Decentralized Data Science

Decentralized Data Science, available at $19.99, has an average rating of 4.86, with 40 lectures, based on 7 reviews, and has 1020 subscribers.

You will learn about Overview of Data Science and Machine Learning Federated Learning Decentralized Data Marketplaces Differential Privacy Homomorphic Encryption TensorFlow Federated (TFF) TensorFlow Lite This course is ideal for individuals who are Techies and Tech Investors It is particularly useful for Techies and Tech Investors.

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Summary

Title: Decentralized Data Science

Price: $19.99

Average Rating: 4.86

Number of Lectures: 40

Number of Published Lectures: 40

Number of Curriculum Items: 40

Number of Published Curriculum Objects: 40

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Overview of Data Science and Machine Learning

Federated Learning

Decentralized Data Marketplaces

Differential Privacy

Homomorphic Encryption

TensorFlow Federated (TFF)

TensorFlow Lite

Who Should Attend

Techies and Tech Investors

Target Audiences

Techies and Tech Investors

Please note that this is not a Data Science or Machine Learning course. This course does not cover any coding.

Welcome to the course on “Decentralized Data Science” – an exploration into the intersection of cutting-edge technologies and the transformative power of decentralized approaches in Data Science – especially in Machine Learning.

ChatGPT brought us to the verge of an AI Race. It is expected that in the coming months and years, all the tech majors will launch many new AI models.

We are all excited about the sector that is poised for dramatic innovation. But, is there anything we should be concerned about?

Yes. Privacy.

These tech majors are likely to use user data to train their models. As centralized data processing involves various vulnerabilities, user privacy will be at stake in this AI Race.

So, is there any way to preserve user privacy in Machine Learning?

This is where Decentralized Data Science comes in.

Decentralized Machine Learning offers various frameworks such as Federated Learning, Differential Privacy, Homomorphic Encryption, Secure Multi-Party Computations, and Edge Computing. These frameworks enable processing of data while preserving user privacy.

We will also discuss tools such as TensorFlow Federated and TensorFlow Lite that help us build these decentralized machine learning systems.

Let us discuss these concepts in this course

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Who is this course for?

Lecture 3: Course Outline

Chapter 2: Basics of Data Science

Lecture 1: What is Data Science?

Lecture 2: Classification of Data Science

Chapter 3: Primer on Machine Learning

Lecture 1: Introduction

Lecture 2: Machine Learning Models

Lecture 3: Representation of ML Models

Lecture 4: ML Training

Lecture 5: ML Frameworks

Chapter 4: MLOps

Lecture 1: Introduction

Lecture 2: Overview of MLOps

Chapter 5: Why does data science need to be decentralized?

Lecture 1: Why does data science need to be decentralized?

Chapter 6: Federated Learning

Lecture 1: Introduction

Lecture 2: TensorFlow Federated (TFF)

Lecture 3: Federated Averaging (FedAvg)

Lecture 4: Secure Aggregation

Lecture 5: TensorFlow Lite

Lecture 6: Federated Datasets

Lecture 7: Federated optimization

Lecture 8: Use Cases

Chapter 7: Decentralized Data Marketplaces

Lecture 1: Introduction

Lecture 2: Workings

Chapter 8: Differential Privacy

Lecture 1: Differential Privacy

Chapter 9: Homomorphic Encryption

Lecture 1: Introduction

Lecture 2: Use Cases

Chapter 10: Edge Computing and Edge Analytics

Lecture 1: Introduction

Lecture 2: Federated Learning Vs Edge Analytics

Lecture 3: Edge Analytics Use Cases

Lecture 4: Use of Edge Computing with Federated Learning

Chapter 11: Secure Multi-Party Computation (SMPC)

Lecture 1: Introduction

Lecture 2: Protocols

Chapter 12: Tensorflow Federated (TFF)

Lecture 1: Introduction

Lecture 2: TensorFlow Federated APIs

Lecture 3: Example Application – Federated Learning (FL) API

Lecture 4: Example Application – Federated Core (FC) API

Chapter 13: TensorFlow Lite

Lecture 1: Introduction

Lecture 2: Role in Decentralized Data Science

Lecture 3: Sample Application

Chapter 14: Thank You

Lecture 1: Thank You

Instructors

Sam Ghosh
Emerging Technology Consultant

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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?

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