Module 2 - Fundamentals of Machine Learning Archives

Evaluation and Model Selection

Are you ready to embark on the exciting journey of model selection? Before you dive in, it’s essential to understand the tools that will guide you through this process. Performance metrics are like your trusty compass and map, helping you navigate the vast landscape of machine learning models. What is Performance Metrics in Evaluation and Model Selection ? When you’re picking a model, think of performance metrics as your guide. They’re like a compass and map to help you navigate through the process. These metrics serve as the guiding stars that illuminate the path towards an

Unsupervised Learning

What is Unsupervised Learning ? Unsupervised learning stands as a captivating domain within machine learning, where algorithms learn from data without any explicit instructions. Imagine embarking on a journey of exploration without a map, where patterns and structures are discovered organically. This self-guided exploration allows algorithms to categorize, cluster, and interpret complex datasets in innovative ways. Clustering Techniques in Unsupervised Learning At the heart of unsupervised learning lies clustering, a method used to group data points into clusters based on similarity. Two widely recognized techniques are K-means and hierarchical clustering, each with its unique approach to

Supervised Learning

What is Supervised Learning ? Supervised learning is a cornerstone of machine learning, where the goal is to learn a function that maps an input to an output based on example input-output pairs. It figures out a rule from labeled training information. This data includes a bunch of examples where each one has an input and the correct output. In the realm of supervised learning, two primary tasks stand out: regression and classification. These tasks are tackled with various algorithms, each with its strengths and applications. Regression and Classification in machine learning At its core, regression

Data and Feature Engineering

Data preprocessing is like the behind-the-scenes hero in machine learning, like preparing the ground before building a house. It’s the essential step that gets the data ready for further analysis. This process sets the stage for feature engineering, which is like shaping the raw materials into something useful. Let’s explore data preprocessing and feature engineering to see how they play a crucial role in making artificial intelligence work. What is Data Preprocessing in Machine Learning – Data preprocessing Before diving into the heart of feature selection and extraction, let’s acknowledge the unglamorous yet critical step of

Introduction to Machine Learning

At the core of modern technological advancements lies a transformative concept: machine learning. This revolutionary field empowers computers to learn from data, enabling them to make decisions, recognize patterns, and generate insights without explicit programming instructions. From personalized recommendations on streaming platforms to optimizing supply chains, machine learning has permeated various aspects of our lives, reshaping industries and opening up unprecedented possibilities. The Language of Machine Learning Diving into the world of machine learning, one encounters a new lexicon. Terms like “algorithm,” “model,” “training data,” and “inference” become part of your everyday vocabulary. An algorithm is

Category: Module 2 – Fundamentals of Machine Learning

Evaluation and Model Selection

Unsupervised Learning

Supervised Learning

Data and Feature Engineering

Introduction to Machine Learning

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