16 others bought this course in the last 24 hours! Machine Learning Basics teaches you everything on the topic thoroughly from scratch so you can achieve a professional certificate for free to showcase your achievement in professional life. This Machine Learning Basics is a comprehensive, instructor-guided course, designed to provide a detailed understanding of the nature of the related sector and your key roles within it. To become successful in your profession, you must have a specific set of skills to succeed in today’s competitive world. In this in-depth training course, you will develop the most in-demand skills to kickstart your career, as well as upgrade your existing knowledge & skills. The training materials of this course are available online for you to learn at your own pace and fast-track your career with ease.

• Accredited by CPD
• Instant e-certificate
• Fully online, interactive course with Professional voice-over

• Self paced learning and laptop, tablet, smartphone friendly
• 24/7 Learning Assistance
• Discounts on bulk purchases

Sneak Peek

Who should take the course

Anyone with a knack for learning new skills can take this Machine Learning Basics. While this comprehensive training is popular for preparing people for job opportunities in the relevant fields, it also helps to advance your career for promotions.

Certification

Once you’ve successfully completed your course, you will immediately be sent a digital certificate. All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.

Accreditation

All of our courses, including this Machine Learning Basics, are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field.

Course Curriculum

The detailed curriculum outline of our Machine Learning Basics is as follows: 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