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
  • Share this book

  • Categories

    • Machine Learning
    • Data Science
    • Artificial Intelligence
    • Business Analysis
    • Python
    • Computer Science
  • Feedback

    Contact the Author(s)

About the Author

Venelin Valkov
Venelin Valkov

Hello there!

I'm excited to welcome you to the wonderful world of Machine Learning! My name is Venelin and I'm thrilled to be your guide on this journey.

With over 5 years of experience in Machine Learning and 10+ years in Software Development, I have worked on several exciting projects, ranging from sentiment analysis, price prediction, and document text extraction to training Object Detection algorithms. Currently, my focus is on Large Language Models such as ChatGPT, which has been a fascinating area of exploration for me.

I look forward to sharing my knowledge and experience with you and helping you discover the limitless possibilities that Machine Learning offers. Let's embark on this adventure together!

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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