Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)
Hacker's Guide to Machine Learning with Python 🐱💻
This book brings the fundamentals of Machine Learning to you, using tools and techniques used to solve real-world problems in Computer Vision, Natural Language Processing, and Time Series analysis. The skills taught in this book will lay the foundation for you to advance your journey to Machine Learning Mastery.
📖 Read for FREE
The whole book can be read using the links below. Each part contains a notebook that you can find in this repository.
- TensorFlow 2 and Keras - Quick Start Guide
- Build your first Neural Network
- Heart Disease Prediction
- Cryptocurrency price prediction using LSTMs
- Practical Guide to Handling Imbalanced Datasets
- Hacker's Guide to Fixing Underfitting and Overfitting Models
- Hacker's Guide to Hyperparameter Tuning
- Deploy a Keras Deep Learning Project to Production with Flask
- Hacker's Guide to Data Preparation for Machine Learning
- Hacker's Guide to Fundamental Machine Learning Algorithms
- Time Series Forecasting with LSTMs
- Time Series Demand Prediction with LSTMs
- Time Series Classification for Human Activity Recognition with LSTMs
- Time Series Anomaly Detection with LSTM Autoencoders
- Object Detection on Custom Dataset
- Image Data Augmentation
- Sentiment Analysis
- Intent Recognition with BERT using Keras and TensorFlow 2
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