Mastering Machine Learning in One Day

Need to Master Machine Learning in ONE DAY ?If you are looking for a complete book in machine learning fundamentals, this one is for you.Machine learning is not just another buzzword. So many professionals who work in different areas such as IT, security, marketing, automation, and even medicine, know that machine learning is the key to development. Without it, so many amazing things that make our lives easier - such as spam-filtering, Google search, relevant ads, accurate weather forecasting or sport prediction - would be impossible. Machine learning is not some speculative science. It is really practical and can be applied to almost every area of modern life and business. Now you can learn it too! This book is the starting point you've been waiting for. From AI Sciences Publishing 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. Readers are advised to adopt a hands on approach, which would lead to better mental representations. What's Inside This Book?Chapter 1: Introduction to Machine Learning What Is Machine Learning? Problems that Machine Learning Can Solve Medicine Vision Fraud detection Natural Language Processing Finance Meteorological Chapter 2: Types of Learning Supervised Learning Unsupervised Learning Reinforcement Learning Semi-supervised Learning Instance-Based Learning Chapter 4: Statistics and Probabilities What is Statistics? Descriptive Statistics Inferential Statistics Introduction to Basic Terms Probability Rules Discrete Probability Distributions Continuous Probability Distributions Confidence interval (1-α) Steps for hypothesis testing Chapter 5: Machine Learning Algorithms Linear Regression Benefits of linear regression Downsides of linear regression Logistic Regression Benefits and downside of logistic regression Decision Trees and Random forest Benefits and downside of Decision Trees Bagging Random Forest Benefits of Random Forest Downsides of Random Forest Boosting Benefits of Boosting Downsides of Boosting Support vector machines Benefits of SVMs Downsides of SVMs k Nearest Neighbors Benefits of k-Nearest Neighbor Downsides of k-Nearest Neighbor Clustering and K-means K-Means Clustering Benefits of K-Means algorithm Downsides of K-Means algorithm Chapter 6: Model Performance R-Squared (R2) Adjusted R-squared Confusion matrix ROC Curve and AUC Cross-Validation Bias Variance Bias-Variance tradeoff Chapter 7: Best Practices Feature Engineering One-hot encoding Binning Feature Scaling Data Imputation techniques Overfitting and underfitting Regularization Frequently Asked Questions Q: Can I have a refund if this book doesn't fit for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform.***** MONEY BACK GUARANTEE BY AMAZON *****