Unleashing Unlabelled Data: Self-Supervised Learning

Master the Power of Unlabelled Data: Self-Supervised Machine Learning Techniques in Python for Artificial Intelligence

What you'll 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

Requirements

Basic Python data science concepts

Basic Python syntax

Understanding of the Colab environment

Description

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.IF YOU ARE A NEWCOMER TO SELF-SUPERVISED MACHINE LEARNING, ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT THIS LATEST ADVANCEMENT IN ARTIFICIAL INTELLIGENCEThis 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 importanceLearn to implement the common data science frameworks and work with important AI packages, including H2O and KerasUse common self-supervised machine learning techniques to learn from unlabelled dataCarry out important AI tasks, including denoising images and anomaly detectionIn 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 :)

Overview

Section 1: Introduction To the Course

Lecture 1 Introduction:What Is Self-Supervised Machine Learning (ML)?

Lecture 2 Data and Code

Lecture 3 Python Installation

Lecture 4 Start With Google Colaboratory Environment

Lecture 5 Google Colabs and GPU

Lecture 6 Installing Packages In Google Colab

Lecture 7 Install H2O In Colab

Lecture 8 Installing H2O Locally

Lecture 9 Course details

Section 2: Basic Data Preprocessing

Lecture 10 Introduction to Numpy

Lecture 11 What Is Pandas?

Lecture 12 Basic Data Cleaning With Pandas

Lecture 13 Basics of Data Visualisation

Section 3: Learning From Unlabelled Data

Lecture 14 What is Unsupervised Learning?

Lecture 15 Theory Behind Autoencoders

Lecture 16 The Link Between Self-Supervised Machine Learning (ML) and Autoencoders

Lecture 17 Lets Implement a Basic Auto-Encoder With H20

Lecture 18 Variational Autoencoder (VAE) With H2O

Lecture 19 What Is Denoising?

Lecture 20 Autoencode the Image Data With H2O

Lecture 21 Denoise the Data with H2O

Lecture 22 Autoencoders With Keras Deep Learning

Lecture 23 Convolutional Autoencoders-Encoding

Lecture 24 Convolutional Autoencoders-Decoding

Section 4: Miscellaneous Concepts

Lecture 25 What is Supervised Learning?

Lecture 26 Theory Behind ANN and DNN

Lecture 27 What Are Activation Functions?

Lecture 28 Introduction To Convolutional Neural Networks (CNN)

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