As of now, Python 3 readiness (http://py3readiness.org/) shows that 360 of the
360 top packages for Python support 3.x. So in the second edition of Mastering Machine Learning with Python in Six Steps, all the code examples have been fully updated to Python 3, a great deal of time has been spent to fix all the editorial corrections from the first edition, and also added de-noising signal using wavelet transform example code.

The area of machine learning has seen a phenomenal growth in the recent past, and I have been lucky to have had the chance to be part of some of the exciting projects around machine learning applications for solving business problems. At this juncture, I’m happy to announce the launch of my book “Mastering Machine Learning with Python in Six Steps”, which is my answer to the quote “If there’s a book that you want to read, but it hasn’t been written yet, then you must write it” – Toni Morrison.

This book is a practical guide towards novice to master in machine learning with Python 3 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 mathematics 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 readers 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. You can find the full code on Apress Github repository here! or on my Github repository here!

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.

Table of Content:

• Chapter 1: Step 1 – Getting Started in Python                     …………………………..      1
• Chapter 2: Step 2 – Introduction to Machine Learning                     ……………..  53
• Chapter 3: Step 3 – Fundamentals of Machine Learning                     …………  117
• Chapter 4: Step 4 – Model Diagnosis and Tuning                      …………………..    209
• Chapter 5: Step 5 – Text Mining and Recommender Systems        ……………   251
• Chapter 6: Step 6 – Deep and Reinforcement Learning                   …………..   297
• Chapter 7: Conclusion                                                                                 ……………. 345

Link to buy the book:

 

or from Apress: https://www.apress.com/in/book/9781484249468

 

Detailed Table of Content:

• 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 Learning

  History 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 Measurementhttps://www.apress.com/in/book/9781484249468
  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

Noise reduction from IoT data
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