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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study	Video and Text Based

Course Overview

The Introduction to Machine Learning certification course is offered by Duke University. It will help you know your strengths and apply your skills in the most efficient manner to get work done. It will give you additional knowledge that will make you more confident in your work and perform better at your workplace or start your own business.

The course will give you a brief overview of machine learning models like natural language processing, logistic regression, convolution neural networks, and multilayer perceptrons, while also explaining how these models can be put to use for solving technical problems in various industries like medical, image recognition, and text prediction.

It is a combination of practical as well as theoretical knowledge about Machine learning, that will provide you with a framework for understanding how you can implement data science models and machine learning algorithms. The Introduction to Machine Learning Training Course will allow you to work on a real-world hands-on project that is required in the fast-developing technological world.

The Highlights

Online course
Shareable Certificate
Self-paced learning
Approx. 21 hour long course

Programme Offerings

practice tests
unparalleled technical assistance
self paced training shareable certificate

Courses and Certificate Fees

Fees Informations	Certificate Availability	Certificate Providing Authority
INR 2381	yes	Coursera

Coursera offers this Introduction to Machine Learning certification fee is free. You can learn the skills required and gain theoretical as well as practical knowledge without spending a single penny.

Audit/Free Trial	Free of Cost
Cost after Audit/Free Trial	INR 2,381

Eligibility Criteria

Eligibility Criteria

There are no prerequisites for taking this training. However, a basic understanding of machine learning algorithms, programming languages, and statistics will prove to be beneficial for you.

Certification Qualifying Details

The candidates after the completion will be offered an Introduction to Machine Learning certification by Coursera.

What you will learn

Natural Language ProcessingMachine learning

After completing the Introduction to Machine Learning certification syllabus course, you will master the following skills:

Data Modeling
Probability
Logistic regression
Multilayer perceptron
Convolutional neural networks
Natural language processing

These skills will help you in becoming more efficient in your work and getting a high paying job.

Who it is for

Data Scientist

This course is best suited for:

Data scientists
Complete beginners
IT graduates

Admission Details

The admission process for the Introduction to Machine Learning classes on Coursera is quite easy. To apply, you can check the ‘Audit’/’Free Trial’ version for a period of 7 days. Further, if you want to get access to assignments and peer-to-peer community, you can even purchase the entire course. Get a detailed procedure mentioned below:

Step 1: Log on to www.coursera.com

Step 2: Enter your details (name, email id, etc.)

Step 3: Enter the password

Step 4: Click ‘Join for Free’

Step 5: Go to the course and click ‘Enrol’

Step 6: Purchase the course

Application Details

You can enroll yourself in the program by directly going to the official website of Coursera and making an account. You can enter your email ID, username and password or use either of the social login features provided by Google, Facebook and Apple. Thereafter, you can log in to your account using any device and select the course you want to pursue, then enroll yourself with that course and start learning.

The Syllabus

Videos

Why Machine Learning Is Exciting
What Is Machine Learning?
Interpretation of Logistic Regression
The motivation for Multilayer Perceptron
Multilayer Perceptron Concepts
Multilayer Perceptron Math Model
CNN Math Model
Logistic Regression
Deep Learning
Hierarchical Structure of Images
Advantages of Hierarchical Features
Convolution Filters
Convolutional Neural Network
How the Model Learns
CNN on Real Images
Applications in Use and Practice
Deep Learning and Transfer Learning
Introduction to PyTorch
Example: Document Analysis
Interpretation of Multilayer Perceptron
Transfer Learning
Model Selection
Early History of Neural Networks

Readings

Course Information
Math for Data Science

Practices exercises

Intro to Machine Learning
Logistic Regression
History of Neural Networks
CNN Concepts
CNN Math Model
Applications In Use and Practice
Week 1 Comprehensive
Multilayer Perceptron
Deep Learning
Model Selection

Videos

How Do We Define Learning?
How Do We Learn Our Network?
How Do We Evaluate Our Networks?
How Do We Handle Big Data?
Early Stopping
Model Learning with PyTorch

Practice Exercises

Lesson One
Lesson
Week 2 Comprehensive

Videos

Motivation: Diabetic Retinopathy
Breakdown of the Convolution (1D and 2D)
Core Components of the Convolutional Layer
Pooling and Fully Connected Layers
Training the Network
Transfer Learning and Fine-Tuning
CNN with PyTorch
Activation Functions

Practice Exercises

Lesson One
Lesson 2
Lesson 3
Week 3 Comprehensive

Videos

Introduction to the Concept of Word Vectors
Words to Vectors
Example of Word Embeddings
Neural Model of Text
The Softmax Function
Methods for Learning Model Parameters
More Details on How to Learn Model Parameters
The Recurrent Neural Network
Long Short-Term Memory
Long Short-Term Memory Review
Use of LSTM for Text Synthesis
Simple and Effective Alternative Methods for Neural NLP
Natural Language Processing with TensorFlow
Introduction to Reinforcement Learning
Reinforcement Learning Problem Setup
Example of Reinforcement Learning in Practice
Moving to a Non-Myopic Policy
Q Learning
Extensions of Q Learning
Limitations of Q Learning, and Introduction to Deep Q Learning
Deep Q Learning Based on Images
Connecting Deep Q Learning with Conventional Q Learning

Practice Exercises

Lesson 1
Lesson 2
Lesson 3
Week 4 Comprehensive

Reading

Bonus Lesson: Reinforcement Learning

Evaluation process

On the successful completion of the course which includes classroom videos as well as homework, an exam will be held. You will be awarded a certificate after you qualify for the exam. You can add your certificate to your LinkedIn profile and resume for better job opportunities.

Instructors

Duke University, Durham Frequently Asked Questions (FAQ's)

1: What is machine learning?

Machine learning is basically an application of AI that allows any system to learn and improve on their own without any human interference. It is used to study natural patterns in data that allow you to predict the future and make better decisions.

2: Is choosing machine learning as a career a good choice?

The answer is yes. Going for machine learning as a career is a great option. It is used by almost all industries across the globe and has benefitted them in endless ways. It has got so many good growth and job opportunities which will help you fetch a handsome salary.

3: Can I add the Coursera certificates to my LinkedIn account?

Yes, you can definitely add the Coursera certificates to your LinkedIn account. It will make you stand out from your peers and will increase your chances of getting selected by the employer.

4: What are the top 5 algorithms that every machine learning engineer should know?

The top algorithms that every machine learning engineer should know are K Means Clustering Algorithm, Apriori Algorithm, Artificial Neural Network, Linear Regression, and more.

5: What is an average salary offered to a machine learning engineer in India?

The average salary that a beginning can expect may range between ₹4,00,000 to ₹5,00,000 whereas an engineer with 6 to 10 years of experience can expect to get a salary between ₹7,50,000 to ₹12,00,000. On the other hand, a person in the capacity of ML data associate can be offered a salary between ₹2300000 to

6: What skills are required to get started with the Introduction to Machine Learning online course?

If you want to get started with machine learning, it is advised that you should be aware of the basics of the following concepts like Statistics, Probability, Linear Algebra, calculus, and programming languages.

7: Which are the top 3 languages to know for a career in machine learning?

The top 3 best languages for machine learning are python, R programming language, and Java Script etc.

8: Why should anyone opt for the Machine learning course offered by Coursera?

It is not mandatory for anyone to take up a machine learning course offered by Coursera only, there are so many other platforms that offer this course. Coursera has got some perks over them like you can learn for free on this platform and they provide you with all the required assistance you need.

9: What are the benefits of taking Coursera Plus?

Coursera Plus offers you several benefits. You get to have unlimited access to 3,000+ courses, professional certifications, and specialization in fields like business, data science, computer science, and many more.