Gain a deep understanding of Supervised Learning techniques by studying the fundamentals and implementing them in NumPy.

Gain hands-on experience using popular Deep Learning frameworks such as Tensorflow 2 and Keras.

Section 1 - The Basics:

- Learn what Supervised Learning is, in the context of AI

- Learn the difference between Parametric and non-Parametric models

- Learn the fundamentals: Weights and biases, threshold functions and learning rates

- An introduction to the Vectorization technique to help speed up our self implemented code

- Learn to process real data: Feature Scaling, Splitting Data, One-hot Encoding and Handling missing data

- Classification vs Regression

Section 2 - Feedforward Networks:

- Learn about the Gradient Descent optimization algorithm.

- Implement the Logistic Regression model using NumPy

- Implement a Feedforward Network using NumPy

- Learn the difference between Multi-task and Multi-class Classification

- Understand the Vanishing Gradient Problem

- Overfitting

- Batching and various Optimizers (Momentum, RMSprop, Adam)

Section 3 - Convolutional Neural Networks:

- Fundamentals such as filters, padding, strides and reshaping

- Implement a Convolutional Neural Network using NumPy

- Introduction to Tensorfow 2 and Keras

- Data Augmentation to reduce overfitting

- Understand and implement Transfer Learning to require less data

- Analyse Object Classification models using Occlusion Sensitivity

- Generate Art using Style Transfer

- One-Shot Learning for Face Verification and Face Recognition

- Perform Object Detection for Blood Stream images

Section 4 - Sequential Data

- Understand Sequential Data and when data should be modeled as Sequential Data

- Implement a Recurrent Neural Network using NumPy

- Implement LSTM and GRUs in Tensorflow 2/Keras

- Sentiment Classification from the basics to the more advanced techniques

- Understand Word Embeddings

- Generate text similar to Romeo and Juliet

- Implement an Attention Model using Tensorflow 2/Keras