Introduction
Due to the fact that blogging on data science topics is getting more and more extensive it is time to create a day archive. The goal is to cluster all publications in a meaningful way so that they can be found quickly when needed.
1 Roadmaps
2 Data Science
- Machine Learning Pipelines
- Visualizations
- How to connect Python to a local SQL Server
- SQL
- The Data Science Process (CRISP-DM)
- Randomized Search
- Grid Search
- Reshape a pandas DataFrame
- Data Management
- Data Manipulation
- Data type conversion
- Add new columns
- Data Wrangling
- The use of the groupby function
- Selection of columns per data type
3 Data pre-processing
3.1 General
3.2 for Regression
3.3 for Classification
4 Machine Learning
4.1 Regression Algorithms
4.2 Classification Algorithms
- Ensemble Modeling - Voting
- Stacking with Scikit-Learn
- Ensemble Modeling - Stacking
- Ensemble Modeling - XGBoost
- Ensemble Modeling - Boosting
- Ensemble Modeling - Bagging
- Introduction to KNN Classifier
- Introduction to Naive Bayes Classifier
- Introduction to Decision Trees
- Multinomial logistic regression
- Introduction to Perceptron Algorithm
- OvO and OvR Classifier
- Introduction to SGD Classifier
- Introduction to Support Vector Machines
- Introduction to Logistic Regression
4.3 Cluster Algorithms
4.4 Dimensionality Reduction Algorithms
5 Some Python Stuff
6 Some Intuitions
7 Analytics Fields
7.1 Marketing Analytics
7.2 Recommendation Systems
8 ETL
9 Time Series Analysis
10 Computer Vision
- Classification of Dog-Breeds using a pre-trained CNN model
- CNN with TFL and Fine-Tuning for Multi-Class Classification
- CNN with TFL and Fine-Tuning
- CNN with Transfer Learning for Multi-Class Classification
- CNN with Transfer Learning
- CNN for Multi-Class Classification
- Convolutional Neural Network
- Automate The Boring Stuff
11 Neural Networks
12 Natural Language Processing (NLP)
- Word Embedding with GENSIM for Text-Classification
- Text Vectorization
- Text Pre-Processing - All in One
- Text Pre-Processing VII (Special Cases)
- Text Pre-Processing VI (Word Removal)
- Text Pre-Processing V (Text Exploration)
- Text Pre-Processing IV (Single Character Removal)
- Text Pre-Processing III (POS, NER and Normalization)
- Text Pre-Processing II (Tokenization and Stop Words)
- Text Pre-Processing I (Text Cleaning)
- Text Manipulation