Applied Deep Learning Capstone Project

BY
IBM via Edx

Learn how to train, create and test a deep learning model by getting admission to the Applied Deep Learning Capstone Project online course by edX.

Lavel

Expert

Mode

Online

Duration

5 Weeks

Fees

Free

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based
Learning efforts 2-4 Hours Per Week

Course overview

The Applied Deep Learning Capstone Project certification programme is a five-week-long online course curated by IBM. During this capstone project, you will get practical, hands-on experience in developing, training and testing a Deep Learning model. For this purpose, you will be using either PyTorch or Keras.

edX’s Applied Deep Learning Capstone Project is an advanced-level programme that is also a part of a Deep Learning professional certificate of edX. With the help of this course, you will learn how to preprocess and load data for a real-life problem, create a model and finally validate it. 

The Applied Deep Learning Capstone Project programme by edX requires only two to four hours per week to study. You will learn through video lessons and ultimately master the procedure of generating a Deep Learning pipeline. At last, you are required to present a project report to showcase your competence in the domain of Deep Learning. EdX provides two tracks to pursue the self-paced course; verified and free audit. In the former mode, the learners can access the programme unlimited and get a certificate of completion. In the free mode, they can audit it for a limited duration. 

The highlights

  • Self-directed learning
  • E-certificate
  • Advanced-level course
  • Video lectures in English
  • Offered by IBM
  • 5-weeks program
  • Requires 2-4 hours/week
  • Data Analysis and Statistics course
  • Free access

Program offerings

  • Industry expert instructor
  • Project work
  • Self-paced learning
  • Paid certification
  • Free course access
  • Digital learning
  • 5-weeks training
  • Video lectures
  • English subtitles for videos
  • An offering of ibm

Course and certificate fees

Type of course

Free

  • You can join the Applied Deep Learning Capstone Project program for free.
  • A payment of Rs. 12,442 is compulsory if you want the certificate.

Applied Deep Learning Capstone Project fee structure

Course option

Fee in INR

Applied Deep Learning Capstone Project (course content audit)

Free

Applied Deep Learning Capstone Project (content + certification)

Rs. 12,442 

certificate availability

Yes

certificate providing authority

IBM

certificate fees

₹12,442

Eligibility criteria

Enrolment in the Applied Deep Learning Capstone Project training requires you to complete all the online courses in edX’s Deep Learning Professional Certification Programme.

What you will learn

Statistical skills Data science knowledge Knowledge of deep learning

By the time you complete the Applied Deep Learning Capstone Project training, you will be proficient in:

  • Developing a Deep Learning model to solve a problem
  • Improving models using real data by implementing Deep Learning knowledge
  • Deciding what type of Deep Learning technique to use in different situations
  • The procedure of generating a Deep Learning pipeline
  • Communicating and presenting the results of Deep Learning projects

The syllabus

Chapter 1

Loading Data
  • Getting Started
  • 1.1 Loading Data – PyTorch
  • 1.2 Loading Data - Keras

Chapter 2

Data Preparation
  • 2.1 Processing Data – PyTorch
  • 2.2 Processing Data - Keras

Chapter 3

Assessment Training Model
  • 3.1 Training Models – PyTorch
  • 3.2 Training Models – Keras

Chapter 4

Compare Two Models
  • 4.1 Compare Two Models – PyTorch
  • 4.2 Compare Two Models - Keras

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  • Course Rating and Feedback

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Admission details

Step 1 – Visit the website and land on the Applied Deep Learning Capstone Project course page.

Step 2 – Locate and click on the ‘Enroll now’ button near the top. A registration page will open.

Step 3 – Sign up by providing the details and clicking on the ‘Create account’ button. Or log in using an Apple, Microsoft, Facebook, or Google account.

Step 4 – You will be enrolled in the Applied Deep Learning Capstone Project programme after a successful registration. Now, you can choose the paid plan or free audit as per your requirements.


Filling the form

There is no lengthy application form to enrol in the Applied Deep Learning Capstone Project online training. You just have to visit the edX website and create an account by providing basic information such as your email address, country, and full name. 

You also have to write a public username and generate a new password for your edX account. Another way of signing in is by linking either of these existing accounts – Facebook, Google, Microsoft or Apple. You can enrol in the Applied Deep Learning Capstone Project course for free by logging in to the portal.

How it helps

The Applied Deep Learning Capstone Project course by edX offers a practical learning experience with a mandatory project report presentation. Also, you are free to learn at your convenience as the course follows a self-paced approach.

Furthermore, when you opt for the Applied Deep Learning Capstone Project certification, you get an officially verified credential to add to your resume. It is easily shareable to LinkedIn and will help you avail of better job opportunities in the relevant field.

Instructors

Mr Joseph Santarcangelo

Mr Joseph Santarcangelo
Data Scientist
IBM

Ph.D

FAQs

What is the category of this course?

The Applied Deep Learning Capstone Project course comes under the Data Analysis and Statistics subject.

Who curated the Applied Deep Learning Capstone Project course?

This course is curated and offered by IBM.

Who is the instructor for the Applied Deep Learning Capstone Project training?

Joseph Santarcangelo, a Data Scientist at IBM, is the course instructor.

How many hours do I have to spare for learning?

You need two to four hours per week to complete the Applied Deep Learning Capstone Project programme.

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