Machine Learning with Python
US$ 19.95
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Description
Contents
Reviews
Language
English
ISBN
9789386551931
Cover Page
Title Page
Copyright Page
Preface
Acknowledgement
Table of Contents
Chapter 1: Introduction to Machine Learning
1.1 Introduction
1.2 Machine Learning Process
Chapter 2: Understanding Python
2.1 Why Python
2.2 Download and Install Python
2.2.1 Install Python in Windows
2.2.2 Anaconda
2.3 First Python Program
2.4 Python Basics
2.5 Data Structure and loops
Chapter 3: Feature Engineering
3.1 What is Feature?
3.2 Why Feature Engineering
3.3 Feature Extraction
3.4 Feature Selection
3.5 Feature Engineering Methods
3.5.1 Handling Numerical Features
3.5.2 Handling Categorical Features
3.5.3 Handling time-based features
3.5.4 Handling text features
3.5.5 Missing Data
3.5.6 Dimension reduction
3.6 Feature Engineering with Python
3.6.1 Pandas
3.6.1.1 Data Import
3.6.1.2 Data Export
3.6.1.3 Data Selection
3.6.1.4 Data Cleaning
Chapter 4: Data Visualization
4.1 Line Chart
4.2 Bar Chart
4.3 Pie Chart
4.4 Histograms
4.5 Scatter Plot
4.6 Box Plot
4.7 Plotting using Object Oriented way
4.8 Seaborn
4.8.1 Distplot
4.8.2 Jointplot
4.8.3 Kernel Density Estimation for bivariate distribution
4.8.4 Bivariate analysis on each pair of features
4.8.5 Categorical Scatterplot
4.8.6 Violonplots
4.8.7 Point Plots
Chapter 5: Regression
5.1 Simple Regression
5.2 Multiple Regression
5.2.1 Polynomial Regression
5.3 Model assessment
5.3.1 Training Error
5.3.2 Generalized Error
5.3.3 Testing Error
5.3.4 Irreducible Error
5.3.5 Bias-Variance Tradeoff
Chapter 6: More on Regression
6.1 Introduction
6.2 Ridge Regression
6.3 Lasso Regression
6.3.1 All Subset algorithm
6.3.2 Greedy Algorithm for Feature Selection
6.3.3 Regularization for Feature selection
6.4 Non-Parametric Regression
6.4.1 K-Nearest Neighbor Regression
6.4.2 Kernel Regression
Chapter 7: Classification
7.1 Linear Classifiers
7.2 Logistic regression
7.3 Decision Tress
7.3.1 Tree Terminology
7.3.2 Decision Tree Learning
7.3.3 Decision Boundaries
7.4 Random Forest
7.5 Naïve Byes
Chapter 8: Un Supervised Learning
8.1 Clustering
8.2 K Means Clustering
8.2.1 Problem with Random assignment of Cluster centroid
8.2.2 Finding value of K
8.3 Hierarchical Clustering
8.3.1 Distance Metrices
8.3.2 Linkage
Chapter 9: Text Analysis
9.1 Basic Text Processing with Python
9.1.1 String Comparisons
9.1.2 String Conversions
9.1.3 String Manipulations
9.2 Regular Expression
9.3 Natural Language Processing
9.3.1 Stemming
9.3.2 Lemmatization
9.3.3 Tokenization
9.4 Text Classification
9.5 Topic Modeling
Chapter 10: Neural Network and Deep Learning
10.1 Vectorization
10.2 Neural Network
10.2.1 Gradient Descent
10.2.2 Activation function
10.2.2.1 Sigmoid Function
10.2.2.2 Tanh Function
10.2.2.3 ReLu Function
10.2.3 Parameter Initialization
10.2.4 Optimizer
10.2.5 Loss Function
10.3 Deep Learning
10.3.1 Hyper parameters
10.4 Deep Learning Architecture
10.4.1 Deep belief networks
10.4.2 Convolutional neural networks
10.4.3 Recurrent neural networks
10.4.4 Long Short-Term Memory
10.4.5 Deep Stacking Network
10.5 Deep Learning Framework
Chapter 11: Recommendation System
11.1 Popularity Based Recommender Engines
11.2 Content Based Recommendation Engine
11.3 Classification Based Recommendation Engine
11.4 Collaborative Filtering
Chapter 12: Time Series Analysis
12.1 Date and Time Handling
12.2 Window Functions
12.3 Correlation
12.4 Time Series Forecasting
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$19.95
