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Course Description

In this course, students will be introduced to Machine Learning and basic Classification and Regression concepts using the Python programming language and various open source visualization tools. Students will predict the probability of customer retention and predict the sales capacity of various chain stores in a given location throughout one year. Students will also use learn to deploy their predictive models in a basic, predefined Machine Learning pipeline for making predictions using real-time data

Course Outline

This course will cover the following topics:

  • Machine Learning Concepts
    • ETL
    • Preprocessing
    • EDA
    • Feature Selection/Engineering
    • Data Splitting and Cross Validation
    • Model Selection, Training and Assessment
    • Prediction and Resampling
    • Deployment and Maintenance
    • Bias Variance Tradeoff
  • Classification
    • Discriminant Functions
    • Input/Discrete Output Variables
    • Lazy Leaners vs. Eager Learners
    • Classification Algorithms
      • Logistic Regression
      • Decision Tree
      • K-Nearest Neighbor
      • Naïve Bayes
      • Random Forrest
      • Support Vector Machine
    • Evaluation of a Classifier
      • Cross-Validation
      • ROC Curve (Receiver Operating Characteristics)
      • Wilson Verification Formula / Confidence Intervals
  • Regression
    • Simple Linear Regression
    • Polynomial Regression
    • Support Vector Regression
    • Decision Tree Regression
    • Random Forest Regression
    • Regression Evaluation
  • Ensemble Methods
    • Boosting
    • Bagging
  • Data Visualization Concepts and tools
    • Pandas
    • Matplotlib/Seaborn
    • Plotly
    • Ggplot2

Learner Outcomes

This is a comprehensive course in understanding the key concepts and application in supervised learning such as classification and regression. In addition, learners will apply these skills to relevant industry business situations.

Successful completion of this course entitles the student to The Intelligence Factory Skillset Certification in the following areas:

  • Supervised Machine Learning
  • Business Intelligence Analytics

This course also provides you the skills and concepts needed to complete the TIF Certification One for a Machine Learning Engineer.

Prerequisites

Basic knowledge of Python programming skills.

Duration

30 Hours | 5 days or 10 nights

Applies Towards the Following Certificates

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Thank you for your interest in this course. Unfortunately, the course you have selected is currently not open for enrollment. Please complete a Course Inquiry or call 314-977-3226 so that we may promptly notify you when enrollment opens.