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Machine Learning: Linear Models for Classification

by Pooja Umathe | Computer Programming

4 Lessons 51:24
  1. 1. Linear Models For Classification 17:13
  2. 2. Linear Models for Classification Types 15:56
  3. 3. Naïve Bayes Algorithm 9:14
  4. 4. Overfitting and Underfitting 9:01



About the course

In this course students will learn what is classification and classification models, types of classification- binary and multiclass, logistic regression and its types, Naïve Bayes classification, and its types, overfitting, and underfitting along with regularization and optimization for machine learning models.

Machine learning is one of the liveliest areas of artificial intelligence. Machine learning algorithms allow computers to learn new things without being programmed. They use statistics to better understand the massive amounts of data that we create every day. These newer algorithms help machines classify images, sounds, and videos. They can answer our questions, discover new drugs, and even write songs.


  • Artificial Intelligence
  • Computing
  • Machine Learning
  • Programing
  • STEM


Hello there! I am Pooja Umathe and I am a data scientist. I build machine learning models and manage data science life cycle to predict future. Currently, I am working at Candlelit Therapy as a data scientist. I helped several organization optimize their business processes and improve productivity and delivery using machine learning and artificial intelligence. I hold a master's degree in Data Science from Saint Peter's University in 2020. I did under-graduation in electronics and computers engineering. Currently I live in New Jersey, USA and I have been tutoring for 4 years around the world. I find tutoring incredibly beautiful job to teach others because the powerful thing about learning is that no one can take it away from you.

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