***** BUY NOW (will soon return to 24.77 $) ***** MONEY BACK GUARANTEE BY AMAZON (See Below FAQ) *****
******Free eBook for customers who purchase the print book from Amazon******
Are you thinking of learning data science from scratch using Python?(For Beginners)
If you are looking for a complete step-by-step guide to data science using Python from scratch, this book is for you. After his great success with his first book “Data Analysis from Scratch with Python”, Peters Morgan publishes his second book focusing now in data science and machine learning. It is considered by practitioners as the easiest guide ever written in this domain.From AI Sciences Publisher
Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. 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
The Book give complete instructions for manipulating, processing, cleaning, modeling and crunching datasets in Python. This is a hands-on guide with practical case studies of data analysis problems effectively. You will learn, pandas, NumPy, IPython, and Jupiter in the Process.Target Users
Target Users The book is designed for a variety of target audiences. The most suitable users would include:- Beginners who want to approach data science, but are too afraid of complex math to start
- Newbies in computer science techniques and data science
- Professionals in data science and social sciences
- Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way
- Students and academicians, especially those focusing on data science
What’s Inside This Book?
Part 1: Data Science Fundamentals, Concepts and Algorithms
- Introduction
- Statistics
- Probability
- Bayes’ Theorem and Naïve Bayes Algorithm
- Asking the Right Question
- Data Acquisition
- Data Preparation
- Data Exploration
- Data Modelling
- Data Presentation
- Supervised Learning Algorithms
- Unsupervised Learning Algorithms
- Semi-supervised Learning Algorithms
- Reinforcement Learning Algorithms
- Overfitting and Underfitting
- Correctness
- The Bias-Variance Trade-off
- Feature Extraction and Selection
Part 2: Data Science in Practice
- Overview of Python Programming Language
- Python Data Science Tools
- Jupyter Notebook
- Numerical Python (Numpy)
- Pandas
- Scientific Python (Scipy)
- Matplotlib
- Scikit-Learn
- K-Nearest Neighbors
- Naive Bayes
- Simple and Multiple Linear Regression
- Logistic Regression
- GLM models
- Decision Trees and Random forest
- Perceptrons
- Backpropagation
- Clustering
- Natural Language Processing