Machine Learning

Step into the advanced world of ensemble learning with this comprehensive course designed to transform your understanding and application of machine learning algorithms. Ensemble learning combines the predictive power of multiple models to create robust, accurate, and generalized models. This course is perfect for data scientists, machine learning engineers, and enthusiasts looking to enhance their skills and tackle complex problems with sophisticated techniques. You will explore the core con...

Unlock the power of decision trees with this in-depth course designed to take you from the basics to advanced techniques. Decision trees are one of the most intuitive and versatile machine learning algorithms, widely used for both classification and regression tasks. This course is ideal for beginners as well as intermediate learners who want to deepen their understanding and application of decision trees in various real-world scenarios. In this course, you will learn how to build, visualize,...

Dive into the world of Machine Learning (ML) with this comprehensive introductory course designed to equip you with the fundamental concepts, techniques, and practical applications of ML. This course is tailored for beginners with a keen interest in understanding how machines can learn from data and make intelligent decisions. You will explore key ML paradigms, including supervised learning, unsupervised learning, and reinforcement learning. Gain hands-on experience with popular ML algorithms...

MACHINE LEARNING

CONCEPT LEARNING

BAYSIAN LEARNING

Vision of the Institute To become a renowned center of outcome based learning and work towards academic, professional, cultural and social enrichment of the lives of individuals and communities. Mission of the Institute M1- Focus on evaluation of learning outcomes and motivate students to inculcate research aptitude by project based learning. M2- Identify, based on informed perception of Indian, regional and global needs, the areas of focus and provide platform to gain knowledge and solutions...

Table of Contents Part 1: Introduction. The big picture of artificial intelligence and machine learning — past, present, and future. Part 2.1: Supervised Learning. Learning with an answer key. Introducing linear regression, loss functions, overfitting, and gradient descent. Part 2.2: Supervised Learning II. Two methods of classification: logistic regression and support vector machines (SVMs). Part 2.3: Supervised Learning III. Non-parametric learners: k-nearest neighbors, decision tree...

It is easily to understand the machine learning and it's fully described in well good experience professor

Machine learning involves training machines to learn from past data, understand and reason. It is more than just learning as it involves prediction and classification of new data. In this article, we will explore the basics of machine learning.