Applied Machine Learning in Python

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods.

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Key Points About This Course

Duration: X Days
Time: 9.00am-5.00pm
Public Class Fee: RM X,XXX.XX
Virtual Class Fee: RM X,XXX.XX
HRDF Claimable

Course Overview

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.

This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

What You Will Learn

  • Describe how machine learning is different than descriptive statistics
  • Create and evaluate data clusters
  • Explain different approaches for creating predictive models
  • Build features that meet analysis needs

Skills You Will Gain

  • Python Programming
  • Machine Learning (ML) Algorithms
  • Machine Learning
  • Scikit-Learn

Course Content

Part 1: Fundamentals of Machine Learning – Intro to SciKit Learn

This module introduces basic machine learning concepts, tasks, and workflow using an example classification problem based on the K-nearest neighbors method, and implemented using the scikit-learn library.

  • Introduction
  • Key Concepts in Machine Learning
  • Python Tools for Machine Learning
  • An Example Machine Learning Problem
  • Examining the Data
  • K-Nearest Neighbors Classification

Part 2: Supervised Machine Learning – Part 1

This module delves into a wider variety of supervised learning methods for both classification and regression, learning about the connection between model complexity and generalization performance, the importance of proper feature scaling, and how to control model complexity by applying techniques like regularization to avoid overfitting.

  • Introduction to Supervised Machine Learning
  • Overfitting and Underfitting
  • Supervised Learning: Datasets
  • K-Nearest Neighbors: Classification and Regression
  • Linear Regression: Least-Squares
  • Linear Regression: Ridge, Lasso, and Polynomial Regression
  • Logistic Regression
  • Linear Classifiers: Support Vector Machines
  • Multi-Class Classification
  • Kernelized Support Vector Machines
  • Cross-Validation
  • Decision Trees

Part 3: Evaluation

This module covers evaluation and model selection methods that you can use to help understand and optimize the performance of your machine learning models.

  • Model Evaluation & Selection
  • Confusion Matrices & Basic Evaluation Metrics
  • Classifier Decision Functions
  • Precision-recall and ROC curves
  • Multi-Class Evaluation
  • Regression Evaluation
  • Model Selection: Optimizing Classifiers for Different Evaluation Metrics

Part 4: Applied Visualizations

This module covers more advanced supervised learning methods that include ensembles of trees (random forests, gradient boosted trees), and neural networks (with an optional summary on deep learning).

  • Naive Bayes Classifiers
  • Random Forests
  • Gradient Boosted Decision Trees
  • Neural Networks
  • Deep Learning (Optional)
  • Data Leakage
  • Dimensionality Reduction and Manifold Learning
  • Clustering
  • Conclusion

Training Schedule

15 – 17 Feb 2021
12 – 14 Apr 2021
8 – 10 Jun 2021
2 – 5 Aug 2021
25 – 27 Oct 2021
13 – 15 Dec 2021

  • Public Class Training

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  • Examination (Optional)

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