Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Source: from the Support Vector Machines chapter, here
Content
- The machine learning landscape
- End to end machine learning project
- Classification
- Training linear models
- Support vector machines
- Decision trees
- Ensemble learning and random forests
- Dimensionality reduction
- Unsupervised learning
- Neural nets with Keras
- Training deep neural networks
- Custom models and training with Tensorflow
- Loading and preprocessing data
- Deep computer vision with CNNs
- Processing sequences using rnns and CNNs
- NLP with rnns and attention
- Autoencoders and GANs
- Reinforcement learning
- Training and deploying at scale
You can access this material here. For other free tutorials (including from Berkeley, Harvard, Columbia, Google, Microsoft and so on), follow this link.
