Applied ML: The Big Picture

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Sanjana Sahayaraj

3:07:27

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1. Introduction

1. Introduction to the instructor.mp4

01:55

2. Introduction to the course.mp4

04:52

2. Discovering your data's potential

1. Types of data.mp4

04:34

2. Data preprocessing basic steps.mp4

12:43

3. Data preprocessing advanced steps.mp4

10:25

4. Sampling the data.mp4

10:45

3. Supervised Learning

1. When to use supervised learning with examples.mp4

10:24

2. Classification.mp4

22:01

3. Regression.mp4

18:34

4. Time series.mp4

16:01

4. Unsupervised Learning

1. When to use unsupervised learning with examples.mp4

05:21

2. Clustering.mp4

06:41

3. Anomaly detection.mp4

03:47

4. Recommender systems and dimensionality reduction.mp4

06:30

5. Reinforcement Learning

1. Introduction to reinforcement learning.mp4

03:05

2. Models and their implementation strategies.mp4

06:19

6. Planning, implementation and maintenance

1. How to plan a data-backed project.mp4

05:07

2. Implementing and maintaining your data pipelines and ML services at scale.mp4

18:57

7. More Learnings Interview preparation

1. Interview Tips.mp4

18:22

2. Feedback Request.mp4

01:04

8. Test your understanding

1. 10 questions.html

Description

A practical overview of Data Science, Machine Learning and how to use it efficiently in your problem solving

What You'll 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 is this 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

What You Need to Know?

You just need to know what is software and how it works, to start understanding about how ML and DL software works and what their potential is

More details

Description
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 their data backed project's lifecycle, thereby increasing its success rate.
Who this course is 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

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Machine Learning

Sanjana Sahayaraj

Instructor's Courses

Hey everyone, I started my career as an NLP Research Engineer with an MNC and later pivoted to more product based roles as a Senior Data Scientist, and got into the startup world as well, where I got to own and apply data and ML at various stages. This gave me a holistic view on how the research and theory in ML space can translate to real business results, when applied the right way. This is the practical knowledge I share on Udemy. In addition, I also run a podcast by the name "Data Epoch" on Spotify, Apple Podcasts and Youtube for the Data and ML community to come together and learn from each other.

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