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Learn and Build Data Science and Machine Learning Models from scratch!

Welcome to the Data Science from Scratch with Python Book!

The book offers you a solid introduction to the world of Data Science and Machine Learning. In this program, you’ll master fundamentals that will enable you to go further in the field, launch or advance a career, and join the next generation of Data Scientists talent that will help define a beneficial, new, AI-powered future for our world. You will study important topics such as Machine learning, Naïve Bayes, regressions, KNN, Decision trees and Random Forests, Ensemble modelling, K means Clustering, SVM, Neural Network and Deep learning.
The book will give you the skills you need to understand the most recent important models in data science, and build and implement your own algorithms.
The term is comprised of more than 15 chapters and practical project in each step. Building a project is one of the best ways to demonstrate the skills you've learned, and each project will contribute to an impressive professional portfolio that shows potential employers your mastery of data science techniques. 

Educational Objectives: Become an expert in data science, and learn to implement them from scratch and using frameworks like NumPy, Pandas, Scikit-learn and Keras. You will:

• Learn to program in Python at a good level
• Learn important concepts in Data Science
• Learn how to deal with Real-world Data
• Lean how to deal with Outlier and missing data
• Build regression models
• Build Classification models
• Build Clustering models

Prerequisite Knowledge: The first chapters of the book will give you all the basic knowledge of probability, statistic, calculus (multivariable derivatives) and linear algebra (matrix multiplication needed for mastering data science. Please, take time to learn them.

Contact Info: While going through the book, if you have questions about anything, you can reach us at [email protected].
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What’s Inside This Book?

• Preliminary to understand Data Science
• Different Data Science Elements
• Important Concepts in Data Science and Machine Learning
• Overview of Python and Data Processing
• Python Data Science Tools
• Dealing with Real-World Data
• Statistics and Probability
• Bayes Rule
• Linear Regression
• Logistic Regression
• Support Vector Machines
• Decision Trees and Random Forests
• K-Nearest Neighbor
• Naïve Bayes
• Model Evaluation and Selection
• K-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis
• Artificial Neural Networks
• Convolution Neural Networks
• Reinforcement Learning Techniques
• Upper Confidence Bound
• Thompson Sampling

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EDITIORIAL REVIEWS

"I always find this data science introductory book very simple written and very easy to follow. I recommend it for any beginners want to learn Data Science from scratch.
The book is a very well practical guide step by step for data science with python. A Great resource for anyone who wants to get started on data science."
- Ph.D Amir Sharif, Data scientist Consultant at BCG. 

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