Applied AI with Deep Learning 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 Applied AI with Deep Learning online course is provided as a part of the IBM advanced data science certification programme for giving much-needed insights into the models of deep learning for students. The Applied AI with Deep Learning certification syllabus will dive into the fundamentals of linear algebra, neural networks, deep learning frameworks and advanced concepts such as time series forecasting, natural language processing and image recognition. The students will be trained at an advanced level of learning and it provides a sharable and verified certificate upon completion. The Applied AI with Deep Learning by Coursera is provided in the English language with subtitles in many languages and the learning can be done easier with flexible deadlines for submission of projects and assignments.

Course and certificate fees

Applied AI with Deep Learning

Particulars	Fee Amount in INR
Introduction to Git and GitHub (audit only)	Free
Introduction to Git and GitHub - 1 month	Rs. 4,074/-
Introduction to Git and GitHub - 3 months	Rs. 8,149/-
Introduction to Git and GitHub - 6 months	Rs. 12,224/-

certificate availability

Yes

certificate providing authority

Coursera

Eligibility criteria

Education

Candidates must have coding skills in Python language and basic knowledge of linear algebra for seeking admission in the Applied AI with Deep Learning certification.

Certification Qualification Details

To receive the verified credential, students must complete both the practice quizzes and the final examination in each module of the Applied AI with Deep Learning certification within the time frame specified.

What you will learn

Machine learning Knowledge of deep learning Knowledge of apache spark

The Applied AI with Deep Learning programme will help the students with

The candidate will get detailed knowledge of cloud architecture used in artificial intelligence and deep learning concepts.
The candidates will be exposed to convolutional and recurrent neural networks.
The candidates will be introduced to the TensorFlow concept with sequential models in Keras.
The candidates will learn about feedforward food as well as recurrent neural networks.
With Applied AI with Deep Learning certification syllabus the candidates will advance into concepts of SystemML, Pytorch installation and packages, tensor creation, competition graph and linear models.
The candidates will use applications in deep learning with anomaly detection, time series forecasting, batch size, auto encoder, image classification and text classification.
The candidates will learn about scaling and deployment on Apache Spark through Apache SystemML.

The syllabus

Week 1: Introduction to deep learning

Videos

A warm welcome from John Cohn, IBM Fellow Watson IoT
Introduction - Romeo Kienzler
Introduction - Ilja Rasin
Introduction - Niketan Pansare
Course Logistics
Cloud Architectures for AI and DeepLearning
Linear algebra
Deep feedforward neural networks
Convolutional Neural Networks
Recurrent neural networks
LSTMs
Auto encoders and representation learning
Methods for neural network training
Gradient Descent Updater Strategies
How to choose the correct activation function
The bias-variance tradeoff in deep learning

Readings

IBM Digital Badge
Video summary on environment setup
Where to get all the code and slides for download
Link to Github

Practice Exercise

DeepLearning Fundamentals

Week 2: Deep Learning Frameworks

Videos

Introduction to TensorFlow
Neural Network Debugging with TensorBoard
Automatic Differentiation
Introduction video
Keras overview
Sequential models in Keras
Feed forward networks
Recurrent neural networks
Beyond sequential models: the functional API
Saving and loading models
What is SystemML (1/2)
What is SystemML (2/2)
PyTorch Installation
PyTorch Packages
Tensor Creation and Visualization of Higher Dimensional Tensors
Math Computation and Reshape
Computation Graph, CUDA
Linear Model

Reading

Link to files in Github

Practice Exercises

TensorFlow
TensorFlow 2
Apache SystemML
PyTorch Introduction

Week 3: Deep Learning Applications

Videos

Introduction to Anomaly Detection
How to implement an anomaly detector (1/2)
How to implement an anomaly detector (2/2)
How to deploy a real-time anomaly detector
Introduction to Time Series Forecasting
Stateful vs. Stateless LSTMs
Batch Size
Number of Time Steps, Epochs, Training and Validation
Training Set Size
Input and Output Data Construction
Designing the LSTM network in Keras
Anatomy of an LSTM Node
Number of Parameters
Training and loading a saved model
Classifying the MNIST dataset with Convolutional Neural Networks
Image classification with Imagenet and Resnet
Autoencoder - understanding Word2Vec
Text Classification with Word Embeddings

Practice Exercises

Anomaly Detection
Sequence Classification with Keras LSTM Network
Image Classification
NLP

Week 4: Scaling and Deployment

Videos

Run Keras Models in Parallel on Apache Spark using Apache SystemML
Computer Vision with IBM Watson Visual Recognition
Text Classification with IBM Watson Natural Language Classifier

Readings

Exercise: Scale a Deep Learning Model on IBM Watson Machine Learning
Link to Github

Practice Exercise

Methods of parallel neural network training

Admission details

Filling the form

In order to apply, students must complete the steps below for the Applied AI with Deep Learning online programme

Step 1: Students must go to the website mentioned. https://www.coursera.org/learn/ai

Step 2: The students must choose "enrol" from the drop-down menu.

Step 3: The students must then complete the form's qualifications.

Step 4: To gain access to the course content, you must first pay the course fee.

Scholarship Details

Note: Financial aid is available to enrolled students, but the percentage awarded has not been disclosed.

How it helps

The Applied AI with Deep Learning certification benefits the candidates who wish to advance into the areas of machine learning, artificial intelligence and deep learning. The certificate has an exam that has assignments and basic eligibility to enrol and that itself speaks about the quality and level of rigour provided by the coursework for the candidates pursuing the course. The test, which requires a significant amount of time in the form of reading materials and assignments for final certification.

The credential includes a signature and is formally attested by majorly IBM Research-based instructors who are world-renowned experts in this field, When added to the portfolio, the credential has a huge amount of traction in the candidate's profile, allowing him or her to add it to their resume as well as social networking platforms like Linkedin, where they can engage with prospective recruiters and other people interested in pursuing a career in deep learning.

It is easier for recruiters who work in this field to note and shortlist applicants for interviews when the credential is shared in addition to the individual's resume or CV. An applicant who is in the process of seeking several jobs in technical fields would be able to negotiate a good remunerative offer and would also be able to have an advantage over other applicants for a promotion or a shift in the job profile.

Instructors

Mr Romeo Kienzler
Data Scientist
IBM

Other Masters

Mr Niketan Pansare
Senior Software Engineer
IBM

Ph.D

Mr Tom Hanlon
Training Director
IBM

Mr Max Pumperla
Deep Learning Engineer
IBM

Ph.D

Mr Ilja Rasin
Data Scientist
IBM

FAQs

What is the cost of enrolling in the online programme?

There is no charge decided as the Applied AI with Deep Learning fee since the registration and learning of the programme are free. But after 7 days of free trial ends, candidates have to pay Rs. 3583 as fees.

Can I apply for any scholarship or financial aid for pursuing this programme?

Yes, during their Applied AI with Deep Learning training, students would be granted scholarship assistance.

Is there any eligibility for this course?

Yes, the Applied AI with Deep Learning certification will need the candidate to have the expertise as a programmer in Python programming with basic knowledge of linear algebra.