Hacker's Guide to Machine Learning with Python
Hacker's Guide to Machine Learning with Python
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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

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
  • 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
  • Build Your First Neural Network
    • Setup
    • Fashion data
    • Data Preprocessing
    • Create your first Neural Network
    • Train your model
    • Making predictions
    • Conclusion
  • Heart Disease Prediction
    • Patient Data
    • Data Preprocessing
    • The Model
    • Training
    • Predicting Heart Disease
    • Conclusion
  • Cryptocurrency price prediction using LSTMs
    • Data Overview
    • Time Series
    • Modeling
    • Predicting Bitcoin price
    • Conclusion
  • 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
  • Data Preprocessing
    • Feature Scaling
    • Handling Categorical Data
    • Adding New Features
    • Predicting Melbourne Housing Prices
    • 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

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