Hacker's Guide to Machine Learning with Python
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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

Venelin Valkov
Venelin Valkov

Hello everybody,

My name is Venelin and I am thrilled to invite you on a journey through the amazing world of Machine Learning. 

I've been working on quite a lot of software projects in the past 10+ years, started my PhD program in Bayesian Statistics and been fascinated by Machine Learning in the past 5+ years (including some industry experience).

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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