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
The Leanpub 60 Day 100% Happiness Guarantee
Within 60 days of purchase you can get a 100% refund on any Leanpub purchase, in two clicks.
Now, this is technically risky for us, since you'll have the book or course files either way. But we're so confident in our products and services, and in our authors and readers, that we're happy to offer a full money back guarantee for everything we sell.
You can only find out how good something is by trying it, and because of our 100% money back guarantee there's literally no risk to do so!
So, there's no reason not to click the Add to Cart button, is there?
See full terms...
Earn $8 on a $10 Purchase, and $16 on a $20 Purchase
We pay 80% royalties on purchases of $7.99 or more, and 80% royalties minus a 50 cent flat fee on purchases between $0.99 and $7.98. You earn $8 on a $10 sale, and $16 on a $20 sale. So, if we sell 5000 non-refunded copies of your book for $20, you'll earn $80,000.
(Yes, some authors have already earned much more than that on Leanpub.)
In fact, authors have earnedover $13 millionwriting, publishing and selling on Leanpub.
Learn more about writing on Leanpub
Free Updates. DRM Free.
If you buy a Leanpub book, you get free updates for as long as the author updates the book! Many authors use Leanpub to publish their books in-progress, while they are writing them. All readers get free updates, regardless of when they bought the book or how much they paid (including free).
Most Leanpub books are available in PDF (for computers) and EPUB (for phones, tablets and Kindle). The formats that a book includes are shown at the top right corner of this page.
Finally, Leanpub books don't have any DRM copy-protection nonsense, so you can easily read them on any supported device.
Learn more about Leanpub's ebook formats and where to read them
