Basic Machine Learning Online Course!

Basic Machine Learning Online Course
Pay QR47 instead of QR606

• Master the fundamentals of AI with the Basic Machine Learning Online Course, designed to give you a strong foundation in supervised learning, regression, classification, and predictive modeling.
• Explore real-world datasets, learn how to build and evaluate models using Minitab, and understand key concepts like data cleaning, logistic regression, and regression trees.
• Perfect for beginners, this course offers practical tools and insights to start your machine learning journey with confidence.
  
  

Course Curriculum

• Section 01: Introduction
• Introduction to Supervised Machine Learning
• Section 02: Regression
• Introduction to Regression
• Evaluating Regression Models
• Conditions for Using Regression Models in ML versus in Classical Statistics
• Statistically Significant Predictors
• Regression Models Including Categorical Predictors. Additive Effects
• Regression Models Including Categorical Predictors. Interaction Effects
• Section 03: Predictors
• Multicollinearity among Predictors and its Consequences
• Prediction for New Observation. Confidence Interval and Prediction Interval
• Model Building. What if the Regression Equation Contains “Wrong” Predictors?
• Section 04: Minitab
• Stepwise Regression and its Use for Finding the Optimal Model in Minitab
• Regression with Minitab. Example. Auto-mpg: Part 1
• Regression with Minitab. Example. Auto-mpg: Part 2
• Section 05: Regression Trees
• The Basic idea of Regression Trees
• Regression Trees with Minitab. Example. Bike Sharing: Part 1
• Regression Trees with Minitab. Example. Bike Sharing: Part 2
• Section 06: Binary Logistics Regression
• Introduction to Binary Logistics Regression
• Evaluating Binary Classification Models. Goodness of Fit Metrics. ROC Curve. AUC
• Binary Logistic Regression with Minitab. Example. Heart Failure: Part 1
• Binary Logistic Regression with Minitab. Example. Heart Failure: Part 2
• Section 07: Classification Trees
• Introduction to Classification Trees
• Node Splitting Methods 1. Splitting by Misclassification Rate
• Node Splitting Methods 2. Splitting by Gini Impurity or Entropy
• Predicted Class for a Node
• The Goodness of the Model – 1. Model Misclassification Cost
• The Goodness of the Model – 2 ROC. Gain. Lit Binary Classification
• The Goodness of the Model – 3. ROC. Gain. Lit. Multinomial Classification
• Predefined Prior Probabilities and Input Misclassification Costs
• Building the Tree
• Classification Trees with Minitab. Example. Maintenance of Machines: Part 1
• Classification Trees with Miitab. Example. Maintenance of Machines: Part 2
• Section 08: Data Cleaning
• Data Cleaning: Part 1
• Data Cleaning: Part 2
• Creating New Features
• Section 09: Data Models
• Polynomial Regression Models for Quantitative Predictor Variables
• Interactions Regression Models for Quantitative Predictor Variables
• Qualitative and Quantitative Predictors: Interaction Models
• Final Models for Duration and TotalCharge: Without Validation
• Underfitting or Overfitting: The “Just Right Model”
• The “Just Right” Model for Duration
• The “Just Right” Model for Duration: A More Detailed Error Analysis
• The “Just Right” Model for TotalCharge
• The “Just Right” Model for ToralCharge: A More Detailed Error Analysis
• Section 10: Learning Success
• Regression Trees for Duration and TotalCharge
• Predicting Learning Success: The Problem Statement
• Predicting Learning Success: Binary Logistic Regression Models
• Predicting Learning Success: Classification Tree Models

• Valid from 15 Mar – 15 May 2026

• Redemption period: 2 months from the deal’s start date

• Limit of ONE voucher per person; may buy multiple vouchers as gifts

• Lifetime access to Course content & materials

• Vouchers not redeemed by 15 Mar 2026 will NOT be refunded

• No mandatory completion deadline; you can take & finish the course anytime you want!

• At the successful completion of this course, learners will receive a digital certificate through their registered email address for FREE; for a printed hard copy of the certificate, learners have to pay £3.99 for shipping within the UK & £10.00 outside the UK

• In order to be eligible for the certificate you need to successfully complete the course & pass each course module

• For more information visit: Machine Learning Basic or email [email protected]  

• Internet access & audio required

• You’ll get access to detailed video tutorials, practical examples & useful tips

• Not valid with other offers

• No cash value/No cash back/No refunds
  
  

How to Redeem:

• Click HERE

• Enter your Name, Email Address, & Contact number

• Enter the voucher code

• Lastly, click on the SUBMIT button

• A user account will be created & the course will be assigned to the account

• The Login Details will be sent to you via email within 48 hours

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