Udemy - Deep Learning - A Complete User Guide

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Free Download Udemy - Deep Learning - A Complete User Guide
Published 9/2024
Created by Dr. Rahul Mapari
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 21 Lectures ( 6h 24m ) | Size: 1.91 GB

Machine Learning, Deep Learning, NLP
What you'll learn:
To Clear the Fundamentals of Machine Learning
To Explain the Machine Learning Algorithms
To Clear the Concepts of Artificial Neural Network (ANN) and Convolutional Neural Networks (CNN)
To Clear the Concepts of Natural Language Processing (NLP) and its Algorithms
To Provide Hands on Experience by Solving Real Time Problems of ANN, CNN and Deep Neural Networks
Requirements:
No prerequisites, everything will be clear in this course.
Description:
This comprehensive course on deep learning is designed to provide parti[beeep]nts 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, parti[beeep]nts 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.
Who this course is for:
Beginners in AI, ML, and Deep Learning
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