Python Machine Learning. A Crash Course for Beginners to Understand Machine learning, Artificial Intelligence, Neural Networks, and Deep Learning with Scikit-Learn, TensorFlow, and Keras.

✍ Scribed by Josh Hugh Learning

• Year: 2019
• Tongue: English
• Leaves: 178
• Category: Library

✦ Table of Contents

Introduction
Chapter 1: The Basics of Machine Learning
The Benefits of Machine Learning
Supervised Machine Learning
Unsupervised Machine Learning
Reinforcement Machine Learning
Chapter 2: Learning the Data sets of Python
Structured Data Sets
Unstructured Data Sets
How to Manage the Missing Data
Splitting Your Data
Training and Testing Your Data
Chapter 3: Supervised Learning with Regressions
The Linear Regression
The Cost Function
Using Weight Training with Gradient Descent
Polynomial Regression
Chapter 4: Regularization
Different Types of Fitting with Predicted Prices
How to Detect Overfitting
How Can I Fix Overfitting?
Chapter 5: Supervised Learning with Classification
Logistic Regression
Multiclass Classification
Chapter 6: Non-linear Classification Models
K-Nearest Neighbor
Decision Trees and Random Forests
Working with Support Vector Machines
The Neural Networks
Chapter 7: Validation and Optimization Techniques
Cross-Validation Techniques
Hyperparameter Optimization
Grid and Random Search
Chapter 8: Unsupervised Machine Learning with Clustering
K-Means Clustering
Hierarchal Clustering
DBSCAN
Chapter 9: Reduction of Dimensionality
The Principal Component Analysis
Linear Discriminant Analysis
Comparing PCA and LDA
Conclusion