Introduction to Machine Learning PDF

Introduction to Machine Learning Exploring the Core Concepts behind ML  Introduction This presentation provides an introduction on Machine Learning concepts. But doesn’t cover how to implement or work with data for ML. Learning Objectives: Understand the different t...

Introduction to Machine Learning Exploring the Core Concepts behind ML  Introduction This presentation provides an introduction on Machine Learning concepts. But doesn’t cover how to implement or work with data for ML. Learning Objectives: Understand the different types of machine learning. Understand the key concepts of supervised machine learning. Learn how solving problems with ML is different from traditional approaches. What is Machine Learning? ML is the process of training a piece of software, called a model, to make useful predictions or generate content from data. What is the training process of ML? Step 1: Prepare Your Data. Step 2: Create a Training Datasource. Step 3: Create an ML Model. Step 4: Review the ML Model's Predictive Performance and Set a Score Threshold. Step 5: Use the ML Model to Generate Predictions. Step 6: Clean Up. Types of ML Systems? Supervised learning Unsupervised learning Reinforcement learning Generative AI Supervised Learning Supervised learning models can make predictions after seeing lots of data with the correct answers and then discovering the connections between the elements in the data that produce the correct answers. Regression and Classification Two of the most common use cases for supervised learning. Regression A regression model predicts a numeric value. Classification Classification models predict the likelihood that something belongs to a category. Unsupervised Learning Unsupervised learning models make predictions by being given data that does not contain any correct answers. An unsupervised learning model's goal is to identify meaningful patterns among the data. In other words, the model has no hints on how to categorize each piece of data, but instead it must infer its own rules. Clustering A commonly used unsupervised learning model employs a technique called clustering. The model finds data points that demarcate natural groupings. Clustering differs from classification because the categories aren't defined by you. Reinforcement Learning Reinforcement learning models make predictions by getting rewards or penalties based on actions performed within an environment. A reinforcement learning system generates a policy that defines the best strategy for getting the most rewards. Generative AI is a class of models that creates content from user input. Generative AI can take a variety of inputs and create a variety of outputs We can discuss generative models by their inputs and outputs, typically written as "type of input"-to-"type of output." How does generative AI work? At a high-level, generative models learn patterns in data with the goal to produce new but similar data. Generative models are like the following: Comedians who learn to imitate others by observing people's behaviors and style of speaking Artists who learn to paint in a particular style by studying lots of paintings in that style Cover bands that learn to sound like a specific music group by listening to lots of music by that group How does generative AI work? To produce unique and creative outputs, generative models are initially trained using an unsupervised approach, where the model learns to mimic the data it's trained on. The model is sometimes trained further using supervised or reinforcement learning on specific data related to tasks the model might be asked to perform, for example, summarize an article or edit a photo. Generative AI Generative AI is a quickly evolving technology with new use cases constantly being discovered. For example, generative models are helping businesses refine their ecommerce product images by automatically removing distracting backgrounds or improving the quality of low-resolution images.  Conclusion That ends our presentation on introduction on Machine Learning concepts. Learning about the core concept behind ML. Thank you!