Machine Learning Intermediate

Machine Learning Algorithms Data Preprocessing Neural Networks

This course provides a comprehensive guide to fundamental and advanced topics in Machine Learning (ML). It is tailored for learners who wish to grasp the essentials of machine learning concepts, data preprocessing techniques, model training, neural networks, and practical applications, thus enabling them to apply ML techniques effectively in real-world scenarios. By the end of this course, learners should be able to understand key ML algorithms, handle data using preprocessing techniques, build predictive models, and undertake projects demonstrating learned skills.

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Model Validation Techniques — Overfitting and Underfitting

Overfitting and Underfitting

Understanding and managing overfitting and underfitting is necessary to build balanced machine learning models that generalize well without being biased towards training data.

ML 14 : Overfitting VS Underfitting | Bias VS Variance | Examples

Machine Learning-Bias And Variance In Depth Intuition| Overfitting Underfitting

Lec-43: Bias & Variance Tradeoff Explained: How to Fix Overfitting & Underfitting?

Machine Learning Intermediate

Fundamentals of Machine Learning

Data Preprocessing Techniques

Building and Training Models

Model Validation Techniques

Practical Applications and Case Studies

Introduction to Neural Networks

Tooling and Environment Setup

Version Control and Collaboration

Creating a Machine Learning Portfolio

Overfitting and Underfitting