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Machine Learning and Deep Learning Using TensorFlow

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Machine Learning and Deep Learning Using TensorFlow, available at $79.99, has an average rating of 4.85, with 40 lectures, based on 47 reviews, and has 620 subscribers.

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You will learn about In depth understanding of Machine Learning. In depth understanding of the Neural Network. Detailed and step by step theoretical derivation and explanation of a majority of the topics to ensure clear understanding of the subject. You will learn Linear Regression, Logistic Regression, Neural Network, Deep Neural Network (DNN), Convolution Neural Network etc. Multiple hands-on projects using Tensorflow 2 and Python to expose you to some of the highly advanced topics of Tensorflow 2 Hands-on projects are selected to make you familiar with some of the expertise that may be very useful should you need to run a very long analysis in future. This course is ideal for individuals who are Who is this course for? Almost for everyone. Machine Learning is not a topic for one single profession. Machine Learning (along with neural networks) is an immensely powerful tool that may help you to find solutions to some of the problems that one may not know how to solve otherwise. Try this course and see if it gives you better insight to address some of the problems you are working on. or People from a diverse range of professions may find this knowledge useful in their own profession. or Topics are explained in detail. Concepts are developed progressively in a step by step manner. I sometimes spent more than 10 minutes discussing a single slide instead of rushing through it. This should help you to be in sync with the material presented and help you better understand it. or The hands-on examples are selected primarily to make you familiar with some aspects of TensorFlow 2 or other skills that may be very useful if you need to run a large and complex neural network job of your own in the future. or Please watch the first two videos to have a better understanding of the course. It is particularly useful for Who is this course for? Almost for everyone. Machine Learning is not a topic for one single profession. Machine Learning (along with neural networks) is an immensely powerful tool that may help you to find solutions to some of the problems that one may not know how to solve otherwise. Try this course and see if it gives you better insight to address some of the problems you are working on. or People from a diverse range of professions may find this knowledge useful in their own profession. or Topics are explained in detail. Concepts are developed progressively in a step by step manner. I sometimes spent more than 10 minutes discussing a single slide instead of rushing through it. This should help you to be in sync with the material presented and help you better understand it. or The hands-on examples are selected primarily to make you familiar with some aspects of TensorFlow 2 or other skills that may be very useful if you need to run a large and complex neural network job of your own in the future. or Please watch the first two videos to have a better understanding of the course.

Enroll now: Machine Learning and Deep Learning Using TensorFlow

Summary

Title: Machine Learning and Deep Learning Using TensorFlow

Price: $79.99

Average Rating: 4.85

Number of Lectures: 40

Number of Published Lectures: 40

Number of Curriculum Items: 40

Number of Published Curriculum Objects: 40

Original Price: $124.99

Quality Status: approved

Status: Live

What You Will Learn

  • In depth understanding of Machine Learning.
  • In depth understanding of the Neural Network.
  • Detailed and step by step theoretical derivation and explanation of a majority of the topics to ensure clear understanding of the subject.
  • You will learn Linear Regression, Logistic Regression, Neural Network, Deep Neural Network (DNN), Convolution Neural Network etc.
  • Multiple hands-on projects using Tensorflow 2 and Python to expose you to some of the highly advanced topics of Tensorflow 2
  • Hands-on projects are selected to make you familiar with some of the expertise that may be very useful should you need to run a very long analysis in future.

Who Should Attend

  • Who is this course for? Almost for everyone. Machine Learning is not a topic for one single profession. Machine Learning (along with neural networks) is an immensely powerful tool that may help you to find solutions to some of the problems that one may not know how to solve otherwise. Try this course and see if it gives you better insight to address some of the problems you are working on.
  • People from a diverse range of professions may find this knowledge useful in their own profession.
  • Topics are explained in detail. Concepts are developed progressively in a step by step manner. I sometimes spent more than 10 minutes discussing a single slide instead of rushing through it. This should help you to be in sync with the material presented and help you better understand it.
  • The hands-on examples are selected primarily to make you familiar with some aspects of TensorFlow 2 or other skills that may be very useful if you need to run a large and complex neural network job of your own in the future.
  • Please watch the first two videos to have a better understanding of the course.

Target Audiences

  • Who is this course for? Almost for everyone. Machine Learning is not a topic for one single profession. Machine Learning (along with neural networks) is an immensely powerful tool that may help you to find solutions to some of the problems that one may not know how to solve otherwise. Try this course and see if it gives you better insight to address some of the problems you are working on.
  • People from a diverse range of professions may find this knowledge useful in their own profession.
  • Topics are explained in detail. Concepts are developed progressively in a step by step manner. I sometimes spent more than 10 minutes discussing a single slide instead of rushing through it. This should help you to be in sync with the material presented and help you better understand it.
  • The hands-on examples are selected primarily to make you familiar with some aspects of TensorFlow 2 or other skills that may be very useful if you need to run a large and complex neural network job of your own in the future.
  • Please watch the first two videos to have a better understanding of the course.

If you are interested in Machine Learning, Neural Networks, Deep Learning, Deep Neural Networks (DNN), and Convolution Neural Networks (CNN) with an in-depth and clear understanding, then this course is for you.

Topics are explained in detail. Concepts are developed progressively in a step by step manner. I sometimes spent more than 10 minutes discussing a single slide instead of rushing through it. This should help you to be in sync with the material presented and help you better understand it.

The hands-on examples are selected primarily to make you familiar with some aspects of TensorFlow 2 or other skills that may be very useful if you need to run a large and complex neural network job of your own in the future.

Hand-on examples are available for you to download.

Please watch the first two videos to have a better understanding of the course.

TOPICS COVERED

  • What is Machine Learning?

  • Linear Regression

  • Steps to Calculate the Parameters

  • Linear Regression-Gradient Descent using Mean Squared Error (MSE) Cost Function

  • Logistic Regression: Classification

  • Decision Boundary

  • Sigmoid Function

  • Non-Linear Decision Boundary

  • Logistic Regression: Gradient Descent

  • Gradient Descent using Mean Squared Error Cost Function

  • Problems with MSE Cost Function for Logistic Regression

  • In Search for an Alternative Cost-Function

  • Entropy and Cross-Entropy

  • Cross-Entropy: Cost Function for Logistic Regression

  • Gradient Descent with Cross Entropy Cost Function

  • Logistic Regression: Multiclass Classification

  • Introduction to Neural Network

  • Logical Operators

  • Modeling Logical Operators using Perceptron(s)

  • Logical Operators using Combination of Perceptron

  • Neural Network: More Complex Decision Making

  • Biological Neuron

  • What is Neuron? Why Is It Called the Neural Network?

  • What Is An Image?

  • My “Math” CAT. Anatomy of an Image

  • Neural Network: Multiclass Classification

  • Calculation of Weights of Multilayer Neural Network Using Backpropagation Technique

  • How to Update the Weights of Hidden Layers using Cross Entropy Cost Function

  • Hands On

  • Google Colab. Setup and Mounting Google Drive (Colab)

  • Deep Neural Network (DNN) Based Image Classification Using Google Colab. & TensorFlow (Colab)

  • Introduction to Convolution Neural Networks (CNN)

  • CNN Architecture

  • Feature Extraction, Filters, Pooling Layer

  • Hands On

  • CNN Based Image Classification Using Google Colab & TensorFlow (Colab)

  • Methods to Address Overfitting and Underfitting Problems

  • Regularization, Data Augmentation, Dropout, Early Stopping

  • Hands On

  • Diabetes prediction model development (Colab)

  • Fixing problems using Regularization, Dropout, and Early Stopping (Colab)

  • Hands On: Various Topics

  • Saving Weights and Loading the Saved Weights (Colab)

  • How To Split a Long Run Into Multiple Smaller Runs

  • Functional API and Transfer Learning (Colab)

  • How to Extract the Output From an Intermediate Layer of an Existing Model (Colab), and add additional layers to it to build a new model.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Topics Covered

Chapter 2: What is Machine Learning?

Lecture 1: What is Machine Learning?

Chapter 3: Linear Regression

Lecture 1: Steps to Calculate the Parameters

Lecture 2: Gradient Descent using Mean Squared Error Cost Function

Lecture 3: Linear Regression: Simple Example

Chapter 4: Logistic Regression: Classification

Lecture 1: Classification, Decision Boundary, and Perceptron

Lecture 2: Decision Boundary and Sigmoid Function

Lecture 3: Contd: Decision Boundary and Sigmoid Function

Lecture 4: Logistic Regression: Gradient Descent

Chapter 5: In Search for an Alternative Cost-Function

Lecture 1: Introduction to Entropy

Lecture 2: Introduction to Cross-Entropy

Lecture 3: Cross-Entropy (contd.)

Lecture 4: Cross-Entropy Cost Function

Lecture 5: Gradient Descent with Cross Entropy Cost Function

Lecture 6: Logistic Regression: Multiclass Classification

Chapter 6: Introduction to Neural Network

Lecture 1: Logical Operators

Lecture 2: Modeling Logical Operators using Perceptron(s)

Lecture 3: Biological Neuron

Lecture 4: What Is An Image?

Lecture 5: Neural Network: Multiclass Classification

Lecture 6: Calculation of Weights Using Backpropagation Technique

Lecture 7: How to Update the Weights of Hidden Layers using Cross Entropy Cost Function

Lecture 8: Update the Weights of Hidden Layers using Cross Entropy Cost Function (contd)

Lecture 9: Sigmoid to ReLU for Inner Layer, Softmax for Output Layer

Lecture 10: What are Softmax and ReLU activation function?

Chapter 7: For download: All Colab files for hands-on

Lecture 1: Files for download to your computer, and then upload to your google drive to run

Chapter 8: Google Colab. Setup, Mounting Google Drive and Hands On

Lecture 1: Google Colab. Setup and Mounting Google Drive

Lecture 2: Deep Neural Network (DNN) Based Image Classification

Lecture 3: DNN Based Image Classification Using Google Colab. & TensorFlow

Chapter 9: Introduction to Convolution Neural Networks (CNN)

Lecture 1: CNN: Feature Extraction

Lecture 2: CNN: Feature Extraction (Contd.)

Lecture 3: Hands On: CNN Based Image Classification Using Google Colab & TensorFlow

Chapter 10: Regularization, Dropout, and Early Stopping

Lecture 1: Methods to Address Overfitting and Underfitting Problems

Lecture 2: Regularization, Dropout, and Early-Stopping

Lecture 3: Hands On: Regularization, Dropout, and Early Stopping

Chapter 11: Hands On: Various Topics

Lecture 1: Saving Weights and Loading the Saved Weights

Lecture 2: How To Split a Long Run Into Multiple Smaller Runs

Lecture 3: Functional API and Transfer Learning

Lecture 4: How to Extract the Output From an Intermediate Layer of an Existing Model

Instructors

Rating Distribution

  • 1 stars: 0 votes
  • 2 stars: 0 votes
  • 3 stars: 2 votes
  • 4 stars: 12 votes
  • 5 stars: 33 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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