Get updates on Eligibility, Admission, Placements Fees Structure
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. 25 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 2480 | yes | Coursera |
Introduction to Machine Learning Fees Structure :
Fees components | Free |
Purchase Course (with certificate) | Rs. 2,480 |
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
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
This course is best suited for:
- Data scientists
- 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?
- Logistic Regression
- Interpretation of Logistic Regression
- Motivation for Multilayer Perceptron
- Multilayer Perceptron Concepts
- Multilayer Perceptron Math Model
- Deep Learning
- Example: Document Analysis
- Interpretation of Multilayer Perceptron
- Transfer Learning
- Model Selection
- Early History of Neural Networks
- Hierarchical Structure of Images
- Convolution Filters
- Convolutional Neural Network
- CNN Math Model
- How the Model Learns
- Advantages of Hierarchical Features
- CNN on Real Images
- Applications in Use and Practice
- Deep Learning and Transfer Learning
- Introduction to PyTorch
Readings
- Course Information
- Math for Data Science
Assignments
- Week 1 Comprehensive
- Intro to Machine Learning
- Logistic Regression
- Multilayer Perceptron
- Deep Learning
- Model Selection
- History of Neural Networks
- CNN Concepts
- CNN Math Model
- Applications In Use and Practice
Ungraded Labs
- Python Prerequisites
- PyTorch Installation
- Coding Environments
Videos
- How Do We Define Learning?
- How Do We Evaluate Our Networks?
- How Do We Learn Our Network?
- How Do We Handle Big Data?
- Early Stopping
- Model Learning with PyTorch
Assignments
- Week 2 Comprehensive
- Lesson One
- Lesson 2
Ungraded Labs
- Logistic Regression
- Multi-Layer Perceptron (MLP) Assignment
Videos
- Motivation: Diabetic Retinopathy
- Breakdown of the Convolution (1D and 2D)
- Core Components of the Convolutional Layer
- Activation Functions
- Pooling and Fully Connected Layers
- Training the Network
- Transfer Learning and Fine-Tuning
- CNN with PyTorch
Assignments
- Week 3 Comprehensive
- Lesson One
- Lesson 2
- Lesson 3
Ungraded Labs
- Convolutional Neural Networks
- CNN Assignment
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
Assignments
- Week 4 Comprehensive
- Lesson 1
- Lesson 2
- Lesson 3
Ungraded Labs
- Natural Language Processing
- Natural Language Processing Assignment
Videos
- Word Vectors and Their Interpretation
- Relationships Between Word Vectors
- Inner Products Between Word Vectors
- Intuition Into Meaning of Inner Products of Word Vectors
- Introduction of Attention Mechanism
- Queries, Keys, and Values of Attention Network
- Self-Attention and Positional Encodings
- Attention-Based Sequence Encoder
- Coupling the Sequence Encoder and Decoder
- Cross Attention in the Sequence-to-Sequence Model
- Multi-Head Attention
- The Complete Transformer Network
Videos
- Introduction to Reinforcement Learning
- Reinforcement Learning Problem Setup
- Example of Reinforcement Learning in Practice
- Reinforcement Learning with PyTorch
- 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
Assignments
- Reinforcement Learning Quiz
- Q Learning Quiz
- Deep Q Learning Quiz
Ungraded Labs
- Reinforcement Learning
- Reinforcement Learning Assignment
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.