Book: Mastering Machine Learning with Python in Six Steps

Book: Mastering Machine Learning with Python in Six Steps


A Practical Implementation Guide to Predictive Data Analytics Using Python

Covers basic to advanced topics in an easy step-oriented manner
Concise on theory, strong focus on practical and hands-on approach
Explores advanced topics, such as Hyper-parameter tuning, deep natural language processing, neural network and deep learning
Describes state-of-art best practices for model tuning for better model accuracy

About The Book:

This book is your practical guide towards novice to master in machine learning with Python in six steps. The six steps path has been designed based on the “Six degrees of separation” theory which states that everyone and everything is a maximum of six steps away. Note that the theory deals with the quality of connections, rather than their existence. So, a great effort has been taken to design an eminent, yet simple six steps covering fundamentals to advanced topics gradually that will help a beginner walk his way from no or least knowledge of machine learning in Python to all the way to becoming a master practitioner. This book is also helpful for current Machine Learning practitioners to learn the advanced topics such as Hyperparameter tuning, various ensemble techniques, Natural Language Processing (NLP), deep learning, and basics of reinforcement learning.
 

Each topic has two parts, the first part will cover the theoretical concepts and the second part will cover practical implementation with different Python packages. The traditional approach of math to machine learning i.e., learning all the mathematic then understanding how to implement them to solve problems need a great deal of time/effort which has proven to be not efficient for working professionals looking to switch careers. Hence the focus in this book has been more on simplification, such that the theory/math behind algorithms have been covered only to extend required to get you started.
I recommend you to work with the book instead of reading it. Real learning goes on only through active participation. Hence, all the code presented in the book are available in the form of iPython notebooks to enable you to try out these examples yourselves and extend them to your advantage or interest as required later.
What You’ll Learn:

Examine the fundamentals of Python programming language
Review machine Learning history & evolution
Learn various machine learning system development frameworks
Learn fundamentals to advanced text mining techniques
Learn and implement deep learning frameworks

Who This Book Is For:
This book will serve as a great resource for learning machine learning concepts and implementation techniques for:

Python developers or data engineers looking to expand their knowledge or career into machine learning area.
A current non-Python (R, SAS, SPSS, Matlab or any other language) machine learning practitioners looking to expand their implementation skills in Python.
Novice machine learning practitioners looking to learn advanced topics such as hyperparameter tuning, various ensemble techniques, Natural Language Processing (NLP), deep learning, and basics of reinforcement learning.

Content at a Glance

Introduction
Chapter 1: Step 1 – Getting Started in Python
Chapter 2: Step 2 – Introduction to Machine Learning
Chapter 3: Step 3 – Fundamentals of Machine Learning
Chapter 4: Step 4 – Model Diagnosis and Tuning
Chapter 5: Step 5 – Text Mining and Recommender Systems
Chapter 6: Step 6 – Deep and Reinforcement Learning
Chapter 7: Conclusion

Table of Content
INTRODUCTION
CHAPTER 1: STEP 1 – GETTING STARTED IN PYTHON

The Best Things in Life Are Free
The Rising Star
Python 2.7.x or Python 3.4.x?
Windows Installation
OSX Installation
Linux Installation
Python from Official Website
Running Python

Key Concepts
Python Identifiers
Keywords
My First Python Program
Code Blocks (Indentation & Suites)
Basic Object Types
When to Use List vs. Tuples vs. Set vs. Dictionary
Comments in Python
Multiline Statement
Basic Operators
Control Structure
Lists
Tuple
Sets
Dictionary
User-Defined Functions
Module
File Input/Output
Exception Handling

Endnotes

CHAPTER 2: STEP 2 – INTRODUCTION TO MACHINE LEARNINGHISTORY AND EVOLUTION

Artificial Intelligence Evolution
Different Forms
Statistics
Data Mining
Data Analytics
Data Science
Statistics vs. Data Mining vs. Data Analytics vs. Data Science

Machine Learning Categories
Supervised Learning
Unsupervised Learning
Reinforcement Learning

Frameworks for Building Machine Learning Systems
Knowledge Discovery Databases (KDD)
Cross-Industry Standard Process for Data Mining
SEMMA (Sample, Explore, Modify, Model, Assess)
KDD vs. CRISP-DM vs. SEMMA

Machine Learning Python Packages
Data Analysis Packages
NumPy
Pandas
Matplotlib

Machine Learning Core Libraries

Endnotes

CHAPTER 3: STEP 3 – FUNDAMENTALS OF MACHINE LEARNING

Machine Learning Perspective of Data
Scales of Measurement
Nominal Scale of Measurement
Ordinal Scale of Measurement
Interval Scale of Measurement
Ratio Scale of Measurement

Feature Engineering
Dealing with Missing Data
Handling Categorical Data
Normalizing Data
Feature Construction or Generation

Exploratory Data Analysis (EDA)
Univariate Analysis
Multivariate Analysis

Supervised Learning– Regression
Correlation and Causation
Fitting a Slope
How Good Is Your Model?
Polynomial Regression
Multivariate Regression
Multicollinearity and Variation Inflation Factor (VIF)
Interpreting the OLS Regression Results
Regression Diagnosis
Regularization
Nonlinear Regression
Supervised Learning – Classification
Logistic Regression
Evaluating a Classification Model Performance
ROC Curve
Fitting Line
Stochastic Gradient Descent
Regularization
Multiclass Logistic Regression
Generalized Linear Models
Supervised Learning – Process Flow
Decision Trees
Support Vector Machine (SVM)
k Nearest Neighbors (kNN)
Time-Series Forecasting

Unsupervised Learning Process Flow
Clustering
K-means
Finding Value of k
Hierarchical Clustering
Principal Component Analysis (PCA)

Endnotes

CHAPTER 4: STEP 4 – MODEL DIAGNOSIS AND TUNING

Optimal Probability Cutoff Point
Which Error Is Costly?

Rare Event or Imbalanced Dataset
Known Disadvantages

Which Resampling Technique Is the Best?
Bias and Variance
Bias
Variance

K-Fold Cross-Validation
Stratified K-Fold Cross-Validation
Ensemble Methods
Bagging
Feature Importance
RandomForest
Extremely Randomized Trees (ExtraTree)
How Does the Decision Boundary Look?
Bagging – Essential Tuning Parameters

Boosting
Example Illustration for AdaBoost
Gradient Boosting
Boosting – Essential Tuning Parameters
Xgboost (eXtreme Gradient Boosting)

Ensemble Voting – Machine Learning’s Biggest Heroes United
Hard Voting vs. Soft Voting

Stacking
Hyperparameter Tuning
GridSearch
RandomSearch

Endnotes

CHAPTER 5: STEP 5 – TEXT MINING AND RECOMMENDER SYSTEMS

Text Mining Process Overview
Data Assemble (Text)
Social Media
Step 1 – Get Access Key (One-Time Activity)
Step 2 – Fetching Tweets

Data Preprocessing (Text)
Convert to Lower Case and Tokenize
Removing Noise
Part of Speech (PoS) Tagging
Stemming
Lemmatization
N-grams
Bag of Words (BoW)
Term Frequency-Inverse Document Frequency (TF-IDF)

Data Exploration (Text)
Frequency Chart
Word Cloud
Lexical Dispersion Plot
Co-occurrence Matrix

Model Building
Text Similarity
Text Clustering
Latent Semantic Analysis (LSA)

Topic Modeling
Latent Dirichlet Allocation (LDA)
Non-negative Matrix Factorization

Text Classification
Sentiment Analysis
Deep Natural Language Processing (DNLP)
Recommender Systems
Content-Based Filtering
Collaborative Filtering (CF)

Endnotes

CHAPTER 6: STEP 6 – DEEP AND REINFORCEMENT LEARNING

Artificial Neural Network (ANN)
What Goes Behind, When Computers Look at an Image?
Why Not a Simple Classification Model for Images?
Perceptron – Single Artificial Neuron
Multilayer Perceptrons (Feedforward Neural Network)
Load MNIST Data
Key Parameters for scikit-learn MLP

Restricted Boltzman Machines (RBM)
MLP Using Keras
Autoencoders
Dimension Reduction Using Autoencoder
De-noise Image Using Autoencoder

Convolution Neural Network (CNN)
CNN on CIFAR10 Dataset
CNN on MNIST Dataset

Recurrent Neural Network (RNN)
Long Short-Term Memory (LSTM)

Transfer Learning
Reinforcement Learning
Endnotes

CHAPTER 7: CONCLUSION

Summary
Tips
Start with Questions/Hypothesis Then Move to Data!
Don’t Reinvent the Wheels from Scratch
Start with Simple Models
Focus on Feature Engineering
Beware of Common ML Imposters

Happy Machine Learning

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Link: Book: Mastering Machine Learning with Python in Six Steps