https://github.com/vkosuri/jason-ml-course-notes

Jason brownlee machine learning mini course notes and examples
https://github.com/vkosuri/jason-ml-course-notes

jason ml-course ml-notes

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Jason brownlee machine learning mini course notes and examples

• Host: GitHub
• URL: https://github.com/vkosuri/jason-ml-course-notes
• Owner: vkosuri
• License: mit
• Created: 2018-02-05T16:36:16.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2018-02-06T01:37:10.000Z (almost 7 years ago)
• Last Synced: 2024-10-24T19:37:49.331Z (2 months ago)
• Topics: jason, ml-course, ml-notes
• Homepage: http://jason-ml-course-notes.rtfd.io/
• Size: 13.7 KB
• Stars: 5
• Watchers: 3
• Forks: 9
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        

Jason brownlee machine learning mini course notes and examples are gathered through subscribed emails from  https://machinelearningmastery.com

[![Documentation Status](https://readthedocs.org/projects/jason-ml-course-notes/badge/?version=latest)](http://jason-ml-course-notes.readthedocs.io/en/latest/?badge=latest)

## Mini Courses

1. Applied Machine Learning With Weka

2. XGBoost With Python Mini-Course

3. Deep Learning With Python Mini-Course

## General Information

1. Be a machine learning engineer

2. Better results by structuring your problem

3. Catalog of machine learning algorithms

4. Combine predictions with ensemble methods

5. Deep learning for sequence prediction

6. Kick your math envy

7. Machine learning has a trap

8. Machine learning without a single line of code

9. Nonlinear algorithms for when you need performance

10. Practical machine learning problems

11. Related fields of study

12. Standard machine learning terms

13. Start with simple linear algorithms

14. Visualize your data with Pandas

15. What is deep learning?

## Machine Learning Algorithms Lessons

1. How To Talk About Data in Machine Learning

2. The Principle That Underpins All Algorithms

3. Parametric and Nonparametric Algorithms

4. Bias, Variance and the Trade-off

5. Linear Regression Algorithm

6. Logistic Regression Algorithm

7. Linear Discriminant Analysis Algorithm

8. Classification and Regression Trees

9. Naive Bayes Algorithm

10. K-Nearest Neighbors Algorithm

11. Learning Vector Quantization

12. Support Vector Machines

13. Bagging and Random Forest

14. Boosting and AdaBoost

## Newsletters

1. 4 Prediction Models and 3 Types of Gradient Descent

2. Attentional LSTMs, BPTT in Keras, and Long Sequences

3. AWS commands, Keras metrics and LSTM tests

4. Deep Learning for Natural Language Processing Courses

5. Differencing, One Hot Encoding and Validation Sets

6. Get great results by being systematic

7. Multivariate Forecasting, Mini-Course and Stacked LSTMs

8. RNNs, Adam Optimization and Data Scaling

9. Sequence Prediction, RNN Unrolling and NLP Books

10. Training Data Size, Hyperparameters and Why One Hot Encode

Author:

![jason](https://3qeqpr26caki16dnhd19sv6by6v-wpengine.netdna-ssl.com/wp-content/uploads/2013/11/jason_brownlee-221x300.jpg)

IMAGE CREDITS: https://machinelearningmastery.com

About him https://machinelearningmastery.com/about/

> Most of the content I have used from Jason Brownlee emails which I have subscribed through https://machinelearningmastery.com.

> And also you will find most of the stuff from here also https://machinelearningmastery.com/start-here/#faq