Hacker's Guide to Machine Learning with Python
This book is 70% complete
Last updated on 2019-11-02
About the Book
Deep Learning has revolutionized the Machine Learning field. Python tools like Scikit-Learn, Pandas, TensorFlow, and Keras allows you to develop applications powered by Machine Learning.
This book is written for you, the Machine Learning practitioner. Every chapter describes a problem and a solution that you'll encounter in your Machine Learning Journey.
- Get started with TensorFlow 2 and Keras
- Deploy a complete Keras Deep Learning project to production with Flask
- Use the Keras library to train Deep Neural Networks
- Learn how to handle imbalanced datasets
- Hyperparameter tuning with Keras Tuner
- Learn how to debug your model when it is underfitting or overfitting
- Predict cryptocurrency prices using LSTMs
TensorFlow 2 and Keras - Quick Start Guide
- Simple Linear Regression Model
- Simple Neural Network Model
- Save/Restore Model
End to End Machine Learning Project
- Define objective/goal
- Load data
- Data exploration
- Prepare the data
- Build your model
- Save the model
- Build REST API
- Deploy to production
Build Your First Neural Network
- Fashion data
- Data Preprocessing
- Create your first Neural Network
- Train your model
- Making predictions
Heart Disease Prediction
- Patient Data
- Data Preprocessing
- The Model
- Predicting Heart Disease
Cryptocurrency price prediction using LSTMs
- Data Overview
- Time Series
- Predicting Bitcoin price
Handling Imbalanced Datasets
- Baseline model
- Using the correct metrics
- Weighted model
- Resampling techniques
Fixing Underfitting and Overfitting Models
- What is a Hyperparameter?
- When to do Hyperparameter Tuning?
- Common strategies
- Finding Hyperparameters
- Feature Scaling
- Handling Categorical Data
- Adding New Features
- Predicting Melbourne Housing Prices
Fundamental Machine Learning Algorithms
- What Makes a Learning Algorithm?
- Our Data
- Linear Regression
- Logistic Regression
- k-Nearest Neighbors
- Naive Bayes
- Decision Trees
- Support Vector Machines (SVM)
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