notebooks

My notes of machine learning/deep learning algorithms, mathematics and statistics, and other computer science topics.

Machine Learning Algorithms

Regression

• Linear Regression (Link)
• TODO: Polynomial Regression (Link)

Classification

• Logistic Regression (Link)
• K Nearest Neighbors (Link)
• TODO: Naive Bayes (Link)
• Decision Trees (Link)
• Random Forests (Link)
• TODO: Gradient Boosted Trees (Link)
• Support Vector Machines (Link)
• TODO: AdaBoost (Link)
• TODOL XGBoost (Link)

Clustering

• K-Means (Link)
• Dimensionality Reduction (Link)

Other

• Time Series Analysis (Link)

Deep Learning Algorithms

• Artificial/Forward Feed Neural Networks (Link)
• Convolutional Neural Networks (Link)
• Recurrent Neural Networks (Link)
• TODO: Autoencoders (Link)
• TODO: Generative Adversarial Networks (Link)

Mathematics & Statistics

• Calculus (Link)
• Linear Algebra (Link)
• Descriptive Statistics (Link)
• Probability (Link)
• Sampling & Distributions (Link)
• Linear Regression (Link)
• Inferential Statistics (Link)
• Bayesian Statistics (Link)

Data Structures

• Data Structures (Link)

Data Analysis & Visualization

• Python (Link)
• Pandas Performance (Link)
• Seaborn (Link)
• SQL (Link) - from Pierian Data: Complete SQL Bootcamp, (Link)
• Easy EDA & Dash (Link)
• PyDataPDX Lunch & Learn Sessions
  • Data Visualization 101 (Link)
  • Data Visualization 102 (Link)
  • Data Visualization 103 (Link)

My Data Science Learning Path

Reading List

1. Practical Statistics for Data Scientists.
2. All of Statistics.
3. An introduction to Statistical Learning.
4. The Elements of Statistical Learning.
5. Think Bayes.
6. Math for Programmers.
7. Fundamentals of Data Visualization.
8. SciPy and NumPy.
9. Feature Engineering for Machine Learning.
10. Hands-On Machine Learning with Scikit-Learn, Tensorflow & Keras.
11. Machine Learning Pocket Reference.
12. Machine Learning with Tensorflow.
13. Machine Learning Systems.
14. Practical Time Series Analysis.
15. Deep Learning for Coders with fastai & PyTorch.
16. Deep Learning with PyTorch.
17. Deep Reinforcement Learning in Action.
18. GANs in Action.
19. Building Machine Learning Powered Applications.
20. Building Machine Learning Pipelines.
21. Data Science on AWS.
22. Introducing MLOps.

Sources

1. ArXiv (ArXiv.org)
2. Papers With Code (paperswithcode.com)

Jonathan Hildenbrand, https://github.com/jonathanhild