Deep Learning - A Complete User Guide

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 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.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Machine Learning

Lecture 2 Types of Machine Learning

Lecture 3 Linear Regression Part 1

Lecture 4 Linear Regression Part 2

Lecture 5 Logistic Regression Part 1

Lecture 6 Logistic Regression Part 2

Lecture 7 Decision Tree Algorithm

Lecture 8 Overfitting and Underfitting Concepts

Lecture 9 Random Forest Algorithm

Lecture 10 Naive Bayes Algorithm

Lecture 11 Support Vector Machine Algorithm

Section 3: Artificial Neural Network (ANN)

Lecture 12 Artificial Neural Network Architecture

Lecture 13 Numerical on ANN

Lecture 14 Back Propagation Algorithm and Numerical

Lecture 15 Techniques to avoid Overfitting problem

Lecture 16 Chain Rule

Lecture 17 Vanishing Gradient problem

Lecture 18 Exploding Gradient Problem

Section 4: Convolutional Neural Network (CNN)

Lecture 19 Working of Convolutional Neural Network (CNN)

Lecture 20 Convolution, Padding, Stride and Pooling Operation in CNN

Section 5: Recurrent Neural Networks (RNN) and NLP

Lecture 21 Introduction to RNN and NLP

Beginners in AI, ML, and Deep Learning