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 Datamethods For Cleaning And Simplifying Datamachine Learning Packages And Toolsneural Networks And Ensemble Methods For Complex Datasetsgrokking 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 Technologydiscover 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 Bookgrokking 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, YouLl Build Interesting Projects With Python, Including Models For Spam Detection And Image Recognition. YouLl Also Pick Up Practical Skills For Cleaning And Preparing Data.WhatS Insidesupervised Algorithms For Classifying And Splitting Datamethods For Cleaning And Simplifying Datamachine Learning Packages And Toolsneural Networks And Ensemble Methods For Complex Datasetsabout The Readerfor Readers Who Know Basic Python. No Machine Learning Knowledge Necessary.About The Authorluis 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 Contents1 What Is Machine Learning? It Is Common Sense, Except Done By A Computer2 Types Of Machine Learning3 Drawing A Line Close To Our Points: Linear Regression4 Optimizing The Training Process: Underfitting, Overfitting, Testing, And Regularization5 Using Lines To Split Our Points: The Perceptron Algorithm6 A Continuous Approach To Splitting Points: Logistic Classifiers7 How Do You Measure Classification Models? Accuracy And Its Friends8 Using Probability To Its Maximum: The Naive Bayes Model9 Splitting Data By Asking Questions: Decision Trees10 Combining Building Blocks To Gain More Power: Neural Networks11 Finding Boundaries With Style: Support Vector Machines And The Kernel Method12 Combining Models To Maximize Results: Ensemble Learning13 Putting It All In Practice: A Real-Life Example Of Data Engineering And Machine Learning