Machine Learning : Linear & Logistic Regression

Machine Learning Linear & Logistic Regression

This excellent Machine Learning – Linear & Logistic Regression course will teach you how to build robust linear models and do logistic regressions in Excel, R, and Python that will stand up to scrutiny when you apply them to real world situations. If you’re someone who needs to get to grips with machine learning, this Machine Learning – Linear & Logistic Regression course is for you, and it will help you to grasp the theory underlying factor analysis.

Our learning material is available to students 24/7 anywhere in the world, so it’s extremely convenient. These intensive online courses are open to everyone, as long as you have an interest in the topic! We provide world-class learning led by IAP, so you can be assured that the material is high quality, accurate and up-to-date.

Why should I choose 1Training?

• Accredited qualification
• Excellent quality video tutorials
• You'll be eligible for an NUS Discount Card
• Get one year's access to the course
• Get support by phone, live chat, and email
• Join our friendly online learning platform
• Our Course is fully compatible with PC’s, Mac’s, Laptop, Tablet and Smartphone devices. So, you can access your course on Wi-Fi, 3G or 4G.

Learn for less with 1Training discount courses online! You’ll love 1Training’s excellent quality, competitive prices, and first class learner support. With this excellent discount course you can expect professional qualification, which will enable you to enhance your CV in no time, and for less!

What skills am I going to get from this course?

• Method of least squares, Explaining variance, Forecasting an outcome
• Residuals, assumptions about residuals
• Implement simple regression in Excel, R and Python
• Interpret regression results and avoid common pitfalls

Course outline:

Module 01 : Introduction

• You, This Course, & Us!

Module 02 : Connect the Dots with Linear Regression

• Using Linear Regression to Connect the Dots
• Two Common Applications of Regression
• Extending Linear Regression to Fit Non-linear Relationships

Module 03 : Basic Statistics Used for Regression

• Understanding Mean & Variance
• Understanding Random Variables
• The Normal Distribution

Module 04 : Simple Regression

• Setting up a Regression Problem
• Using Simple Regression to Explain Cause-Effect Relationships
• Using Simple Regression for Explaining Variance
• Using Simple Regression for Prediction
• Interpreting Regression results – Adjusted R-squared
• Mitigating Risks in Simple Regression

Module 05 : Applying Simple Regression

• Applying Simple Regression in Excel
• Applying Simple Regression in R
• Applying Simple Regression in Python

Module 06 : Multiple Regression

• Introducing Multiple Regression
• Some Risks inherent to Multiple Regression
• Benefits of Multiple Regression
• Introducing Categorical Variables
• Interpreting Regression results – Adjusted R-squared
• Interpreting Regression results – Standard Errors of Coefficients
• Interpreting Regression results – t-statistics & p-values
• Interpreting Regression results – F-Statistic

Module 07 : Applying Multiple Regression using Excel

• Implementing Multiple Regression in Excel
• Implementing Multiple Regression in R
• Implementing Multiple Regression in Python

Module 08 : Logistic Regression for Categorical Dependent Variables

• Understanding the need for Logistic Regression
• Setting up a Logistic Regression problem
• Applications of Logistic Regression
• The link between Linear & Logistic Regression
• The link between Logistic Regression & Machine Learning

Module 09 : Solving Logistic Regression

• Understanding the intuition behind Logistic Regression & the S-curve
• Solving Logistic Regression using Maximum Likelihood Estimation
• Solving Logistic Regression using Linear Regression
• Binomial vs Multinomial Logistic Regression

Module 10 : Applying Logistic Regression

• Predict Stock Price movements using Logistic Regression in Excel
• Predict Stock Price movements using Logistic Regression in R
• Predict Stock Price movements using Rule-based & Linear Regression
• Predict Stock Price movements using Logistic Regression in Python

Method of Assessment

• You will have one assignment.
• You will only need to pay £19 for assessment when you submit your assignment.

Certification

Those who successfully pass this course will be awarded a Machine Learning – Linear & Logistic Regression Certificate. Anyone eligible for certification will receive both FREE e-certificate (PDF format), and printed certificate.

• School Leavers
• Job Seekers
• Data Operators

This course requires no formal prerequisites and this certification is open to everyone

• Data Scientist
• Big Data Specialist
• Data Architect
• Data Analyst

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