Hands-On Machine Learning from Scratch
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Hands-On Machine Learning from Scratch

Develop a Deeper Understanding of Machine Learning Models by Implementing Them from Scratch in Python

About the Book


Recent developments in the field of Artificial Intelligence made Machine Learning methods usable and efficient in practice. Tools like PyTorch, TensorFlow, Scikit-Learn make it easy to build your first model.

Unfortunately, reading through a Machine Learning/Deep Learning tutorial showing how to use a library will leave you asking - why does it work, when can I use that and how can I improve it? Now what? This book will help you answer those questions!

What is in for you?

- Step-by-step guide on how to approach, visualize and solve data science problems

- Learn why and when Machine Learning is the right tool for the job

- Learn how to process CSV, text, and image data

- Develop Linear Regression, Logistic Regression, Decision Tree, Neural Network, and other models. Use your models to solve real-world problems.

- Find how to improve low performing models

- Learn how to use Python libraries like NumPy, Pandas, Seaborn and more

- Complete source code (notebooks) that works and runs in the cloud

Welcome to the amazing world of Machine Learning!

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

  • Machine Learning fundamentals
    • Sick of being a lamer?
    • What is Machine Learning?
    • What Learning Algorithms are made of?
    • Making predictions
    • Tools of the trade
    • Ready to start?
  • Smart Discounts with Logistic Regression
    • The Data
    • Making decisions with Logistic regression
    • The Logistic Regression model
    • How can we find the parameters for our model?
    • Bonus — building your own LogisticRegressor
    • Conclusion
  • Predicting House Prices with Linear Regression
    • The Data
    • Load the data
    • Exploration — getting a feel for our data
    • Predicting the sale price
    • Loss function
    • Multivariable Linear Regression
    • Conclusion
  • Building a Decision Tree
    • The Data
    • Decision Trees
    • Data Preprocessing
    • Cost function
    • Using a prebuild Decision Tree model
    • Building your own Decision Tree
    • Evaluation
    • Sending your predictions to Kaggle
    • Conclusion
    • Acknowledgments
  • Color palette extraction with K-means clustering
    • Unsupervised Learning
    • The Data
    • What is K-Means Clustering?
    • Data Preprocessing
    • Implementing K-Means clustering
    • Evaluation
    • Conclusion
  • Movie review sentiment analysis with Naive Bayes
    • Dealing with Text
    • Naive Bayes
    • Implementing Multinomial Naive Bayes
    • Predicting sentiment
    • Conclusion
  • Music artist Recommender System using Stochastic Gradient Descent
    • User Ratings
    • Recommender Systems
    • Recommending music artists
    • Conclusion
  • Fashion product image classification using Neural Networks
    • Image Data
    • Neural Networks
    • Classifying Images
    • Improving the accuracy
    • Conclusion
  • Build a taxi driving agent in a post-apocalyptic world using Reinforcement Learning
    • Reinforcement Learning
    • Driving in a post-apocalyptic world
    • Evaluation
    • Conclusion

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