Collection of Jupyter notebook created while going through LemBS Data Science school in 2019. Some assignments were not fully completed, so I left them out for a time being (including topics like NLP, Anomaly Detection, Dimensionality reduction and Recommender Systems).
I will upload them if I will ever get to bring them to satisfying result.
*.ipynb files are associated with JupyterLab and to comfortly work with them they do require to have following environment installed on your machine:
- Anaconda Navigator ver. 1.9.12 or higher
- JupyterLab ver. 0.35.4 or higher
- Python ver. 2.7.14 or higher.
NOTE: ipynb files can be opened in view mode inside your browser, but some features might be not available (e.g: ToC navigation), or may require prolonged load time.
File structure should be self-explanatory without any additional comments.
- Assignments
- 00 Linear Algebra using NumPy.ipynb
- 01 Linear Regression
- 01-00 Linear Regression Theory.ipynb
- 01-01 Linear Regression Univariable Solution.ipynb
- 01-02 Linear Regression Multivariable Solution.ipynb
- 02 Logistic Regression
- 02-00 Logistic Regression on MNIST dataset.ipynb
- 02-01 Logistic Regression using 1-vs-All and SoftMax on MNIST dataset.ipynb
- 03 CNN for Image Recognition
- 03-00 Using CNN to Classify Roadmarking.ipynb
- 04 KMeans and GMM Clusterization
- 04-00 Using KMeans and GMM Clusterization for Customer Segmentation.ipynb
- Coursework
- Part 1 Hearthstone Cards Collection EDA.ipynb
- Part 2 Q-Learning for FrozenLake-v0.ipynb
Coursework consists of two parts:
- Hearthstone Cards Collection EDA (Exploratory Data Analysis)
- Q-Learning for FrozenLake-v0, OpenAI Gym environment
