Deep Neural Networks with PyTorch by IBM via Coursera: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based

Course overview

The Deep Neural Networks with PyTorch certification course will teach candidates, how to use Pytorch to create deep learning models. It is a part of the IBM AI Engineering Professional Certificate. There are a total of 6 courses in that specialisation. The Deep Neural Networks with PyTorch training course is the fourth one of them. Artificial intelligence or AI is revolutionising whole industries by transforming the way businesses exploit knowledge to make decisions across sectors. Organisations need skilled AI engineers who use cutting-edge approaches such as machine learning algorithms and deep learning neural networks to provide their companies with data-driven actionable intelligence in order to remain competitive. This Technical Certificate of 6 courses is designed to equip candidates with the tools which are needed to excel as an Artificial Intelligence or Machine Learning engineer in their career.

Using programming languages such as Python, candidates will master the basic principles of machine learning and deep learning, which includes supervised and unsupervised learning. Candidates will also obtain a digital badge from IBM acknowledging their proficiency in AI engineering in addition to receiving a Technical Certificate from Coursera.

Course and certificate fees

The Deep Neural Networks with PyTorch certification fee depends, and varies on a month to month basis.

Deep Neural Networks with PyTorch Fees Payment:

Head	Amount
Deep Neural Networks with PyTorch, 1 Month	Rs. 4,085
Deep Neural Networks with PyTorch, 3 Months	Rs. 8,171
Deep Neural Networks with PyTorch, 6 Months	Rs. 12,256

certificate availability

Yes

certificate providing authority

Coursera

Who it is for

Python programmer Machine learning engineer

The candidates who take up this course will usually become python programmers, and ML Engineer.

Eligibility criteria

Certification Qualifying Details

For getting certification for the Deep Neural Networks with PyTorch certification by Coursera candidates should complete the whole 7 weeks training. The candidates enrolled for free auditing will not get a certificate of completion. Those who have subscribed by paying the fee will get the certificate.

What you will learn

Knowledge of deep learning

After the completion of the Deep Neural Networks with PyTorch certification syllabus candidates will learn these in depth:

The candidate will know how to use Python libraries for Deep Learning applications
The candidate can create Deep Neural Networks using PyTorch and will illustrate and apply their knowledge of related machine learning methods and Deep Neural Networks.

The syllabus

Week 1: Tensor and Datasets

Videos

Overview of Tensors
Two-Dimensional Tensors
Tensors 1D
Simple Dataset
Differentiation in PyTorch
Dataset

Practice exercises

Tensors
Two-Dimensional Tensors
Derivatives in PyTorch
Simple Dataset
Datasets

Week 2: Linear Regression

Videos

Linear Regression Prediction
Loss
Linear Regression Training
Cost
Gradient Descent
PyTorch Linear Regression Training Slope and Bias
Linear Regression PyToch

Practice Exercise

Prediction in One Dimension
Loss
Linear Regression Training
Cost
Gradient Descent
PyTorch Linear Regression Training Slope and Bias
Training Parameters in PyTorch

Week 2: Linear Regression PyTorch Way

Videos

Stochastic Gradient Descent Addsub
Optimization in PyTorch
Mini-Batch Gradient Descent
Training, Validation and Test Split PyTorch
Training, Validation and Test Split

Practice Exercise

Quiz: Stochastic Gradient Descent
Optimization in PyTorch
Mini-Batch Gradient Descent
Training and Validation Data PyTorch

Week 3: Multiple Input Output Linear Regression

Videos

Multiple Linear Regression Prediction
Linear Regression Multiple Outputs
Multiple Linear Regression Training
Multiple Output Linear Regression Training

Practice Exercise

Multiple Output Linear Regression
Multiple Linear Regression Prediction

Week 3: Logistic Regression for Classification

Videos

Linear Classifiers
Bernoulli Distribution and Maximum Likelihood Estimation
Logistic Regression: Prediction
Logistic Regression Cross Entropy Loss

Practice Exercise

Linear Classifiers
Logistic Regression: Prediction
Linear Classifiers
Logistic Regression Cross Entropy Loss
Bernoulli Distribution and Maximum Likelihood Estimation

Week 4: Softmax Regression

Videos

Softmax
Softmax PyTorch
Softmax Function: Using Lines to Classify Data

Practice Exercises

Softmax Function: Using Lines to Classify Data
Softmax PyTorch Quiz
Softmax Prediction

Week 4: Shallow Neural Networks

Videos

What's a Neural Network
Neural Networks with Multiple Dimensional Input
More Hidden Neurons
Backpropagation
Multi-Class Neural Networks
Activation Functions

Practice Exercises

Neural Networks
Neural Networks with Multiple Dimensional Inputs
More Hidden Neurons
Backpropagation
Multi-Class Neural Networks
Activation Functions

Week 5: Deep Networks

Videos

Deep Neural Networks
Dropout
Deeper Neural Networks: nn.ModuleList()
Gradient Descent with Momentum
Neural Network initialization Weights
Batch Normalization

Practice Exercise

Deep Neural Networks
Dropout
Deeper Neural Networks: nn.ModuleList()
Gradient Descent with Momentum
Neural Network initialization
Batch Normalization

Week 6: Convolutional Neural Network

Videos

Convolution
Multiple Input and Output Channels
Activation Functions and Max Polling
Convolutional Neural Network
Convolutional Neural Network
TORCH-VISION MODELS
GPU in PyTorch

Practice Exercises

Convolution
Convolutional Neural Network
Activation Functions and Max Pooling
TORCH-VISION MODELS
Convolutional Neural Networks

Week 7: Peer Review

Reading

Setup

Admission details

To apply for Deep Neural Networks with PyTorch classes admission, candidates need to follow the below given steps:

Step 1: Visit the official programme URL https://www.coursera.org/learn/deep-neural-networks-with-pytorch

Step 2: There is a tab Enroll for free which should be clicked to proceed ahead

Step 3: Create your login id via LinkedIN or Facebook id

Step 4: Access the programme for 7 days free trial.

Step 5: Once 7 days come to an end, the candidate needs to then make a fee payment to continue ahead

Scholarship Details

In case a candidate is not able to make the fee payment, then he/she can do so by seeking financial assistance from Coursera.

How it helps

The Deep Neural Networks with PyTorch certification benefits by being a course is part of the IBM AI Engineering Professional Certificate. By completing the programme candidates will receive a Digital badge from IBM acknowledging their proficiency in AI engineering. That will be a privilege to candidates. Artificial Intelligence and Machine Learning have a big future in this world. Their influences in industries are big nowadays. So as an Artificial Intelligence engineer or Machine Learning engineer candidates will be receiving an up gradation in their career.

Candidates will be able to find their dream jobs after completing the specialisation. The completion certificate and the Digital Badge from IBM are very valuable when candidates are attending an interview. Job opportunities for AI and Machine Learning engineers are higher in the present world. So the programme will be very worthy. The benefits are not just in the job sector. By learning the course candidates may be able to apply the learning outcomes to their project works very directly. As mentioned above, the opportunities are wide.

Instructors

Mr Joseph Santarcangelo
Data Scientist
IBM

Ph.D

FAQs

How much time is needed to complete Deep Neural Networks with PyTorch programme?

The candidate is supposed to dedicate 31 hours to complete this programme.

If I cannot make the fee payment, can I seek a scholarship?

Currently, Coursera is providing financial assistance to candidates who are not able to afford the fees for the course.

I do not want to continue the programme, can I quit in between?

The programme gives the facility to access the programme for free for 7 days. So after that once payment is done, the candidate cannot get a refund for the programme.

Who is offering this Deep Neural Networks with PyTorch online course ?

The Deep Neural Networks with PyTorch programme is offered by IBM.

What are some of the subjects that will be covered under this programme?

The Deep Neural Networks with PyTorch programme will deal with subjects namely, Logistic Regression, Softmax Regression, Linear Regression and many others.