Machine Learning Fundamentals In Depth: 16+ Hour Full Course | Part - 1 | Skill-Lync
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Welcome to Skill-Lync’s 16+ Hour Machine Learning Fundamentals In Depth course! This comprehensive, free course is designed for beginners and students looking to dive into the world of machine learning and artificial intelligence. Whether you’re just starting out or need a refresher, this course provides hands-on learning with machine learning projects to help you apply your skills in real-world scenarios.
In this Skill-Lync machine learning course, you’ll begin by mastering the basics of probability and statistics, which are essential for understanding data-driven decision-making. As the course progresses, you’ll explore key machine learning concepts such as supervised learning and unsupervised learning, diving deep into algorithms like linear regression, logistic regression, decision trees, K-means clustering, random forests, SVM, and more.
This Skill-Lync training provides detailed explanations of core techniques such as gradient descent, cost functions, and model evaluation, ensuring you have a solid understanding of how these algorithms work and when to apply them. You’ll also explore advanced topics like Principal Component Analysis (PCA), neural networks, and deep learning—all critical skills for today’s machine learning engineers.
By the end of this Skill-Lync machine learning fundamentals course, you’ll have a complete understanding of the foundations of machine learning, along with practical experience through machine learning projects. Whether you’re a student or an engineer, this course will help you unlock opportunities in the exciting field of machine learning.
Key Topics Covered:
Basics of machine learning and artificial intelligence
Supervised learning (Prediction and Classification)
Unsupervised learning (Clustering and PCA)
Neural networks and deep learning
Practical machine learning projects for students and engineers
Course Highlights:
Full 16+ hours of in-depth machine learning training
Free access with certification upon completion
Designed for both beginners and advanced learners
Perfect for those looking to explore machine learning for engineering
Enroll in this machine learning full course and start building your expertise today! 📈
Timestamps -
0:04:02 - Machine Learning and AI with Python
0:37:29 - Implementing K-Nearest Neighbors (KNN) for Classification
1:07:06 - Difference between Classification and Regression, Multi Linear Regression, and Gradient Descent
1:27:23 - Understanding Loop Iterations and Output in Python
1:48:16 - Standardizing Normal Distributions for Comparison
2:18:38 - Introduction to Machine Learning
2:39:49 - Introduction to Probability and Predictive Analytics
2:57:11 - Understanding Training and Test Data in Linear Regression
3:17:01 - Understanding Training and Test Data Sets in Machine Learning
3:42:52 - Understanding Linear Regression and Gradient Descent in Python
4:12:09 - Basic Probability and Random Variables in Machine Learning
4:42:39 - Generalization vs. Overfitting in Classification Algorithms: A Comparative Analysis
5:07:25 - Introduction to AI and ML Using Python
5:38:00 - Introduction to AI and ML using Python
6:10:51 - Understanding Decision Trees and Overfitting in Machine Learning
6:41:39 - Understanding Random Forest and Bootstrapping in Machine Learning
7:11:48 - Impact of Errors in Machine Learning and Manual Classification
7:37:21 - Understanding Overfitting and Underfitting in Machine Learning
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