Python x ML Course
Aged 16+
24 hours
This course introduces key AI concepts and practical skills in cloud computing and Python. Learn to analyze data with Python, and master machine learning techniques using libraries like Scikit-Learn, PyTorch, and TensorFlow. Through hands-on projects, you will be ready to build and apply AI models in the real world.

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.

Course Outline:

  • 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


  • Knowledge of basic Statistic/Mathematics
  • Python x AI Course or equivalent

Software Requirements:

  • Jupyter Notebook

Hardware Requirements:

  • Notebook/Desktop with updated browser
  • Stable network


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