• This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons۔
• Convolutional neural networks, natural language processing, etc.) as well as demonstrate.
• How these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction.
• In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets.
• These practice exercises will teach you how to implement machine learning algorithms with PyTorch.
• Open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more)

Simple Introduction To Machine Learning

• The focus of this module is to introduce the concepts of machine learning with as little mathematics as possible.
• We will introduce basic concepts in machine learning, including logistic regression.
• A simple but widely employed machine learning (ML) method.
• Also covered is multilayered perceptron (MLP), a fundamental neural network.
• The concept of deep learning is discussed, and also related to simpler models.

Basics Of Model Learning

• In this module we will be discussing the mathematical basis of learning deep networks.
• We’ll first work through how we define the issue of learning deep networks as a minimization problem of a mathematical function in Introduction to Machine Learning.
• After defining our mathematical goal, we will introduce validation methods to estimate real-world performance of the learned deep networks.
• We will then discuss how gradient descent, a classical technique in optimization, can be used to achieve this mathematical goal.
• Finally, we will discuss both why and how stochastic gradient descent is used in practice to learn deep networks.