Skip to main content
Back to top
Ctrl
+
K
Tutorials on Data Science with Python
Welcome to D. Akman’s Tutorials on Data Science with Python!
Python Basics (PB) Tutorial Series
Introduction to Jupyter Notebooks
Notebook Markdown Tutorial
Introduction to Python Programming
NumPy
Pandas
Matplotlib
Seaborn
Python vs. R
Statistics Tutorials/ OpenIntro Labs for Python
Introduction to data
Probability
Normal distribution
Foundations for statistical inference - Confidence intervals
Foundations for statistical inference - Sampling distributions
Inference for categorical data
Inference for numerical data
Introduction to linear regression
Multiple linear regression
Predicting Age in Census Data
Machine Learning Tutorials with Scikit-Learn
Data Preparation for Predictive Modeling
SK Part 0: Introduction to Predictive Modeling with Python and Scikit-Learn
SK Part 1: Basic Modeling
SK Part 2: Feature Selection and Ranking
SK Part 3: Model Evaluation
SK Part 4: Cross-Validation and Hyper-parameter Tuning
SK Part 5: Pipelines, Statistical Model Comparison, and Model Deployment
SK Part 6: K-Means Clustering
SK Part 7: Neural Networks
Light GBM & Parameter Tuning with Optuna
Forecasting Fundamentals with Python & Facebook Prophet
Predicting Income Status
Predicting Optimal Machine Maintenance Cycle
Hotel Prediction with Hybrid Collaborative Filtering with SVD
Information Gain Computation in Python
Index