Python x ML

Learning objectives

Understand and apply the fundamental concepts of AI, including data preparation, statistical analysis, and machine learning techniques such as regression, classification, and clustering.
Gain proficiency in Python for AI, learning how to manipulate data using libraries like NumPy and Pandas, and refresh core programming skills with practical Python notebook exercises.
Master deep learning principles by training neural networks with PyTorch or TensorFlow and leverage transfer learning to build advanced image classification models.

Hello AI | Understand what is AI and how to use cloud computer

Python refresher | Refresh Python knowledge: Variable and operators, Conditional statement, Loop statement, Function, Class & Object

Data in AI | Understand the basic concept of data in AI. Learn Basic of data manipulation libraries (NumPy and Pandas)

Statistic in AI | Understand the basic statistic in AI with statistic. Data analysis with Scatter Plots, Line Graphs, Bar Charts, and Histograms.

Regression | Learn how to use regression to predict numeric values and understand how various parameters can optimise prediction accuracy.

Classification | Learn how to categorize items into classes in machine learning and how to evaluate classification model.

Clustering | Learn how to apply clustering model and understand its principles in grouping similar items into clusters in machine learning.

Neural network | Learn basic principles of deep learning and train a deep neural network (DNN) using PyTorch or Tensorflow

Transfer learning | Use transfer learning to train a convolutional neural network (CNN) with PyTorch or Tensorflow