About this book

The underlying goal of "Machine Learning Simplified" is to develop strong intuition into inner workings of ML. We use simple intuitive examples to explain complex concepts, algorithms or methods, as well as democratize all mathematics "behind the scenes".

After reading this book, you will understand everything that comes into the scope of supervised ML. You will be able to not only understand nitty-gritty details of mathematics, but also explain to anyone how things work on a high level.

About this book

Who the book is for

  • Developers
    Any front/backend developer or devops/software engineer who wants to become an ML engineer.
  • Students
    Students who take or plan to take ML classes, as well as any student who wants to transition to IT career.
  • Lecturers
    Lecturers or anyone else teaching the subject who want to explore how complex frameworks can be gently explained.
  • Everyone
    Literally anyone else who wants to feel confident while talking about ML in front of technical or business people.

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About this author

Author

Hi there. My name is Andrew. I am an MLOps Engineer by day and an enthusiastic STEM tutor by night.

While teaching ML to my students, it always occured to me that the materials I managed to find on the introduction to ML were either rife with academic trilogies, filled with theorems and designed for experienced researchers, or were sprinkled with fishy fairy tales about artificial intelligence, data-science magic, and jobs of the future. I tried to fix this problem by writing the "Machine Learning Simplified" book that makes machine learning both simple to understand and fun to read about!

Learn more about me on 5x12.ai. Join my ML in Production course. Follow me on Github, or LinkedIn.

“A great book for beginners! Andrew Wolf breaks down the most complex processes into simple steps.”

Passionately Plotted on goodreads

“This was very informative and so much can be learned from this. ”

Sebrina Bancroft on goodreads

“I LOVED the book. Simple and intuitive examples explain complex concepts.”

Helen on goodreads

“Andrew Wolf's book strips down one knowledge to a new and simplified way.”

Isa Rodriguez on goodreads

“Focus on the intuition, didn’t cram up the book with python code. ”

Eugene on goodreads

“Amazing book. so far the best resource I've ever read”

Lorenzo on goodreads

“Everything is straightforward and separated into several sections.”

Jailene on goodreads

“Great as an intro to machine learning, and as a refresher for those who already in the field.”

Paul on goodreads

“Andrew Wolf has very well narrated all prospectives of machine learning.”

Ciliya Joji on goodreads

FAQ

Yes. You can read it here in PDF format. In case you need EPUB format, you can buy it on Amazon .

Yes, you can purchase a hardcover edition. More information will be provided shortly.

If you'd like to express your appreciation or financially contribute to what I do, you could leave a rating and buy the book on Amazon (epub), or Leanpub (pdf).

There are no strict prerequisites to reading this book. However, basic knowledge of mathematics (incl. derivatives and linear algebra) and inferential statistics (incl. linear/polynomial regressions and distributions) will undoubtedly help the readers to get the most out of this book.

The initial seven chapters of the book lay the groundwork in supervised learning, offering thorough explanations of key concepts such as artificial intelligence, data science, machine learning, deep learning, and various fundamental algorithms like gradient descent and linear regression. It also discusses common challenges like overfitting, underfitting, and the trade-offs between bias and variance, alongside techniques like basis expansion and regularization. The following six chapters explore a range of advanced algorithms, including logistic regression, decision trees, ensemble models like boosting, bagging, and stacking, as well as support vector machines. These sections also delve into evaluation metrics such as accuracy, precision, recall, ROC AUC, logarithmic loss, and the F1 score.