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Data Science with Python: Machine Learning

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Beginner

Data Science with Python:

Machine Learning

This 20-hour Machine Learning with Python course covers all the basic machine learning methods and Python modules (especially Scikit-Learn) for implementing them. The five sessions cover: simple and multiple Linear regressions; classification methods including logistic regression, discriminant analysis and naive bayes, support vector machines (SVMs) and tree based methods; cross-validation and feature selection; regularization; principal component analysis (PCA) and clustering algorithms. After successfully completing of this course, you will be able to explain the principles of machine learning algorithms and implement these methods to analyze complex datasets and make predictions in Python.

* Tuition paid for part-time courses can be applied to the Data Science Bootcamps if admitted within 9 months.

Course Overview

This 20-hour Machine Learning with Python course covers all the basic machine learning methods and Python modules (especially Scikit-Learn) for implementing them. The five sessions cover: simple and multiple Linear regressions; classification methods including logistic regression, discriminant analysis and naive bayes, support vector machines (SVMs) and tree based methods; cross-validation and feature selection; regularization; principal component analysis (PCA) and clustering algorithms. After successfully completing of this course, you will be able to explain the principles of machine learning algorithms and implement these methods to analyze complex datasets and make predictions in Python.

Prerequisites

  • Knowledge of Python programming
  • Able to munge, analyze, and visualize data in Python

Certificate

Certificates are awarded at the end of the program at the satisfactory completion of the course. Students are evaluated on a pass/fail basis for their performance on the required homework and final project (where applicable). Students who complete 80% of the homework and attend a minimum of 85% of all classes are eligible for the certificate of completion.

Syllabus

Unit 1: Introduction and Regression

  • What is Machine Learning
  • Simple Linear Regression
  • Multiple Linear Regression
  • Numpy/Scikit-Learn Lab

Unit 2: Classification I

  • Logistic Regression
  • Discriminant Analysis
  • Naive Bayes
  • Supervised Learning Lab

Unit 3: Resampling and Model Selection

  • Cross-Validation
  • Bootstrap
  • Feature Selection
  • Model Selection and Regularization lab

Unit 4: Classification II

  • Support Vector Machines
  • Decision Trees
  • Bagging and Random Forests
  • Decision Tree and SVM Lab

Unit 5: Unsupervised Learning

  • Principal Component Analysis
  • Kmeans and Hierarchical Clustering
  • PCA and Clustering Lab
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