Learn Machine Learning Algorithms with Jax

Learn Machine Learning Algorithms with Jax, available at $19.99, has an average rating of 1, with 26 lectures, based on 1 reviews, and has 36 subscribers.

You will learn about 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. This course is ideal for individuals who are This course is for persons interested in expanding their knowledge of Python's Jax library, machine learning, and data science. It is particularly useful for This course is for persons interested in expanding their knowledge of Python's Jax library, machine learning, and data science.

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Summary

Title: Learn Machine Learning Algorithms with Jax

Price: $19.99

Average Rating: 1

Number of Lectures: 26

Number of Published Lectures: 26

Number of Curriculum Items: 26

Number of Published Curriculum Objects: 26

Original Price: £22.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

This course is for persons interested in expanding their knowledge of Python's Jax library, machine learning, and data science.

Target Audiences

This course is for persons interested in expanding their knowledge of Python's Jax library, machine learning, and data science.

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 regression

2. Logistic regression

3. Naive bayes

4. Decision tree

5. Random forest

6. K nearest neighbour

7. Support vector machine

8. Neural networks

In 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 program

2. Load the appropriate dataset into the program from Google Colab, GitHub, or sklearn

3. Preprocess the data if necessary

4. Remove outliers if appropriate

5. Remove highly correlated features if appropriate

6. Standardise the data if needed

7. Define dependent and independent variables

8. Split the dataset into training, validating, and testing sets, whichever is appropriate

9. Define the Jax model

10. Compare the Jax model with its sklearn equivalent

11. Obtain predictions and test their accuracy or error, whever is appropriate.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Intro to Jax

Chapter 2: Linear regression

Lecture 1: Introduction to Linear Regression

Lecture 2: Simple Linear Regression (mtcars)

Lecture 3: Multiple linear regression (mtcars)

Lecture 4: Jax jit (California house prices)

Chapter 3: Logistic regression

Lecture 1: Introduction to Logistic Regression

Lecture 2: Logistic regression (binary classification – breast cancer)

Lecture 3: Multinomial logistic regression (softmax – iris)

Lecture 4: Calculate probabilities (Kaggle play 3.23)

Chapter 4: Naive Bayes

Lecture 1: Introduction to Naive Bayes

Lecture 2: Naive Bayes Classifier (wine)

Chapter 5: Decision tree

Lecture 1: Introduction to decision trees

Lecture 2: Decision tree classifier (wine)

Chapter 6: Random Forest

Lecture 1: Introduction to Random Forest

Lecture 2: Random forest classifier (wine)

Chapter 7: K Nearest Neighbour

Lecture 1: Introduction to KNN

Lecture 2: KNN classifier (titanic)

Chapter 8: Support Vector Machine

Lecture 1: Introduction to SVM

Lecture 2: SVM Classifier (titanic)

Chapter 9: Neural Networks

Lecture 1: Introduction to neural networks

Lecture 2: Perceptron (breast cancer)

Lecture 3: Regression neural network (Boston house prices)

Lecture 4: Binary classifier neural network (breast cancer)

Lecture 5: Multiclass neural network (seeds)

Lecture 6: Image classifier (pizza)

Instructors

Tracy Renee
Data Scientist

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1 stars: 1 votes
2 stars: 0 votes
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5 stars: 0 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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