Ultimate Step by Step Guide to Machine Learning Using Python: Predictive modelling concepts explained in simple terms for beginners

by Daneyal Anis

Then let this breezy and fun little book on Python and machine learning models make you a data scientist in 7 days!
First part of this book introduces Python basics
1) Data Structures like Pandas
2) Foundational libraries like Numpy, Seaborn and Scikit-Learn
Second part of this book shows you how to build predictive machine learning models step by step using techniques such
1) Regression analysis
2) Decision tree analysis
3) Training and testing data models
4) And much more!
After reading this book you will be able
1) Code in Python with confidence
2) Build new machine learning models from scratch
3) Know how to clean and prepare your data for analytics
4) Speak confidently about statistical analysis techniques
Data Science was ranked the fast-growing field by LinkedIn and Data Scientist is one of the most highly sought after and lucrative careers in the world!
If you are on the fence about making the leap to a new and lucrative career, this is the book for you!

1) Step by step code examples and explanation
2) Complex concepts explained visually
3) Real world applicability of the machine learning models introduced
4) Bonus free code samples that you can try yourself without any prior experience in Python!
What do I need to get started?
You will have a step by step action plan in place once you finish this book and finally feel that you, can master data science and machine learning and start lucrative and rewarding career!
Ready to dive in to the exciting world of Python and Machine Learning?
Then scroll up to the top and hit that BUY BUTTON!

Genres: Engineering

68 pages, Paperback

Published February 17, 2020

Reviews

[Scott Pearson · Rating 4 out of 5]

The conscientious reader should carefully heed the words of this title. The book functions as an introduction to Python that leads, step by step, to using it with Machine Learning. It takes the shortest and easiest path to get there. As such, it provides fairly immediate gratification for the reader. With such low-hanging fruit, its intended audience is beginners without a lot of knowledge in computer science. For that audience, Anis effectively communicates his message. My only concern is that it does not provide many ways to gain depth, through suggested resources, appendices, footnotes/endnotes, etc.

This book helps beginners get over the initial fear of programming and machine learning. These terms are thrown about casually, and this book demystifies them. However, it does not veer from that trail almost at all. An interested reader will surely need to purchase two other books to read after Anis’ work: a book on Python and a book on Machine Learning. This book can serve as a high-level overview, but more in-depth knowledge is needed to leverage machine learning’s and Python’s strengths towards particular problems.

As a computer programmer and as a writer, I appreciate how quickly Anis gets us up to speed with Python and with basic machine learning concepts. He clearly communicates with little technical jargon. He describes his concepts simply and elegantly. He deserves much laudatory credit for this accomplishment. In addition, his choice of Python serves his purposes well. In my professional work, I’ve dealt with these more with the R programming/statistics language. Python certainly seems easier and more suited for beginners.

Overall, this book is well-written, has code snippets, and has good graphics. However, it must serve as one resource among many. It lacks a comprehensive treatment of the matter or even suggestions on how to access one. Anyone looking to do serious work and depth needs to spend time using other resources. Besides beginning data scientists, this work could help business leadership identify specific technical opportunities in the Python-Machine Learning combination. This book whet my appetite, but I am still hungry for the main course.

[Pablo María Fernández · Rating 2 out of 5]

Para los que venimos con rodaje en estos temas es un buen repaso y sintetiza en un solo lugar conceptos básicos de Data Mining. Es bastante ordenado cómo va siguiendo los temas (definiciones de tipos de dato, de algoritmos, etc.) pero siempre sin profundizar por el límite de las menos de cien hojas y cae a veces en punteos tipo PowerPoint que no llegan a describir temas clave con el detalle necesario. Se ayuda de un ejemplo propio en Python para hacer un modelo predictivo de punta a punta (todavía no lo pude descargar, es medio engorroso el acceso a través de su web). Vale los USD0,99 que pagué, no mucho más.

[Giovanni · Rating 5 out of 5]

surprisingly comprehensible and extraordinarily full of information

I'm not very experienced in this, I just started to get interested in Python from the very moment that I needed to work with the data to optimize my online business and I have to admit that this book is surprisingly comprehensible and extraordinarily full of information. I am really satisfied with the choice, I recommend to those like me want to find out more about the data science to buy this book...

[Val · Rating 5 out of 5]

A must read for Python Enthusiasts

Daneyal explained python and how to get started in an easy to follow guidelines. I have always enjoyed object oriented languages like Java, but enjoyed how this book dealt with data science programming using Python

[Jack Strom · Rating 3 out of 5]

Good book but needs editing

Good intro. But many grammatical errors. One big error was in coding the type of property as ordinal data. It’s categorical data and so pandas.get_dummies should be used for encoding this data

[Katrina Tie · Rating 5 out of 5]

Great for a beginner

This book gave me, a beginner, a quick introduction to python. I know have a workable knowledge to get started.

[Roberto · Rating 5 out of 5]

Fast and useful

This book explains in a easy way data science. Simple and complete. You can start working with data science fast.

[John Ferrier Jr · Rating 1 out of 5]

There's nothing here...

This "book" just gives a Wikipedia intro to the topic. Don't pay for this. It's not worth the 99 cents.

[Chris · Rating 4 out of 5]

Concise, informative intro to ML in Python

[Laxman Chiliveru · Rating 4 out of 5]

Introductory book for ML and modelling

The book helps to understand the process of ML through the a single use case of house prices data.

The explanations for various ML concepts and the process for ML are helpful.