Mastering Machine Learning With Python In Six Steps A Practical Implementation Guide To Predictive Data Analytics Using Pytho
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- Contents at a Glance
- Contents
- About the Author
- About the Technical Reviewer
- Acknowledgments
- 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?
- 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
- Machine Learning Categories
- Frameworks for Building Machine Learning Systems
- Machine Learning Python Packages
- Data Analysis Packages
- NumPy
- Pandas
- Matplotlib
- Using Global Functions
- 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
- Feature Engineering
- Exploratory Data Analysis (EDA)
- Supervised Learning– 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
- Endnotes
- 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
- 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)
- Restricted Boltzman Machines (RBM)
- MLP Using Keras
- Autoencoders
- Convolution Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Transfer Learning
- Reinforcement Learning
- Endnotes
- Chapter 7: Conclusion
- Index