Applied ML: A to Z of real-world Data Science

Applied ML: A to Z of real-world Data Science, available at $19.99, has an average rating of 4.63, with 20 lectures, 1 quizzes, based on 8 reviews, and has 52 subscribers.

You will learn about Discovering and increasing your data's potential Supervised learning and it's real world applications Unsupervised learning and it's real world applications Reinforcement learning and it's real world applications How to plan and execute your ML or DL project How you can take control of data and ML lifecycle This course is ideal for individuals who are Business Leaders wanting to solve their problems through data, Product Managers, Software developers curious about solving problems using data, Beginner Data Scientists and Business Analysts It is particularly useful for Business Leaders wanting to solve their problems through data, Product Managers, Software developers curious about solving problems using data, Beginner Data Scientists and Business Analysts.

Enroll now: Applied ML: A to Z of real-world Data Science

Summary

Title: Applied ML: A to Z of real-world Data Science

Price: $19.99

Average Rating: 4.63

Number of Lectures: 20

Number of Quizzes: 1

Number of Published Lectures: 20

Number of Published Quizzes: 1

Number of Curriculum Items: 22

Number of Published Curriculum Objects: 22

Original Price: ₹799

Quality Status: approved

Status: Live

What You Will Learn

Discovering and increasing your data's potential
Supervised learning and it's real world applications
Unsupervised learning and it's real world applications
Reinforcement learning and it's real world applications
How to plan and execute your ML or DL project
How you can take control of data and ML lifecycle

Who Should Attend

Business Leaders wanting to solve their problems through data, Product Managers, Software developers curious about solving problems using data, Beginner Data Scientists and Business Analysts

Target Audiences

Business Leaders wanting to solve their problems through data, Product Managers, Software developers curious about solving problems using data, Beginner Data Scientists and Business Analysts

This course will provide the technical knowledge you need to get started with applying Machine Learning (ML) to solve your problem efficiently and at scale. We start from the data stage, move onto ML concepts, tying them back to example use cases and their evaluation, and also cover planning and scaling strategies that help you get your solution out into the world. Beyond that, the course also covers steps that help you continuously maintain and improve your solution pipeline, throughout its lifecycle.

There could be parts of this course that the learner may be aware of already, but as someone who does this day in and out, I have tried to include scenarios, challenges, steps and the outlook to face even well known topics with more confidence than before, and put them together in a well-ordered flow. This might come in handy to someone preparing for an interview in this field. As someone who has learnt courses on the go during commute or other times, and having realised the time saving value, I have made the course’s audio content substantially context rich for those who prefer consuming it through audio. It does have the video component as well, for visual learners.

This course can act as a well organised end-to-end guidebook to integrate Data Science and Machine Learning knowledge across the board into the everyday work of a Business Leader, Product Manager, Software Developer, Researcher, Analyst or Data Scientist, by being realistic and holistic. The learner can use this as a framework and mindset, that will enable them to think objectively and comprehensively at all stages of data and ML adaptation and application, thereby increasing its success rate.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to the instructor

Lecture 2: Introduction to the course

Chapter 2: Discovering your data's potential

Lecture 1: Types of data

Lecture 2: Data preprocessing: basic steps

Lecture 3: Data preprocessing: advanced steps

Lecture 4: Sampling the data

Chapter 3: Supervised Learning

Lecture 1: When to use supervised learning with examples

Lecture 2: Classification

Lecture 3: Regression

Lecture 4: Time series

Chapter 4: Unsupervised Learning

Lecture 1: When to use unsupervised learning with examples

Lecture 2: Clustering

Lecture 3: Anomaly detection

Lecture 4: Recommender systems and dimensionality reduction

Chapter 5: Reinforcement Learning

Lecture 1: Introduction to reinforcement learning

Lecture 2: Models and their implementation strategies

Chapter 6: Planning, implementation and maintenance

Lecture 1: How to plan a data-backed project

Lecture 2: Implementing and maintaining your data pipelines and ML services at scale

Chapter 7: More Learnings: Interview preparation

Lecture 1: Interview Tips

Lecture 2: Feedback Request

Chapter 8: Test your understanding

Instructors

Your Data HQ
Senior Data Scientist, ML Engineer, ML Researcher

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 1 votes
5 stars: 6 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!

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