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data-science-learning-path
Data Science Learning Path - A complete guide to learn data science for beginners
https://github.com/data-folks/data-science-learning-path
- Basic Python
- Object-oriented Programming
- Intro to DBMS
- SQL Data Manipulation
- Git
- Github
- Shell Script
- Hackerrank
- Linear Algebra
- Calculus
- Descriptive Statistics
- Data Distributions
- Statistical Testing
- Exploratory Data Analysis
- Regression
- TOOLBOX: Pandas
- TOOLBOX: Numpy
- TOOLBOX: Matplotlib
- TOOLBOX: Seaborn
- K-NN (K-Nearest Neighbors)
- Naive Bayes
- Support Vector Machine
- Random Forest
- AdaBoost
- Gradient Boosting
- XGBoost
- CatBoost
- Bagging Classifier
- Voting Classifier
- Stacking Classifier
- TOOLBOX: Scikit Learn
- TOOLBOX: statsmodels
- CASE STUDY: House Pricing
- CASE STUDY: Titanic
- CASE STUDY: Credit Scoring
- K-Means Clustering
- DBSCAN
- Hierarchical Clustering
- Confusion Matrix
- Accuracy
- Precision
- Recall
- F Score
- Hamming Loss
- ROC (Receiver Operating Characteristic)
- ROC AUC (Area Under Curve)
- Top K Accuracy
- MAE
- MSE
- Silhouette Coefficient
- Activation Functions
- Linear Layer
- CNN (Convolutional Neural Networks)
- RNN (Recurrent Neural Networks)
- Optimization
- Loss Functions / Objective Functions
- Dropout
- Batchnorm
- Learning Rate Scheduler
- TOOLBOX: PyTorch
- TOOLBOX: Tensorflow
- TOOLBOX: Keras
- STUDY CASE: News Classification
- STUDY CASE: Sentiment Analysis
- STUDY CASE: Machine Translation
- Practical Deep Learning for Coders
- Dive Into Deep Learning
- Interpretable Machine Learning
- An Introduction to Statistical Learning with Applications in R
- Natural Language Processing with Python
Programming Languages