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Course content
Sections:
4
•
Activities:
1
•
Resources:
27
- Announcements
- 001 Introduction 5Sg8 gitir
- 002 Setup of the Anaconda Cloud Notebook 1yi6 gitir
- 003 Download and installation of the Anaconda Distribution optional m0mW gitir
- 004 The Conda Package Management System optional pdAy gitir
- 005 Regression Prediction and Supervised Learning Section Overview I mJWA gitir
- 006 The Traditional Simple Regression Model II 3lSN gitir
- 007 The Traditional Simple Regression Model III Q7aO gitir
- 008 Some practical and useful modelling concepts IV HvBR gitir
- 009 Some practical and useful modelling concepts V RVxr gitir
- 010 Linear Multiple Regression model VI W5AG gitir
- 011 Linear Multiple Regression model VII vdse gitir
- 012 Multivariate Polynomial Multiple Regression models VIII ufPI gitir
- 013 Multivariate Polynomial Multiple Regression models VIIII QGSX gitir
- 014 Regression Regularization Lasso and Ridge models X bBVU gitir
- 015 Decision Tree Regression models XI 5Zsx gitir
- 016 Random Forest Regression XII Aro8 gitir
- 017 Voting Regression XIII ckmZ gitir
- 018 Classification and Supervised Learning overview jtNl gitir
- 019 Logistic Regression Classifier EgTM gitir
- 020 The Naive Bayes Classifier hJNy gitir
- 021 The Decision Tree Classifier r1qT gitir
- 022 The Random Forest Classifier ygMO gitir
- 024 The Voting Classifier znMF gitir
- 025 Cluster Analysis an overview tha7 gitir
- 026 K Means Cluster Analysis and an introduction to auto updated K means algorithms 3te5 gitir
- 027 Density Based Spatial Clustering of Applications with Noise DBSCAN qi9F gitir
- 028 Four Hierarchical Clustering algorithms UmGS gitir