Does Machine Learning Require Math
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Machine Learning Math All You Need to Know Immediately About Math If You Want Spark In Deep Learning, Artificial Intelligent and Machine Learning
★ 55% OFF for Bookstores! NOW at $36.95 instead of $49.95★ You find out about machine learning form A to Z even if you are a beginner Do you want to spark in the science of XXI century? Do you want to become a recreational scientist in deep learning? If you answer yes to one of these previous questions, then keep reading till the end. Machine learning is an advanced form of data analysis and computation which uses the exceptional processing speed and pattern recognition techniques of computers to find and learn new trends in data. Putting it, it is an artificial-intelligence-inspired technique of programming that allows computers to improve their learning capabilities through the data they are fed, or they can access. The concept behind the technique is consistently to improve and to test, and it will be the key in the bigger technological revolution for the future. It is important for any current or aspiring data scientist to join the growing machine learning community, and contribute a quota to improve technology. This guide will focus on the following items: - Induction and Deduction - Decision Trees - Types of Artificial Intelligence and Machine Learning - Stacked Denoising Autoencoders - Robotics - Reinforcement Learning - Linear Algebra - How Companies Use Big Data to Increase Sales - What Is Supervised Machine Learning - How To Build A Predictive Model - Data Preprocessing with Machine Learning - Machine Learning and Robotics - How AI Is Revolutionizing Industry... AND MORE!!! What are you waiting for? A lot of people think that studying ML and Mathematics is difficult. It's because there are a lot of people that don't know the topic in depth so they can't explain it in easy ways. In this book the items will be described in such an easy way you will be surprised! Buy now if you want to spark in deep learning and know whatever it takes about ML and Math
Machine Learning with Python for Everyone
Author: Mark Fenner
language: en
Publisher: Addison-Wesley Professional
Release Date: 2019-07-30
The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use. Understand machine learning algorithms, models, and core machine learning concepts Classify examples with classifiers, and quantify examples with regressors Realistically assess performance of machine learning systems Use feature engineering to smooth rough data into useful forms Chain multiple components into one system and tune its performance Apply machine learning techniques to images and text Connect the core concepts to neural networks and graphical models Leverage the Python scikit-learn library and other powerful tools Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Grokking Machine Learning
Discover valuable machine learning techniques you can understand and apply using just high-school math. In Grokking Machine Learning you will learn: Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble methods for complex datasets Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Discover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data. ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations. About the book Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you’ll build interesting projects with Python, including models for spam detection and image recognition. You’ll also pick up practical skills for cleaning and preparing data. What's inside Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble methods for complex datasets About the reader For readers who know basic Python. No machine learning knowledge necessary. About the author Luis G. Serrano is a research scientist in quantum artificial intelligence. Previously, he was a Machine Learning Engineer at Google and Lead Artificial Intelligence Educator at Apple. Table of Contents 1 What is machine learning? It is common sense, except done by a computer 2 Types of machine learning 3 Drawing a line close to our points: Linear regression 4 Optimizing the training process: Underfitting, overfitting, testing, and regularization 5 Using lines to split our points: The perceptron algorithm 6 A continuous approach to splitting points: Logistic classifiers 7 How do you measure classification models? Accuracy and its friends 8 Using probability to its maximum: The naive Bayes model 9 Splitting data by asking questions: Decision trees 10 Combining building blocks to gain more power: Neural networks 11 Finding boundaries with style: Support vector machines and the kernel method 12 Combining models to maximize results: Ensemble learning 13 Putting it all in practice: A real-life example of data engineering and machine learning