Home Page Icon
Home Page
Table of Contents for
Cover Page
Close
Cover Page
by
Python Machine Learning for Beginners
Cover Page
Title Page
Copyright
How to contact us
About the Publisher
AI Publishing Is Searching for Authors Like You
Table of Contents
Preface
About the Author
Get in Touch with Us
Download the PDF version
Chapter 1: Introduction and Environment Set Up
1.1. Difference between Data Science and Machine Learning
1.2. Steps in Learning Data Science and Machine Learning
1.3. Environment Setup
1.3.1. Windows Setup
1.3.2. Mac Setup
1.3.3. Linux Setup
1.3.4. Using Google Colab Cloud Environment
Chapter 2: Python Crash Course
2.1. Writing Your First Program
2.2. Python Variables and Data Types
2.3. Python Operators
2.4. Conditional Statements
2.5. Iteration Statements
2.6. Functions
2.7. Objects and Classes
2.8. Data Science and Machine Learning Libraries
2.8.1 NumPy
2.8.2. Matplotlib
2.8.3. Seaborn
2.8.4. Pandas
2.8.5. Scikit Learn
2.8.6. TensorFlow
2.8.7. Keras
Exercise 2.1
Exercise 2.2
Chapter 3: Python NumPy Library for Data Analysis
3.1. Advantages of NumPy Library
3.2. Creating NumPy Arrays
3.2.1 Using Array Methods
3.2.2. Using Arrange Method
3.2.3. Using Ones Method
3.2.4. Using Zeros Method
3.2.5. Using Eyes Method
3.2.6. Using Random Method
3.3. Reshaping NumPy Arrays
3.4. Array Indexing and Slicing
3.5. NumPy for Arithmetic Operations
3.5.1. Finding Square Roots
3.5.2. Finding Logs
3.5.3. Finding Exponents
3.5.4. Finding Sine and Cosine
3.6. NumPy for Linear Algebra Operations
3.6.1. Finding Matrix Dot Product
3.6.2. Element-wise Matrix Multiplication
3.6.3. Finding Matrix Inverse
3.6.4. Finding Matrix Determinant
3.6.5. Finding Matrix Trace
Exercise 3.1
Exercise 3.2
Chapter 4: Introduction to Pandas Library for Data Analysis
4.1. Introduction
4.2. Reading Data into Pandas Dataframe
4.3. Filtering Rows
4.4. Filtering Columns
4.5. Concatenating Dataframes
4.6. Sorting Dataframes
4.7. Apply Function
4.8. Pivot & Crosstab
4.9. Arithmetic Operations with Where
Exercise 4.1
Exercise 4.2
Chapter 5: Data Visualization via Matplotlib, Seaborn, and Pandas Libraries
5.1. What is Data Visualization?
5.2. Data Visualization via Matplotlib
5.2.1. Line Plots
5.2.2. Titles, Labels, and Legends
5.2.3. Plotting Using CSV and TSV files
5.2.4. Scatter Plots
5.2.5. Bar Plots
5.2.6. Histograms
5.2.7. Pie Charts
5.3. Data Visualization via Seaborn
5.3.1. The Dist Plot
5.3.2 The Joint Plot
5.3.3. The Pair Plot
5.3.4. The Bar Plot
5.3.5. The Count Plot
5.3.6. The Box Plot
5.3.7. The Violin Plot
5.4. Data Visualization via Pandas
5.4.1. Loading Datasets with Pandas
5.4.2. Plotting Histograms with Pandas
5.4.3. Pandas Line Plots
5.4.4. Pandas Scatter Plots
5.4.5. Pandas Bar Plots
5.4.6. Pandas Box Plots
Exercise 5.1
Exercise 5.2
Chapter 6: Solving Regression Problems in Machine Learning Using Sklearn Library
6.1. Preparing Data for Regression Problems
6.1.1. Dividing Data into Features and Labels
6.1.2. Converting Categorical Data to Numbers
6.1.3. Divide Data into Training and Test Sets
6.1.4. Data Scaling/Normalization
6.2. Linear Regression
6.3. KNN Regression
6.4. Random Forest Regression
6.5. Support Vector Regression
6.6. K Fold Cross-Validation
6.7. Making Prediction on a Single Record
Exercise 6.1
Exercise 6.2
Chapter 7: Solving Classification Problems in Machine Learning Using Sklearn Library
7.1. Preparing Data for Classification Problems
7.1.1. Dividing Data into Features and Labels
7.1.2. Converting Categorical Data to Numbers
7.1.3. Divide Data into Training and Test Sets
7.1.4. Data Scaling/Normalization
7.2. Logistic Regression
7.3. KNN Classifier
7.4. Random Forest Classifier
7.5. Support Vector Classification
7.6. K-Fold Cross-Validation
7.7. Predicting a Single Value
Exercise 7.1
Exercise 7.2
Chapter 8: Data Clustering with Machine Learning Using Sklearn Library
8.1. K Means Clustering
8.1.1. Clustering Dummy Data with Sklearn
8.1.2. Clustering Iris Dataset
8.2. Hierarchical Clustering
8.2.1. Clustering Dummy Data
8.2.2. Clustering the Iris Dataset
Exercise 8.1
Exercise 8.2
Chapter 9: Deep Learning with Python TensorFlow 2.0
9.1. Densely Connected Neural Network
9.1.1. Feed Forward
9.1.2. Backpropagation
9.1.3. Implementing a Densely Connected Neural Network
Importing Required Libraries
Importing the Dataset
Dividing Data into Training and Test Sets
Creating a Neural Network
Evaluating the Neural Network Performance
9.2. Recurrent Neural Networks (RNN)
9.2.1. What Is an RNN and LSTM?
What Is an RNN?
Problems with RNN
What Is an LSTM?
9.3. Predicting Future Stock Prices via LSTM in Keras
9.3.1. Training the Stock Prediction Model
9.3.2. Testing the Stock Prediction Model
9.4. Convolutional Neural Network
9.4.1. Image Classification with CNN
9.4.2. Implementing CNN with TensorFlow Keras
Exercise 9.1
Exercise 9.2
Chapter 10: Dimensionality Reduction with PCA and LDA Using Sklearn
10.1. Principal Component Analysis
10.2. Linear Discriminant Analysis
Exercise 10.1
Exercise 10.2
Exercises Solutions
Exercise 2.1
Exercise 2.2
Exercise 3.1
Exercise 3.2
Exercise 4.1
Exercise 4.2
Exercise 5.1
Exercise 5.2
Exercise 6.1
Exercise 6.2
Exercise 7.1
Exercise 7.2
Exercise 8.1
Exercise 8.2
Exercise 9.1
Exercise 9.2
Exercise 10.1
Exercise 10.2
Search in book...
Toggle Font Controls
Playlists
Add To
Create new playlist
Name your new playlist
Playlist description (optional)
Cancel
Create playlist
Sign In
Email address
Password
Forgot Password?
Create account
Login
or
Continue with Facebook
Continue with Google
Sign Up
Full Name
Email address
Confirm Email Address
Password
Login
Create account
or
Continue with Facebook
Continue with Google
Next
Next Chapter
Title Page
Add Highlight
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click
here
login for view all page.
Day Mode
Cloud Mode
Night Mode
Reset