum.

• Worked on a YOLOv8-based object detection project (I’ll be posting a separate blog on that soon!).

Now, I'm moving on to more advanced and structured learning using this book — and I’ve just finished Chapter 1: The Machine Learning Landscape.

Chapter 1 Overview: The Big Picture of Machine Learning

This chapter provides a sweeping overview of what Machine Learning is, why it matters, and how it's used in practice. It's perfect for grounding yourself before diving into code.

Key Topics Covered:

What is Machine Learning?

• A program is considered to learn if it improves at some task with experience.

• ML is categorised based on:

  • Whether or not they are trained with human supervision (supervised, unsupervised, semisupervised, and Reinforcement Learning)

  • Whether or not they can learn incrementally on the fly (online versus batch learning)

  • Whether they work by simply comparing new data points to known data points, or instead by detecting patterns in the training data and building a predictive model, much like scientists do (instance-based versus model-based learning

Real-World Applications

• Spam filtering, product recommendations, fraud detection, self-driving cars, and more.

• Underlines how widespread ML is becoming in various industries.

Main ML Techniques Discussed

• Supervised, Unsupervised, Semi-supervised, and Reinforcement Learning

• Batch vs Online Learning

• Visualization Algorithms

• Dimensionality Reduction

• Anomaly Detection vs Novelty Detection

• Instance-Based vs Model-Based Learning

Challenges in ML

• Insufficient data

• Non-representative data

• Irrelevant Features

• Poor-quality data

• Overfitting & underfitting

• Feature engineering and pipeline setup

Evaluating ML Models

• Introduced concepts like training vs testing error, cross-validation, generalisation error, regularisation, hyperparameter tuning, model selection, and data mismatch.

Reflections

This chapter didn’t involve much code, but it laid a strong theoretical foundation. What stood out the most was how strategically the book structures ML thinking. Rather than just diving into libraries, it starts with the essential mental models you need to develop a reliable ML system.

I appreciated the overview of the types of learning and the importance of data quality — something I witnessed first-hand in my YOLOv8 project. It reminded me how even the best models fail with bad or insufficient data.

My Key Takeaways

• ML isn’t just about algorithms — it’s about the data, the assumptions, the business context, and the ability to iterate.

• Understanding failure points like overfitting is just as important as getting high accuracy.

• Feature engineering and data preprocessing pipelines are the real unsung heroes of successful ML systems.

What’s Next?

Chapter 2 dives into a full end-to-end ML project, which I will be blogging about later. It’s where theory meets code.

Soon, I’ll also be exploring:

• Neural Networks with Keras & TensorFlow

• Convolutional & Recurrent Neural Nets

• Reinforcement Learning concepts

• And a deeper dive into unsupervised learning

I’ll also be sharing a detailed blog on my club project using YOLOv8 for retail product detection and tracking.

Final Thoughts

This chapter was a great warm-up and mindset-setter. If you’re self-studying ML or transitioning from course-based learning (like I am), don’t skip this chapter. It gives you the right lens to look at problems and techniques you’ll encounter ahead.

Let me know if you're reading this book too or working on cool ML/AI projects — I’d love to connect!