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Are you thinking of learning more about Machine Learning using Python?

The importance of machine learning and deep learning is such that everyone regardless of their profession should have a fair understanding of how it works. Having said that, this book is geared towards the following set of people:

While this book would seek to explain common terms and algorithms in an intuitive way, it would however not dumb down the mathematics on whose foundation these techniques are based. There would be little assumption of prior knowledge on the part of the reader as terms would be introduced and explained as required. We would use a progressive approach whereby we start out slowly and improve on the complexity of our solutions.

To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach which would lead to better mental representations.

Step By Step Guide and Visual Illustrations and Examples

This book and the accompanying examples, you would be well suited to tackle problems which pique your interests using machine learning and deep learning models.
Instead of tough math formulas, this book contains several graphs and images which detail all important Python and Machine Learning concepts and their applications.

Target Users

The book designed for a variety of target audiences. The most suitable users would include:
Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field. Software developers and engineers with a strong programming background but seeking to break into the field of machine learning.Seasoned professionals in the field of artificial intelligence and machine learning who desire a bird’s eye view of current techniques and approaches.

What’s Inside This Book?

IntroductionIntroduction to Labels and FeaturesA Regression Example: Predicting Boston Housing PricesImport Libraries:How to forecast and PredictPopular Classification AlgorithmsIntroduction to K Nearest NeighborsIntroduction to Support Vector MachineExample of ClusteringRunning K-means with Scikit-LearnIntroduction to Deep Learning using TensorFlowDeep Learning Compared to Other Machine Learning ApproachesApplications of Deep LearningHow to run the Neural Network using TensorFlowCases of Study with Real DataSources & References

Frequently Asked Questions

Q: Is this book for me and do I need programming experience?
A: f you want to smash Machine Learning from scratch, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you’ll be OK.

Q: Can I loan this book to friends?
A: Yes. Under Amazon’s Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days.

Q: Does this book include everything I need to become a Machine Learning expert?
A: Unfortunately, no. This book is designed for readers taking their first steps in Machine Learning and further learning will be required beyond this book to master all aspects of Machine Learning.

Q: Can I have a refund if this book is not fitted for me?
A: Yes, Amazon refund you if you aren’t satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at contact@aiscicences.com.