Published 1/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.99 GB | Duration: 4h 58m
to develop your data science skills
What you’ll learn
Students will learn about Python’s Jax library.
Students will learn how to code supervised classification machine learning algorithms in Jax.
Students will learn how to code supervised regression machine learning algorithms in Jax.
Students will learn how to code neural networks in Jax.
Requirements
Students should have a basic understanding of Python before taking this course.
Students should have taken my free Udemy courses, such as:- Introduction to Python programming; Theoretical concepts of machine learning; and Practicalities involved in exploratory data analysis.
Description
Jax is a Python library developed by Google in 2018 and is set to overtake Google’s other Python library, Tensorflow, for research purposes. There is significantly less code available in Jax than there is in Tensorflow, which is why I have decided to develop a course in Jax. Jax has been written very similar to the numpy API, but there are a few differences that will be covered in the course.The beginning of the course will cover an introduction to Jax, discussing some of the code that will be in the 16 Jupyter Notebooks that will be presented. An introduction to machine learning algorithms will be vovered in eight sections. The machine learning algorithms that will be introduced, with the code covered in depth are:-1. Linear regression2. Logistic regression3. Naive bayes4. Decision tree5. Random forest6. K nearest neighbour7. Support vector machine8. Neural networksIn order for the machine learning algorithms to be efficiently presented, they must be included in a machine learning project, to include:-1. Import Jax and other Python libraries into the program2. Load the appropriate dataset into the program from Google Colab, GitHub, or sklearn3. Preprocess the data if necessary4. Remove outliers if appropriate5. Remove highly correlated features if appropriate6. Standardise the data if needed7. Define dependent and independent variables8. Split the dataset into training, validating, and testing sets, whichever is appropriate9. Define the Jax model10. Compare the Jax model with its sklearn equivalent11. Obtain predictions and test their accuracy or error, whever is appropriate.
