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Introduction to Machine Learning (IITM)

  • Course level: Intermediate

Description

Introduction to Machine Learning (IITM)

With the rising availability of data from numerous sources, several data-driven disciplines such as analytics and machine learning have received more attention. In this course, we’ll cover some of the fundamental concepts of machine learning from a mathematically sound standpoint. We’ll go through the various learning paradigms and some of the most widely used algorithms and architectures in each of them.

Along with AI, machine learning is the gasoline that will power robots. We can use ML to power programs that can be easily updated and tweaked to adapt to new surroundings and tasks, allowing us to get more done in less time. Here are a few reasons why you should pursue a Machine Learning career:
1) Machine learning is a future skill – Despite its rapid expansion, ML is experiencing a skills deficit. You will have a solid career in a technology that is on the increase if you can match the demands of huge corporations by obtaining proficiency in Machine Learning

2) Work on real-world problems – In this digital age, businesses face a slew of problems that Machine Learning promises to solve. As a Machine Learning Engineer, you’ll work on real-world problems and develop solutions that will have a significant impact on how organizations and people succeed. Naturally, a profession that allows you to work and solve real-world problems provides a lot of satisfaction.

3) Learn and grow – With Machine Learning on the rise, getting into the sector early allows you to see trends firsthand and continue to increase your market relevance, improving your worth to your employer.

4) An exponential career graph – When it comes down to it, ML is still in its infancy. You’ll have the skills and expertise to follow an upward career path and approach your preferred companies as technology evolves and advances.

5) Create a wealthy profession– One of the major reasons why ML appears to be a lucrative career to many of us is the typical compensation of an ML engineer. Because the industry is growing, this number is likely to increase in the coming years.

6) Take a detour into data science — ML skills can help you broaden your job options. ML skills can equip you with two hats: one of a data scientist and the other of a machine learning expert. Gain experience in both sectors at the same time to become a valuable resource and embark on an exciting journey filled with challenges, changes, and information.

Right now, ML is taking place. So you’d like to get a head start on experimenting with solutions and technology that support it. As a result, when the time comes, your abilities will be in much higher demand, and you will be able to secure a career path that is continually growing.

This course is intended for the following individuals:

For software engineers who wish to study ML from the ground up.
Python programmers that want to master ML

Professionals interested in pursuing a profession in Machine Learning

 

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What Will I Learn?

  • Learn how to use NumPy in machine learning to perform quick mathematical calculations.
  • Find out what Machine Learning is and how to deal with data in machine learning.
  • Learn how to use scikit-learn for machine learning data preprocessing.
  • In machine learning, you'll learn about several model selection and feature selection strategies.
  • Learn about machine learning's cluster analysis and anomaly detection.
  • Learn how to use SVMs in machine learning for classification, regression, and outlier detection.

Topics for this course

36 Lessons

INTRODUCTION TO THE COURSE

Introduction to a web-app13:27
Building a web-app13:39
Networks6:28
P1: Practical: Running your own web-server17:25
Protocols7:15
Module P2: Practical: SSH + Network experiments18:01
Module P3: Practical: Building a webapp with nodejs and using git. Introduction to reverse proxies.28:06
Module P4: Module P4: Practical: Introduciton to server-side javascript and HTML/CSS40:01
Module P5: Introduction to client-side Javascript16:27
Module P6: Practical: APIs and mobile apps use web-servers34:46
Module 6: Introduction to databases00:00:00
Module 7: Data modelling & constraints18:24
Module P7: Interacting with a DBMS00:00:00
Module P8: Practical: Deeper exploration of a DBMS (column types and more)17:45
Module P9: Introduction to SQL00:00:00
Module 8: Understanding database performance19:08
Module 9: Transactions & ACID properties7:39
Module 10: Database security, backup & recovery9:24
Module 11: Analytics & Views8:50
Module 12: Scaling a database7:12
Module P10: Module P10: Connecting your webapp to your database & SQL Injection19:59
Module 17: SQL & NoSQL systems11:15
Module 13: Authentication with HTTP15:10
Module 14: Understanding security, and some best practices for webapps15:38
Module P11: Introduction to authentication, hashing, curl & sessions41:01
Module 15: Introduction to mobile apps6:51
Module 16 – Introduction to Mobile Application Development Part 28:01
Module 17 – Introduction to Android8:38
Module P12 – Getting started with Android Application Development22:15
Module P13 – Building Custom UI using XML and Logs18:21
Module P14 – Building a Blog App58:12
Module 18: Deploying an app to the Google Play Store10:32
Module 19: Introduction to iOS7:31
Module 21: Modern Development Practices (Version Control)43:27
Module 19: The API Economy4:03
Module 22: Backend Architectures29:43

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Enrolment validity: Lifetime