Machine Intelligence an Introductory Course

Machine Intelligence – an Introductory Course, available at Free, has an average rating of 3.85, with 10 lectures, based on 161 reviews, and has 14607 subscribers.

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You will learn about Machine Learning algorithms Artificial Intelligence algorithms Information Retrieval algorithms Deep Learning algorithms Quantum Computing algorithms Computer Vision algorithms Natural Language Processing algorithms This course is ideal for individuals who are Aspiring and Professional Data Scientists and Machine Learning Engineers. or Students pursuing their PhD and looking for a refresher course. It is particularly useful for Aspiring and Professional Data Scientists and Machine Learning Engineers. or Students pursuing their PhD and looking for a refresher course.

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Summary

Title: Machine Intelligence – an Introductory Course

Price: Free

Average Rating: 3.85

Number of Lectures: 10

Number of Published Lectures: 10

Number of Curriculum Items: 10

Number of Published Curriculum Objects: 10

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Machine Learning algorithms

Artificial Intelligence algorithms

Information Retrieval algorithms

Deep Learning algorithms

Quantum Computing algorithms

Computer Vision algorithms

Natural Language Processing algorithms

Who Should Attend

Aspiring and Professional Data Scientists and Machine Learning Engineers.

Students pursuing their PhD and looking for a refresher course.

Target Audiences

Aspiring and Professional Data Scientists and Machine Learning Engineers.

Students pursuing their PhD and looking for a refresher course.

This course focuses on the theoretical aspects of the field of Data Science and Machine Learning. It helps the students to quickly gain an in-depth overview of different algorithmic techniques used in various domains and applications. This course features external links to further enhance the experience and reinforce the concepts acquired. It also provides easy explanations of popular and useful research papers that are driving this field forward.

Course Curriculum

Chapter 1: Survival Analysis

Lecture 1: Introduction to Survival Analysis

Lecture 2: Censorship

Lecture 3: The Survival Function and the Hazard Function

Lecture 4: Kaplan-Meier Estimate and Nelson Aalen Fitter

Lecture 5: Survival Regression – Cox Proportional Hazard Regression Model

Chapter 2: Information Retrieval

Lecture 1: Introduction to Information Retrieval

Lecture 2: Text Preprocessing

Lecture 3: Term-Document Incidence Matrix

Lecture 4: Inverted Index

Lecture 5: Retrieval Vector Space Model

Instructors

Taimur Zahid
Machine Learning Engineer

Rating Distribution

1 stars: 13 votes

2 stars: 18 votes

3 stars: 35 votes

4 stars: 49 votes

5 stars: 46 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!