Machine Learning for Beginners

 Complex concepts are broken down into simple steps and examples are carefully chosen to illustrate each concept. Mathematical concepts are explained without complicated notations and formulas.

 Hands-On Approach

 Countless examples are provided for you to try out in each chapter, so that you can understand exactly what different machine learning methods do.

 Systematic Approach

 A systematic approach is taken to provide you with the background knowledge needed before covering advanced concepts.

 How is this book different?

 The best way to learn anything is by doing.

 This book includes three hands-on projects at the end of the book for you to apply and practice all the concepts taught previously.

 Working through the projects will not only give you an immense sense of achievement, it'll also help you retain the knowledge and solidify your understanding.

 Whether you are an aspiring data scientist or just curious about machine learning, the book is designed to help you grasp the fundamental concepts of machine learning in a systematic and step-by-step fashion.
 

 What you'll learn:

• What is Machine Learning
• What is supervised, unsupervised, and reinforcement learning
• How to use the NumPy and pandas library
• How to use matplotlib to plot charts
• What is the Scikit-Learn library?
• What do the fit() and transform() methods do
• How to pre-process our data
• How to use pipelines and column transformers to streamline our code
• How to evaluate our models
• What is a confusion matrix and how to interpret it
• What is regression, classification, and clustering
• What is the theory behind the linear regression, poly regression, decision tree, random forest, SVM, and k-means clustering algorithms
• How to do a grid search to find the best hyperparameters
• What is regularization
• How to reduce the dimensions of our dataset
• and more...

 Finally, you'll be guided through three hands-on projects that require the application of all the topics covered.