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All LessonsFree Lessons (4)

Introduction to the Course

Course Overview Who Can Take This Course?

Getting Started

Overview of AI, ML, DL, and Supervised/Unsupervised Learning Image Classification Image Classification Using Python Programming Data Types The Model-Centric Approach vs. the Data-Centric Approach Quiz: Artificial Intelligence Basics

Understanding Noisy Data, Label Noise, and Its Types

Noisy Data and Label Noise Simulating Unbiased Mislabeling Using Python Programming Simulating Biased Mislabeling Using Python Programming Quiz: The Fundamentals of Noisy Data, Label Noise, and Its Types

Introduction to Convolutional Neural Network (CNN)

Image Classification Using Convolutional Neural Networks Input Layer and Convolution Layer Implementation of Pooling Layers Using Python Programming Hyperparameter Tuning Quiz: Fundamental Concepts of CNN Architecture

Project

Cats vs Dogs Classification with Convolutional Neural Networks

Performance Comparison of Mislabeled and Clean Dataset

Implementing CNNs for Image Classification in Python Unbiased Mislabeling in Image Classification Using CNNs Biased Mislabeling in Image Classification Using CNNs Dealing with Mislabeled Datasets Using Pretrained Models Quiz: Evaluating the Performance of Mislabeled and Clean Datasets

Dealing with Imbalance Dataset

Imbalanced Datasets Methods for Transforming Imbalanced Data into Balanced Data Dealing with Imbalanced Datasets in Python Programming Quiz: Essential Concepts for Dealing with Imbalanced Datasets

Mini Project

Gauge the Impact of Imbalanced and Mislabeled Datasets

Course Assessment

Comprehensive Quiz

Wrap Up

Appendix

Project

Dealing With Small Datasets In ML

Deal with Mislabeled and Imbalanced Machine Learning Datasets/

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Conclusion

Conclusion

Final words on the impact of imbalanced and mislabeled datasets in ML.

We'll cover the following...

Final words

Final words

Congratulations!

You have completed the course. Give yourself a round of applause!

To wrap up, in this course, we comprehensively covered the fundamentals of ML ...