Hacker's Guide to Machine Learning with Python
Hacker's Guide to Machine Learning with Python
Hands-on guide to solving real-world Machine Learning problems with Scikit-Learn, TensorFlow 2, and Keras
About the Book
Deep Learning has revolutionized the Machine Learning field. Python tools like Scikit-Learn, Pandas, TensorFlow, and Keras allows you to develop state-of-the-art 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
- Learn about fundamental/classical Machine Learning algorithms
- Hyperparameter tuning with Keras Tuner
- Learn how to debug your model when it is underfitting or overfitting
- Predict cryptocurrency prices using LSTMs
- Detect anomalies in Time Series data
- Detect objects in images
- Recognize user intents from raw text data
About the Author
Table of Contents
-
TensorFlow 2 and Keras - Quick Start Guide
- Setup
- Tensors
- Simple Linear Regression Model
- Simple Neural Network Model
- Save/Restore Model
- Conclusion
- References
-
Build Your First Neural Network
- Setup
- Fashion data
- Data Preprocessing
- Create your first Neural Network
- Train your model
- Making predictions
- Conclusion
-
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
- Conclusion
- References
-
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)
- Conclusion
- References
-
Data Preprocessing
- Feature Scaling
- Handling Categorical Data
- Adding New Features
- Predicting Melbourne Housing Prices
- Conclusion
- References
-
Handling Imbalanced Datasets
- Data
- Baseline model
- Using the correct metrics
- Weighted model
- Resampling techniques
- Conclusion
- References
-
Fixing Underfitting and Overfitting Models
- Data
- Underfitting
- Overfitting
- Conclusion
- References
-
Hyperparameter Tuning
- What is a Hyperparameter?
- When to do Hyperparameter Tuning?
- Common strategies
- Finding Hyperparameters
- Conclusion
- References
-
Heart Disease Prediction
- Patient Data
- Data Preprocessing
- The Model
- Training
- Predicting Heart Disease
- Conclusion
-
Time Series Forecasting
- Time Series
- Recurrent Neural Networks
- Time Series Prediction with LSTMs
- Conclusion
- References
-
Cryptocurrency price prediction using LSTMs
- Data Overview
- Time Series
- Modeling
- Predicting Bitcoin price
- Conclusion
-
Demand Prediction for Multivariate Time Series with LSTMs
- Data
- Feature Engineering
- Exploration
- Preprocessing
- Predicting Demand
- Evaluation
- Conclusion
- References
-
Time Series Classification for Human Activity Recognition with LSTMs in Keras
- Human Activity Data
- Classifying Human Activity
- Evaluation
- Conclusion
- References
-
Time Series Anomaly Detection with LSTM Autoencoders using Keras in Python
- Anomaly Detection
- LSTM Autoencoders
- S&P 500 Index Data
- LSTM Autoencoder in Keras
- Finding Anomalies
- Conclusion
- References
-
Object Detection
- Object Detection
- RetinaNet
- Preparing the Dataset
- Detecting Vehicle Plates
- Conclusion
- References
-
Image Data Augmentation
- Tools for Image Augmentation
- Augmenting Scanned Documents
- Creating Augmented Dataset
- Conclusion
- References
-
Sentiment Analysis
- Universal Sentence Encoder
- Hotel Reviews Data
- Sentiment Analysis
- Conclusion
- References
-
Intent Recognition with BERT
- Data
- BERT
- Intent Recognition with BERT
- Conclusion
- References
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