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Index
Introduction
CHAPTER 1: Machine Learning History
CHAPTER 2: What is Machine Learning?
What is Machine Learning?
When Should We Use Machine Learning?
Steps in Building a Machine Learning System
CHAPTER 3: Categories of Machine Learning
Supervised Machine Learning
Unsupervised Machine Learning
Reinforcement Learning
CHAPTER 4: Sectors and Industries that use M.L
Healthcare
Drug Manufacturing and Discovery
Personalized Medication or Treatment
Finance
Retail
Statistical Arbitrage
Prediction
CHAPTER 5: Introduction to Programming Languages
Knowing Some of the Features of This Library
CHAPTER 6: Why Python
Simple and Easy to Learn
High-level Language
Fast and Efficient to Use
Open Source
Interpreted
Object Oriented
Portable
Batteries Included
Numpy, Panda and Scikit-learn
Improved Productivity
Easy Learnability
Easy Readability
Wide Support of Major Platforms
Software Quality
CHAPTER 7: Installing Scikit -Learn
What Is Scikit-Learn?
Understanding More About Tensor Flow
Getting Started with Scikit-Learn
CHAPTER 8: IDE (Spyder, Jupiter)
Python Interpreter, IDLE, and the Shell
CHAPTER 9: Introduction to the Main Python Libraries
Keras
Theano
TensorFlow
Scikit-Learn
CHAPTER 10: Introduction to Bias and Variance
CHAPTER 11: Evaluating the error in the Regression models (RMSE, MAE, R2)
Regression Analysis
Testing with correlation:
CHAPTER 12: Supervised Learning
Supervised Learning Algorithms
CHAPTER 13: Linear Regression
Choosing the best regression model
Statistical Methods Used to Find the Best Regression Model
Finding the correct Regression Model
CHAPTER 14: Random Forests -Theory
How to Interpret Random Forests
CHAPTER 15: Evaluation Metrics and Classification Models
Model Evaluation
CHAPTER 16: Unsupervised Learning
Unsupervised Learning Algorithms
CHAPTER 17: Deep learning
Classification
Pattern recognition
CHAPTER 18: Logistic Regression-Theory
CHAPTER 19: KNN -theory
CHAPTER 20: Support Vector Machines Classification
CHAPTER 21: Reinforcement Machine Learning Algorithms
How Clustering Algorithms Work
Types of Clustering Algorithms
Application of Clustering Algorithms:
When to use Clustering Algorithms?
CHAPTER 22: Naive Bayes -Theory
Naïve Bayes Estimation and Bayesian Networks
CHAPTER 23: Decision Trees -Theory
Classification Using Decision Tree
Decision Tree Construction
Decision Tree Algorithm
CHAPTER 24: Benefits of Machine Learning
CHAPTER 25: Deep Neutral Network
Neural networks
Feedforward Neural Networks
Single-layer perceptron
Multi-layer Perceptron
Recurrent Neural Networks
Backpropagation
CHAPTER 26: Big Data Analytics
Volume
Velocity
Variety
Value
Veracity
Current uses of Big Data.
CHAPTER 27: Data Mining and Applications
How Does Data Mining Work?
Unbalanced Data Set
Conclusion
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