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Machine Learning Linear & Logistic Regression

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387 STUDENTS
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Course Description:

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.

What skills will I gain?

Simple Regression:

  • 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

Multiple Regression:

  • Implement Multiple regression in Excel, R and Python
  • Introduce a categorical variable

Logistic Regression:

  • Applications of Logistic Regression, the link to Linear Regression and Machine Learning
  • Solving logistic regression using Maximum Likelihood Estimation and Linear Regression
  • Extending Binomial Logistic Regression to Multinomial Logistic Regression
  • Implement Logistic regression to build a model stock price movements in Excel, R and Python

What are the requirements?

  • You must be 16 or over
  • You should have a basic understanding of English, Maths and ICT
  • You will need a computer or tablet with internet connection (or access to one)

Meet the Instructor:

Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.

  • Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft
  • Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too
  • Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum
  • Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum

Course outline:

  • Module 01: Introduction
  • Module 02: Connect the Dots with Linear Regression
  • Module 03: Basic Statistics Used for Regression
  • Module 04: Simple Regression
  • Module 05: Applying Simple Regression
  • Module 06: Multiple Regression
  • Module 07: Applying Multiple Regression using Excel
  • Module 08: Logistic Regression for Categorical Dependent Variables
  • Module 09: Solving Logistic Regression
  • Module 10: Applying Logistic Regression

How will I be assessed?

  • You will have one assignment. Pass mark is 65%.
  • You will only need to pay £19 for assessment.
  • You will receive the results within 72 hours of submittal, and will be sent a certificate in 7-14 days.

What Certification am I going to receive?

Those who successfully pass this course will be awarded a Machine Learning – Linear & Logistic Regression certificate. Anyone eligible for certification will receive a free e-certificate, and printed certificate.

What careers can I get with this qualification?

Once you have completed this Machine Learning – Linear & Logistic Regression course you will have desirable skills. You could go on to further study of this topic, or could gain entry level employment in analytics or big data. These roles often command a high salary, for example, the average salary of a Data Scientist in the UK is £43,318, and this will go up with experience (payscale.com). When you complete this Machine Learning – Linear & Logistic Regression, you could fulfil any of the following roles:

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

Key Features

Gain an accredited UK qualification

Access to excellent quality study materials

Learners will be eligible for TOTUM Discount Card

Personalized learning experience

One year’s access to the course

Support by phone, live chat, and email

Course Curriculum Total Units : 40
01: Introduction
1. You, This Course, & Us!
02: Connect the Dots with Linear Regression
1. Using Linear Regression to Connect the Dots
2. Two Common Applications of Regression
3. Extending Linear Regression to Fit Non-linear Relationships
03: Basic Statistics Used for Regression
1. Understanding Mean & Variance
2. Understanding Random Variables
3. The Normal Distribution
04: Simple Regression
1. Setting up a Regression Problem
2. Using Simple Regression to Explain Cause-Effect Relationships
3. Using Simple Regression for Explaining Variance
4. Using Simple Regression for Prediction
5. Interpreting Regression results – Adjusted R-squared
6. Mitigating Risks in Simple Regression
05: Applying Simple Regression
1. Applying Simple Regression in Excel
2. Applying Simple Regression in R
3. Applying Simple Regression in Python
06: Multiple Regression
1. Introducing Multiple Regression
2. Some Risks inherent to Multiple Regression
3. Benefits of Multiple Regression
4. Introducing Categorical Variables
5. Interpreting Regression results – Adjusted R-squared
6. Interpreting Regression results – Standard Errors of Coefficients
7. Interpreting Regression results – t-statistics & p-values
8. Interpreting Regression results – F-Statistic
07: Applying Multiple Regression using Excel
1. Implementing Multiple Regression in Excel
2. Implementing Multiple Regression in R
3. Implementing Multiple Regression in Python
08: Logistic Regression for Categorical Dependent Variables
1. Understanding the need for Logistic Regression
2. Setting up a Logistic Regression problem
3. Applications of Logistic Regression
4. The link between Linear & Logistic Regression
5. The link between Logistic Regression & Machine Learning
09: Solving Logistic Regression
1. Understanding the intuition behind Logistic Regression & the S-curve
2. Solving Logistic Regression using Maximum Likelihood Estimation
3. Solving Logistic Regression using Linear Regression
4. Binomial vs Multinomial Logistic Regression
10: Applying Logistic Regression
1. Predict Stock Price movements using Logistic Regression in Excel
2. Predict Stock Price movements using Logistic Regression in R
3. Predict Stock Price movements using Rule-based & Linear Regression
4. Predict Stock Price movements using Logistic Regression in Python
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