EricSchles / Datascience_book
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Programming Probabilistically: An introduction to the world of data science
By
Eric Schles
Hello and welcome to my book! You'll find the following sections:
- Descriptive Statistics and Hypothesis testing
- Applied Statistical Tests - A/B testing
- Regression Introduction
- Classification Introduction
- Information Theory, Entropy and Tree Models
- Neural Network Models
Each section covers about 4 to 5 chapters worth of materail broken out into:
- Basics
- Mathematical Intuition
- Implementation
- Typical API
- Advanced Use Cases
Sections to come:
- Reinforcement Learning
- Engineering for Data Science
- Text Processing
- Image Processing
- Support Vector Machines
- Genetic Algorithms
- neural network optimizers
- Recommender Systems
- A/B testing and other related workflows
- SQL best practice
- Timeseries Forecasting and Analysis
- Geospatial Analysis
- Geospatial and Timeseries forecasting
- Video Processing
- Building Data Dashboards
- Working With Search
- Building An OCR System
- Advanced Python Usage
- Active Learning
- Recurrent Neural Networks
- Convolutional Neural Networks
- Capsule Networks
- Adversarial Machine Learning
- Open World - in distribution out of distribution
- Bayesian Machine Learning
- Graph Based Neural Networks
- Monitoring
- Working with Spark
- Working with Streaming Data
- Ensembling - scikit learn ensembling strategies
- Random Forests
- Additive models:
- Gradient boosted trees
- splines
- General Additive Models
- adaboost
- explainability metrics
- litany of examples
- showing when and how they can fail
- Metrics
- Hyper parameter tunning
- Randomness in your models
- Counterfactual examples
- testing in machine learning applications
To Dos
- fix Decision Tree Implementation
- add SVM chapter
- add dimensionality reduction chapter
- add clustering chapter
- add RNN chapter
- add conv net chapter
- discuss attention
- create engineering productionization chapter
- hypothesis test as a ticket within engineering scrum context
- reproducibility of results
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