Mastering Machine Learning Algorithms (2025) torrent

Mastering Machine Learning Algorithms (2025)

https://WebToolTip.com

Published 4/2025
Created by Pralhad Teggi
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 99 Lectures ( 9h 43m ) | Size: 3.76 GB

A comprehensive, step-by-step guide to key Machine Learning algorithms, use cases, and implementation using Python.

What you'll learn
Gain a solid understanding of the foundational concepts of machine learning including the principles of classification and regression.
Learn the key terminology and mathematical concepts behind machine learning algorithms, such as features, labels, training data, and the role of algorithms.
Explore and master popular machine learning algorithms, including but not limited to linear regression, KNN, decision trees, support vector machines etc.
Acquire practical skills by implementing machine learning algorithms using industry-standard tools and programming languages like Python, scikit learn etc
Work on real-world datasets to gain hands-on experience in preprocessing data, training models, and evaluating performance metrics.

Requirements
Programming Proficiency - Prerequisite : Basic programming skills. Rationale : Participants should have a fundamental understanding of programming concepts, as the course may involve coding exercises and implementations using languages such as Python.
Mathematics and Statistics Background: Prerequisite: Basic understanding of algebra, calculus, and statistics. Rationale: Supervised machine learning often involves mathematical and statistical concepts. Familiarity with concepts like derivatives, linear algebra, probability, and basic statistical measures will aid in understanding algorithms and evaluation metrics.
Introduction to Data Science: Prerequisite: Basic knowledge of data science concepts. Rationale: Participants should be familiar with key data science concepts, such as data types, exploratory data analysis, and the overall data science workflow. This foundation helps in understanding how machine learning fits into the broader context of data science.