Unleashing Unlabelled Data: Self-Supervised Learning

Unleashing Unlabelled Data: Self-Supervised Learning, available at $44.99, has an average rating of 4.34, with 28 lectures, based on 64 reviews, and has 324 subscribers.

You will learn about Understanding the concepts behind basic machine learning tasks, including clustering and classification Learn about the uses of self-supervised machine learning Implement self-supervised machine learning frameworks such as autoencoders using Python Learn about deep learning frameworks such as Keras and H2O This course is ideal for individuals who are Data Scientists who want to increase their knowledge of self-supervised machine learning or Students of Artificial Intelligence (AI) or Students interested in learning about frameworks such as autoencoders It is particularly useful for Data Scientists who want to increase their knowledge of self-supervised machine learning or Students of Artificial Intelligence (AI) or Students interested in learning about frameworks such as autoencoders.

Enroll now: Unleashing Unlabelled Data: Self-Supervised Learning

Summary

Title: Unleashing Unlabelled Data: Self-Supervised Learning

Price: $44.99

Average Rating: 4.34

Number of Lectures: 28

Number of Published Lectures: 28

Number of Curriculum Items: 28

Number of Published Curriculum Objects: 28

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Understanding the concepts behind basic machine learning tasks, including clustering and classification
Learn about the uses of self-supervised machine learning
Implement self-supervised machine learning frameworks such as autoencoders using Python
Learn about deep learning frameworks such as Keras and H2O

Who Should Attend

Data Scientists who want to increase their knowledge of self-supervised machine learning
Students of Artificial Intelligence (AI)
Students interested in learning about frameworks such as autoencoders

Target Audiences

Data Scientists who want to increase their knowledge of self-supervised machine learning
Students of Artificial Intelligence (AI)
Students interested in learning about frameworks such as autoencoders

Self-supervised machine learning is a paradigm that learns from unlabeled data without explicit human labelling. It involves creating surrogate or pretext tasks that the model is trained to solve using the raw data. By focusing on these tasks, the model learns to capture underlying patterns and structures, enabling it to discover useful representations. Self-supervised learning benefits from abundant unlabeled data reduces the need for manual annotation, and produces rich and transferable representations. It has found success in various arenas, offering a promising approach to leverage unlabeled data for extracting meaningful information without relying on external labels.

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This course will help you gain fluency in deploying data science-based BI solutions using a powerful clouded based python environment called GoogleColab. Specifically, you will

Learn the main aspects of implementing a Python data science framework within Google Colab.
Learn what self-supervised machine learning is and its importance
Learn to implement the common data science frameworks and work with important AI packages, including H2O and Keras
Use common self-supervised machine learning techniques to learn from unlabelled data
Carry out important AI tasks, including denoising images and anomaly detection

In addition to all the above, you’ll have MY CONTINUOUS SUPPORT to ensure you get the most value out of your investment!

ENROLL NOW 🙂

Why Should You Take My Course?

My course provides a foundation to conduct PRACTICAL, real-life self-supervised machine learning By taking this course, you are taking a significant step forward in your data science journey to become an expert in harnessing the power of unlabelled data for deriving insights and identifying trends.

I have an MPhil (Geography and Environment) from the University of Oxford, UK. I also completed a data science intense PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience analyzing real-life data from different sources, producing publications for international peer-reviewed journals and undertaking data science consultancy work. In addition to all the above, you’ll have MY CONTINUOUS SUPPORT to ensure you get the most value out of your investment!

ENROLL NOW 🙂

Course Curriculum

Chapter 1: Introduction To the Course

Lecture 1: Welcome To The Course

Lecture 2: What Is Self-Supervised Machine Learning (ML)?

Lecture 3: Data and Code

Lecture 4: Python Installation

Lecture 5: Start With Google Colaboratory Environment

Lecture 6: Google Colabs and GPU

Lecture 7: Installing Packages In Google Colab

Lecture 8: Install H2O In Colab

Lecture 9: Installing H2O Locally

Chapter 2: Basic Data Preprocessing

Lecture 1: Introduction to Numpy

Lecture 2: What Is Pandas?

Lecture 3: Basic Data Cleaning With Pandas

Lecture 4: Basics of Data Visualisation

Chapter 3: Learning From Unlabelled Data

Lecture 1: What is Unsupervised Learning?

Lecture 2: Theory Behind Autoencoders

Lecture 3: The Link Between Self-Supervised Machine Learning (ML) and Autoencoders

Lecture 4: Lets Implement a Basic Auto-Encoder With H20

Lecture 5: Variational Autoencoder (VAE) With H2O

Lecture 6: What Is Denoising?

Lecture 7: Autoencode the Image Data With H2O

Lecture 8: Denoise the Data with H2O

Lecture 9: Autoencoders With Keras Deep Learning

Lecture 10: Convolutional Autoencoders-Encoding

Lecture 11: Convolutional Autoencoders-Decoding

Chapter 4: Miscellaneous Concepts

Lecture 1: What is Supervised Learning?

Lecture 2: Theory Behind ANN and DNN

Lecture 3: What Are Activation Functions?

Lecture 4: Introduction To Convolutional Neural Networks (CNN)

Instructors

Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

Rating Distribution

1 stars: 0 votes
2 stars: 5 votes
3 stars: 10 votes
4 stars: 10 votes
5 stars: 39 votes

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