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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

Venelin Valkov

"What I cannot create, I do not understand" - Richard Feynman

This book will guide you on your journey to deeper Machine Learning understanding by developing algorithms in Python from scratch! Learn why and when Machine learning is the right tool for the job and how to improve low performing models!

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About

About

About the Book

Hello!

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!

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Machine LearningArtificial IntelligenceData SciencePythonComputer ScienceSciencesComputers and Programming

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Author

About the Author

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!

Contents

Table of Contents

Machine Learning fundamentals

  1. Sick of being a lamer?
  2. What is Machine Learning?
  3. What Learning Algorithms are made of?
  4. Making predictions
  5. Tools of the trade
  6. Ready to start?

Smart Discounts with Logistic Regression

  1. The Data
  2. Making decisions with Logistic regression
  3. The Logistic Regression model
  4. How can we find the parameters for our model?
  5. Bonus — building your own LogisticRegressor
  6. Conclusion

Predicting House Prices with Linear Regression

  1. The Data
  2. Load the data
  3. Exploration — getting a feel for our data
  4. Predicting the sale price
  5. Loss function
  6. Multivariable Linear Regression
  7. Conclusion

Building a Decision Tree

  1. The Data
  2. Decision Trees
  3. Data Preprocessing
  4. Cost function
  5. Using a prebuild Decision Tree model
  6. Building your own Decision Tree
  7. Evaluation
  8. Sending your predictions to Kaggle
  9. Conclusion
  10. Acknowledgments

Color palette extraction with K-means clustering

  1. Unsupervised Learning
  2. The Data
  3. What is K-Means Clustering?
  4. Data Preprocessing
  5. Implementing K-Means clustering
  6. Evaluation
  7. Conclusion

Movie review sentiment analysis with Naive Bayes

  1. Dealing with Text
  2. Naive Bayes
  3. Implementing Multinomial Naive Bayes
  4. Predicting sentiment
  5. Conclusion

Music artist Recommender System using Stochastic Gradient Descent

  1. User Ratings
  2. Recommender Systems
  3. Recommending music artists
  4. Conclusion

Fashion product image classification using Neural Networks

  1. Image Data
  2. Neural Networks
  3. Classifying Images
  4. Improving the accuracy
  5. Conclusion

Build a taxi driving agent in a post-apocalyptic world using Reinforcement Learning

  1. Reinforcement Learning
  2. Driving in a post-apocalyptic world
  3. Evaluation
  4. Conclusion

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