Advanced ML Algorithms & Unsupervised Learning

Overview

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In this course, you will explore advanced machine learning algorithms and unsupervised learning techniques to enhance your model-building skills. You’ll learn how to improve model performance using ensemble methods like Random Forest, apply Support Vector Machines (SVM) for complex classification tasks, and reduce dimensionality with techniques like Principal Component Analysis (PCA). By the end of the course, you'll also have an understanding of unsupervised learning through K-Means clustering and an introduction to deep learning. The course begins with an introduction to ensemble learning using Random Forests, where you'll understand how this method improves predictive model accuracy and reduces overfitting. You will then dive into Support Vector Machines (SVM), learning to apply this powerful technique to solve complex classification problems, including how to optimize SVM models for better performance. Next, you will explore Principal Component Analysis (PCA) to reduce dimensionality and optimize model performance, enabling you to work with high-dimensional datasets more effectively. You will also learn about K-Means clustering for unsupervised learning, focusing on how to detect patterns and anomalies in unlabeled data. Finally, the course concludes with an introduction to deep learning, exploring how this rapidly growing field builds on traditional machine learning concepts. You will gain an understanding of how deep learning can be applied to a range of complex tasks such as image and speech recognition. This course is ideal for learners with prior experience in machine learning and Python who are ready to tackle more advanced topics. Familiarity with statistics and linear algebra is helpful.

Syllabus

Random Forest Ensemble

In this module, we will introduce Random Forest, an ensemble learning method that improves upon decision trees. You will learn how to build, optimize, and evaluate Random Forest models using techniques such as grid search and cross-validation. This module focuses on making these models more robust and accurate for real-world applications.

Support Vector Machine

In this module, we will introduce Support Vector Machines (SVM), an advanced algorithm used for classification tasks. You will gain hands-on experience using SVM for classifying polynomial data, as well as techniques for optimizing SVM models to improve prediction accuracy.

Dimensionality Reduction - Principal Component Analysis (PCA)

In this module, we will explore Principal Component Analysis (PCA), a key technique for reducing the dimensionality of complex datasets. You will learn how to compute and apply PCA in practical scenarios, understanding how it can enhance machine learning model performance by simplifying the data while retaining essential information.

Unsupervised Learning using K-Means Clustering

In this module, we will focus on K-Means clustering, a powerful unsupervised learning technique. You will learn how to apply K-Means to segment data, optimize clusters, and evaluate the model's performance. This module emphasizes hands-on experience to ensure you can apply K-Means clustering to real-world datasets effectively.

Introduction to Deep Learning

In this module, we will introduce deep learning, a transformative technology in artificial intelligence. You will learn the core principles behind deep learning models, explore their applications, and gain insight into the potential of deep learning across industries. This module serves as a foundation for more advanced topics in deep learning.