Concepts, Tools, and Techniques to Build Intelligent Systems
Part I. The fundamentals of Machine Learning
- The Machine Learning Landscape
- End-to-End Machine Learning Project
- Classification
- training Models
- Support Vector Machines (SVM)
- Decision Trees
- Ensemble learning and random forests
- dimensionality reduction
- unsupervised learning techniques
Part II. Neural Networks and Deep Learning
- Introduction to Artificial Neural Networks with Keras
- Training Deep Neural Networks
- Custom Models and Training with TensorFlow
- Loading and Preprocessing Data with TensorFlow
- Deep Computer Vision using Convolutional Neural Networks (CNNs)
- Processing Sequences using RNNs and CNNs
- Natural Language Processing (NPL) with RNNs and Attention
- Autoencoders, GANs, and Diffusion Models
- Reinforcement Learning
- Training and Deploying TensorFlow Models at Scale