Python-Machine-Learning-Deep-Learning-AI

Language: Python

Level:

Machine Learning
Deep Learning
AI

Part 1 - Introduction to AI, Machine Learning: the unified framework TEFPA

0/ From intelligence & learning to AI & ML. The TEFPA framework.

Introduction to list of final projects

1/ Predictions: introduction to linear models

Plot Decision Boundary of Neural Network for Spiral dataset

Spiral Scatter Plot for 3 Classes

Train the Deep Neural Network for Fashion Mnist dataset (epochs = 50)

Train the Neural Network for Spiral dataset

Visualize Fashion Mnist dataset

Evaluate and Inference the visualized Fashion Mnist dataset trained by Deep Neural Network

2/ Predictions: introduction to nonlinear models

Part 2 - Key issues in AI/ML

3/ Representations: feature extraction, embedding coordinates, and nonlinear transformations.

4/ Evaluation: common metrics and loss functions

5/ Search: gradient descent and variants

Part 3 - More on key issues in AI/ML

6/ More on search: overfitting, underfitting, regularization, and generalization

7/ More on representation: CNNs for grid-like data

8/ More on representation: RNNs for time-series-like data

Part 4 - Data Engineering"

9/ Decision trees & Ensemble methods in practical use.

10/ Unsupervised learning: Kmeans clustering

11/ Data acquisition, cleaning, annotation.

Data exploration: visualization, statistics, imbalance, patterns, etc.

12/ Midterm exam + Projects open discussion

Part 5 - Introduction to Sequential Decision Making"

13/ Sequential decision making: classical MDP planning

14/ Sequential decision making: Tabular Q-learning & DQN

15/Interactive decision making: Contextual & multi-armed bandits

Part 6 - Introduction to Computer Vision

16/ DeepCNN: AlexNet, VGGNet, ResNet, MobileNet, etc.

17/ Computer vision applications: image classification, segmentation, etc.

Review

18/ Theory reviews + Final projects checkpoint + implementation guide

Part 7 - Introduction to NLP

19/ Sequence modeling: LSTM /GRU & language models

20/ NLP applications: sentiment classification, language generation, etc.

daiphuongngo / Python-Machine-Learning-Deep-Learning-AI