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Free Download Gradient Descent for Data Analysts Optimize ML Models
Download this premium online course featuring high-quality video training, step-by-step lessons, practical demonstrations, and expert instruction. With Gradient Descent for Data Analysts Optimize ML Models, you'll gain practical knowledge through structured learning, hands-on examples, and real-world applications. This comprehensive eLearning resource is ideal for students, professionals, freelancers, and lifelong learners looking to develop valuable skills and stay current with modern industry practices at their own pace.
Published 8/2026
Created by Anishabrata Ghosh
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 101 Lectures ( 1h 39m ) | Size: 240.2 MB
Gradient Descent for Data Analysts: Optimize ML Models
What you'll learn
⚡ Understand Gradient Descent, gradients, loss functions, and learning rates through intuitive explanations and real-world analogies.
⚡ Implement Gradient Descent from scratch in Python and train simple machine learning models without ML libraries.
⚡ Master Batch, Stochastic, and Mini-Batch Gradient Descent, Momentum, Adam, and learning rate schedules.
⚡ Visualize optimization landscapes, local vs global minima, saddle points, and convergence in deep learning.
Requirements
❗ No prior knowledge of Machine Learning or Deep Learning is required. Basic Python programming is helpful but not mandatory. Familiarity with high school algebra is beneficial. A computer with internet access to run the Python examples.
Description
Gradient Descent is one of the most important algorithms in Machine Learning and Deep Learning. Every modern AI model—from linear regression to neural networks — uses optimization techniques based on Gradient Descent to learn from data. This course is designed to help you understand these concepts from the ground up through intuitive explanations, visualizations, and hands-on Python examples.
You'll begin by learning the fundamentals of optimization, loss functions, gradients, and learning rates before exploring how Gradient Descent minimizes error step by step. The course then introduces optimization landscapes, local and global minima, saddle points, and convergence, helping you develop a strong conceptual understanding of how machine learning models improve during training.
You'll implement Gradient Descent from scratch in Python, compare Batch, Stochastic, and Mini-Batch Gradient Descent, and explore advanced optimization algorithms such as Momentum, RMSProp, Adam, and learning rate scheduling. Throughout the course, practical demonstrations and quizzes reinforce every concept.
Whether you're a beginner, a Data Science student, or a Python developer preparing for Deep Learning, this course provides the optimization foundation needed to understand how modern AI systems learn. By the end of the course, you'll confidently apply Gradient Descent concepts and be well prepared for more advanced Machine Learning and Deep Learning topics.
Who this course is for
⭐ Whether you're a beginner starting your AI journey, a student of Machine Learning or Data Science, or a Python developer looking to strengthen your fundamentals, this course will help you master Gradient Descent through intuitive explanations, visualizations, and hands-on Python implementations before moving on to advanced Deep Learning concepts.
Homepage
https://www.udemy.com/course/gradient-descent-for-data-analysts-optimize-ml-models
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