The course will introduce you to applied machine learning. You will learn the methods and techniques of logistic regression. This is a complete package, and qualifying in this course will give you the opportunity to enhance your career in the industry. The course will discuss key topics basic statistics in regression, connecting dots in linear regression, and applying multiple regression using Excel. In this Linear & Logistic Regression course, you’ll learn about topics such as: understanding random variables, cause-effect relationships, maximum likelihood estimation, and so much more. Follow along with the experts as they break down these concepts in easy-to-understand lessons.
Learning with Study 365 has many advantages. The course material is delivered straight to you and can be adapted to fit in with your lifestyle. It is created by experts within the industry, meaning you are receiving accurate information, which is up-to-date and easy to understand.
This course is comprised of professional learning materials, all delivered through a system that you will have access to 24 hours a day, 7 days a week for 365 days (12 months).
This course consists of the following modules:
From the day you purchase the course, you will have 12 months access to the online study platform. As the course is self-paced you can decide how fast or slow the training goes, and are able to complete the course in stages, revisiting the training at any time.
At the end of each module, you will have one assignment to be submitted (you need a mark of 65% to pass) and you can submit the assignment at any time. You will only need to pay £19 for assessment and certification when you submit the assignment. You will receive the results within 72 hours of submittal, and will be sent a certificate in 7-14 days if you have successfully passed the course.
Successful candidates will be awarded a certificate in Machine Learning – Linear & Logistic Regression.
A comprehensive and useful course which will teach you many aspects like understanding random variables, cause-effect relationships and maximum likelihood estimation. Although these sound complex, you will be taught in a step by step approach which will break down these concepts so that you can understand better. A career with bright prospects await you with the completion of this course. According to www.payscale.uk, some of the key job positions along with the average UK salary per annum you can go for after completing this course will be as follows.
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
PLEASE NOTE: We do not provide any software with this course.
|Machine Learning – Linear & Logistic Regression||FREE||00:00:00|
|1. You, This Course, & Us!|
|2: 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|
|3: Basic Statistics Used for Regression|
|1. Understanding Mean & Variance|
|2. Understanding Random Variables|
|3. The Normal Distribution|
|4: 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 the results of a Regression|
|6. Mitigating Risks in Simple Regression|
|5: Applying Simple Regression|
|1. Applying Simple Regression in Excel|
|2. Applying Simple Regression in R|
|3. Applying Simple Regression in Python|
|6: 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|
|7: Applying Multiple Regression using Excel|
|1. Implementing Multiple Regression in Excel|
|2. Implementing Multiple Regression in R|
|3. Implementing Multiple Regression in Python|
|8: 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|
|9: 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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