Learn Machine Learning Algorithms with Jax

Focused View

Tracy Renee

4:58:38

7 View

1. Introduction

1. Introduction.mp4

06:49

2.1 Intro to Jax Jupyter Notebook.html

2. Intro to Jax.mp4

22:18

2. Linear regression

1. Introduction to Linear Regression.mp4

03:32

2.1 Simple Linear Regression (mtcars).html

2. Simple Linear Regression (mtcars).mp4

18:00

3.1 Multiple linear regression (mtcars).html

3. Multiple linear regression (mtcars).mp4

11:29

4.1 Jax jit (California house prices).html

4. Jax jit (California house prices).mp4

15:20

3. Logistic regression

1. Introduction to Logistic Regression.mp4

04:03

2.1 Logistic regression (binary classification classification - breast cancer).html

2. Logistic regression (binary classification - breast cancer).mp4

14:37

3.1 Multinomial logistic regression (softmax - iris).html

3. Multinomial logistic regression (softmax - iris).mp4

09:01

4.1 Kaggle play 3.23 (calculate probabilities).html

4. Calculate probabilities (Kaggle play 3.23).mp4

15:42

4. Naive Bayes

1. Introduction to Naive Bayes.mp4

05:08

2.1 Naive Bayes classifier (wine).html

2. Naive Bayes Classifier (wine).mp4

10:40

5. Decision tree

1. Introduction to decision trees.mp4

05:06

2.1 Decision tree classifier (wine).html

2. Decision tree classifier (wine).mp4

12:23

6. Random Forest

1. Introduction to Random Forest.mp4

04:35

2.1 Random forest classifier (breast cancer).html

2. Random forest classifier (wine).mp4

11:08

7. K Nearest Neighbour

1. Introduction to KNN.mp4

05:10

2.1 KNN classifier (titanic).html

2. KNN classifier (titanic).mp4

16:39

8. Support Vector Machine

1. Introduction to SVM.mp4

03:47

2.1 SVM classdifier (titanic).html

2. SVM Classifier (titanic).mp4

11:12

9. Neural Networks

1. Introduction to neural networks.mp4

19:34

2.1 Jax perceptron(breast cancer).html

2. Perceptron (breast cancer).mp4

08:09

3.1 Regression neural network (Boston house prices).html

3. Regression neural network (Boston house prices).mp4

16:56

4.1 Jax binary classification neural network (breast cancer).html

4. Binary classifier neural network (breast cancer).mp4

14:27

5.1 Multiclass neural network (seeds).html

5. Multiclass neural network (seeds).mp4

15:58

6.1 Image classifier (pizza).html

6. Image classifier (pizza).mp4

16:55

Description

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.

Who is this for?

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

What You Need to Know?

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.

More details

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

User Reviews

Rating

average 0

Total votes0

Focused display

Machine Learning

Tracy Renee

Instructor's Courses

I have almost five decades experience in work, to include United States Air Force, the corporate sector, non profit sectors, and charities. I also have a BA in Computer Studies, a MSc in Finance, and have a Diploma in Accounting through the AAT. My hobbies include data science, creating content on social media, and writing.

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.