Mastering Machine Learning with Python in Six Steps A Practical Implementation Guide to Predictive Data Analytics Using Python 1st Edition by Manohar Swamynathan ISBN 1484228669 9781484228661

Mastering Machine Learning with Python in Six Steps A Practical Implementation Guide to Predictive Data Analytics Using Python 1st Edition by Manohar Swamynathan – Ebook PDF Instant Download/Delivery. 1484228669, 9781484228661
Full download Mastering Machine Learning with Python in Six Steps A Practical Implementation Guide to Predictive Data Analytics Using Python 1st Edition after payment

Product details:

ISBN 10: 1484228669 
ISBN 13: 9781484228661
Author: Manohar Swamynathan

Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner.  This book’s approach is based on the “Six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages.  You’ll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered. Finally, you’ll explore advanced text mining techniques, neural networks and deep learning techniques, and their implementation.  All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage. What You’ll Learn Examine the fundamentals of Python programming language Review machine Learning history and evolution Understand machine learning system development frameworks Implement supervised/unsupervised/reinforcement learning techniques with examples Explore fundamental to advanced text mining techniques Implement various deep learning frameworks Who This Book Is For Python developers or data engineers looking to expand their knowledge or career into machine learning area. 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.

Mastering Machine Learning with Python in Six Steps A Practical Implementation Guide to Predictive Data Analytics Using Python 1st Table of contents:

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
Graphical Installer
Command-Line Installer
Linux Installation
Python from Official Website
Running Python
Key Concepts
Python Identifiers
Keywords
My First Python Program
Code Blocks (Indentation & Suites)
Indentation
Suites
Basic Object Types
When to Use List vs. Tuples vs. Set vs. Dictionary
Comments in Python
Multiline Statement
Multiple Statements on a Single Line
Basic Operators
Arithmetic Operators
Comparison or Relational Operators
Assignment Operators
Bitwise Operators
Logical Operators
Membership Operators
Identity Operators
Control Structure
Selection
Iteration
Lists
Tuple
Sets
Accessing Set Elements
Changing a Set in Python
Removing Items from Set
Set Operations
Set Union
Set Intersection
Set Difference
Set Symmetric Difference
Basic Operations
Dictionary
User-Defined Functions
Defining a Function
Syntax for Creating Functions with Argument
Scope of Variables
Default Argument
Variable Length Arguments
The *args Will Provide All Function Parameters in the Form of a tuple
Module
File Input/Output
Opening a File
Exception Handling
Endnotes
Chapter 2: Step 2 – Introduction to Machine Learning
History and Evolution
Artificial Intelligence Evolution
Different Forms
Statistics
Frequentist
Bayesian
Regression
Descriptive Analytics
Data Mining
Data Analytics
Diagnostic Analytics
Predictive Analytics
Prescriptive Analytics
Data Science
Statistics vs. Data Mining vs. Data Analytics vs. Data Science
Machine Learning Categories
Supervised Learning
1) Regression
2) Classification
Unsupervised Learning
Clustering
Dimension Reduction
Anomaly Detection
Reinforcement Learning
Frameworks for Building Machine Learning Systems
Knowledge Discovery Databases (KDD)
Selection
Preprocessing
Transformation
Data Mining
Interpretation / Evaluation
Cross-Industry Standard Process for Data Mining
Phase 1: Business Understanding
Phase 2: Data Understanding
Phase 3: Data Preparation
Phase 4: Modeling
Phase 5: Evaluation
Phase 6: Deployment
SEMMA (Sample, Explore, Modify, Model, Assess)
Sample
Explore
Modify
Model
Assess
KDD vs. CRISP-DM vs. SEMMA
Machine Learning Python Packages
Data Analysis Packages
NumPy
Array
Creating NumPy Array
Data Types
Array Indexing
Field Access
Basic Slicing
Advanced Indexing
Array Math
Broadcasting
Pandas
Data Structures
DataFrame
Reading and Writing Data
Basic Statistics Summary
Viewing Data
Basic Operations
Merge/Join
Join
Grouping
Pivot Tables
Matplotlib
Using Global Functions
Customizing Labels
Object Oriented
Line Plots – Using ax.plot()
Multiple Lines on Same Axis
Multiple Lines on Different Axis
Control the Line Style and Marker Style
Line Style Reference
Marker Reference
Colomaps Reference
Bar Plots – using ax.bar() and ax.barh()
Horizontal Bar Charts
Side-by-Side Bar Chart
Stacked Bar Example Code
Pie Chart – Using ax.pie()
Example Code for Grid Creation
Plotting – Defaults
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
Correlation Matrix
Pair Plot
Findings from EDA
Supervised Learning– Regression
Correlation and Causation
Fitting a Slope
How Good Is Your Model?
R-Squared for Goodness of Fit
Root Mean Squared Error (RMSE)
Mean Absolute Error
Polynomial Regression
Multivariate Regression
Multicollinearity and Variation Inflation Factor (VIF)
Interpreting the OLS Regression Results
Regression Diagnosis
Outliers
Homoscedasticity and Normality
Over-fitting and Under-fitting
Regularization
Nonlinear Regression
Supervised Learning – Classification
Logistic Regression
Evaluating a Classification Model Performance
ROC Curve
Fitting Line
Stochastic Gradient Descent
Regularization
Multiclass Logistic Regression
Load Data
Normalize Data
Split Data
Training Logistic Regression Model and Evaluating
Generalized Linear Models
Supervised Learning – Process Flow
Decision Trees
How the Tree Splits and Grows?
Conditions for Stopping Partitioning
Key Parameters for Stopping Tree Growth
Support Vector Machine (SVM)
Key Parameters
k Nearest Neighbors (kNN)
Time-Series Forecasting
Components of Time Series
Autoregressive Integrated Moving Average (ARIMA)
Running ARIMA Model
Checking for Stationary
Autocorrelation Test
Build Model and Evaluate
Predicting the Future Values
Unsupervised Learning Process Flow
Clustering
K-means
Limitations of K-means
Finding Value of k
Elbow Method
Average Silhouette Method
Hierarchical Clustering
Key Parameters
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
Boosting Iteration 1
Boosting Iteration 2
Boosting Iteration 3
Final Model
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
Sentence Tokenizing
Word Tokenizing
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
Outline Placeholder
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)
Word2Vec
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
Visualization of Layers
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

People also search for Mastering Machine Learning with Python in Six Steps A Practical Implementation Guide to Predictive Data Analytics Using Python 1st:

mastering machine learning with python in six steps
    
mastering machine learning with python in six steps github
    
how long to master python
    
is python good for machine learning
    
how long does it take to master python