Deep Learning - A Complete User Guide

This comprehensive course on deep learning is designed to provide participants with a thorough understanding of the principles, techniques, and applications of machine learning and deep learning. Whether you are a beginner looking to enter the field of artificial intelligence or an experienced professional aiming to enhance your skills, this course covers a wide range of topics to cater to various levels of expertise. This course will clear the basic concepts of machine learning and deep learning.

Mathematical intuitions of linear and logistic regression, machine learning algorithms like decision tree, random forest, naive bayes, support vector machine etc. , will be cover. Overfitting, under fitting concepts and their techniques of avoidance like dropout, L1, L2 regularization, early stopping is also highlight during this course.

This course also covers the complete understanding of Artificial Neural Network (ANN), Convolutional Neural Network (CNN), recurrent neural network (RNN), Dated Recurrent Units (GRU) and Generative Adversarial Network (GAN) techniques. The natural language processing application are also the part of this course. At the end hands-on practice on real time case studies on linear regression, logistic regression, decision tree, random forest, naive bayes, support vector machine, ANN, CNN, RNN, GAN will be discussed.

By the end of this course, participants will have gained a solid foundation in deep learning, enabling them to apply these techniques to various domains and stay abreast of the rapidly evolving field. Whether you are looking to kickstart a career in AI or enhance your current skills, this course provides a comprehensive and practical guide to deep learning.