Overview

This advanced machine learning and deep learning course provides a robust foundation in these transformative technologies. Starting with an overview of deep learning, you'll explore its core concepts, real-world applications, and significance in AI's evolution. Practical aspects include neural network layers, activation functions, and performance metrics in model evaluation. Through hands-on coding labs, you'll cover regression, classification, and convolutional neural networks (CNNs), building and fine-tuning models, understanding loss functions, and using optimizers for accuracy. Emphasis is on frameworks like TensorFlow and PyTorch for developing robust neural networks. The course concludes with specialized topics such as autoencoders, transfer learning, and recurrent neural networks (RNNs). Interactive labs and projects will apply knowledge to complex data analysis, time-series prediction, and creating web applications with Shiny. Ideal for data scientists, machine learning engineers, and AI enthusiasts, prerequisites include Python proficiency and basic machine learning knowledge.

Syllabus

Deep Learning: Introduction

In this module, we will explore the fundamental principles of deep learning, from its basic concepts to the intricacies of building and training neural networks. We will delve into various types of neural network layers, activation and loss functions, optimizers, and the tools and frameworks essential for deep learning development.

Deep Learning: Regression

In this module, we will delve into the specialized field of multi-target regression using deep learning. We will cover the theoretical foundations and follow a step-by-step coding guide to implement and refine regression models capable of predicting multiple continuous variables simultaneously.

Deep Learning: Classification

In this module, we will embark on a comprehensive journey into classification with deep learning, focusing on binary and multi-label classification techniques. We will build, code, and refine models that can effectively classify data into distinct or multiple categories, using hands-on labs and practical examples.

Deep Learning: Convolutional Neural Networks

In this module, we will dive deep into Convolutional Neural Networks (CNNs), from their basic architecture to advanced applications. We will engage with interactive explorations, hands-on labs, and practical exercises to develop a robust understanding of CNNs' role in image recognition, classification, and semantic segmentation.

Deep Learning: Autoencoders

In this module, we will explore the fascinating world of Autoencoders, focusing on their theoretical foundations and practical applications. We will learn how to effectively implement Autoencoders, understand their diverse uses, and gain hands-on experience through coding labs.

Deep Learning: Transfer Learning and Pretrained Networks

In this module, we will delve into transfer learning and pretrained models, exploring how these techniques revolutionize the efficiency and effectiveness of deep learning. We will learn to apply these methods practically through lab sessions, significantly enhancing our deep learning projects.

Deep Learning: Recurrent Neural Networks

In this module, we will explore Recurrent Neural Networks (RNNs) and their application in processing sequential data. We will focus on Long Short-Term Memory (LSTM) networks for time series prediction, gaining practical experience through coding labs and hands-on experimentation.

Shiny

In this module, we will explore Shiny, a framework for building interactive web applications. We will learn about its essential components, delve into language selection and reactive expressions, and gain hands-on experience in developing and deploying Shiny apps for real-world use.