Supervised Machine Learning: Classification

BY
IBM via Coursera

Get more familiarized with the concept of Machine learning by enrolling for the course on Supervised Machine Learning: Classification by Coursera.

Lavel

Intermediate

Mode

Online

Quick Facts

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

Course overview

A part of the multiple series programme, the online session on Supervised Machine Learning: Classification by Coursera provides the students with a detailed emphasis on the main types of modelling that are involved with the genre of machine learning. The students will learn elaborately to train the predictive models that are involved and used across various domains. The Supervised Machine Learning: Classification certification syllabus will be covered on the online platform by the students over a time frame of 24 hours. The course will train the students professionally in the domain of machine learning.

The highlights

  • 24 hours total course duration
  • 100% online programme
  • Subtitles in English
  • Course level intermediate
  • Programme by Coursera
  • Course offered by IBM 
  • Beginner-level course
  • Shareable certificate
  • Deadlines flexible

Program offerings

  • Online course
  • Subtitles
  • Assignments
  • Video lectures
  • Practice sets
  • Quizzes.

Course and certificate fees

Fee Structure

ParticularsFee Amount in INR
Supervised Machine Learning: Classification (audit only)
Free
Supervised Machine Learning: Classification - 1 month
Rs. 3,275/-
Supervised Machine Learning: Classification - 3 months
Rs. 6,550/-
Supervised Machine Learning: Classification - 6 months
Rs. 9,825/-
certificate availability

Yes

certificate providing authority

Coursera

Eligibility criteria

Education

Before applying for the Supervised Machine Learning: Classification online course the students need to have a fair understanding of-  Data Analysis, Statistics, Probability, Linear Algebra, Calculus, Data cleaning and Python.

Certification Qualification Details

The Supervised Machine Learning: Classification certificate will be issued after students complete the course.

What you will learn

Machine learning

After completing the Supervised Machine Learning: Classification training the students will be learning about-

  • The applicants will learn the applications of tree-ensemble models.
  • Developed skills in the domain of decision trees.
  • The use of undersampling and oversampling will be covered in the course.
  • The Supervised Machine Learning: Classification certification syllabus covers the details of unbalanced classes.
  • The usage and filling of data sets will be covered by the applicants.

The syllabus

Module 1: Logistic Regression

Videos
  • Welcome
  • Introduction: What is Classification?
  • Introduction to Logistic Regression
  • Classification with Logistic Regression
  • Logistic Regression with Multi-Classes
  • Implementing Logistic Regression Models
  • Confusion Matrix, Accuracy, Specificity, Precision, and Recall
  • Classification Error Metrics: ROC and Precision-Recall Curves
  • Implementing the Calculation of ROC and Precision-Recall Curves
  • [Optional] Logistic Regression Lab - Part 1
  • [Optional] Logistic Regression Lab - Part 2
  • [Optional] Logistic Regression Lab - Part 3
Readings
  • About this course
  • Optional: Download data assets
  • [Optional] Download Assets for Demo Lab: Logistic Regression 
  • Summary/Review
Quizzes
  • Logistic Regression
  • Logistic Regression Labs
  • Module 1 Graded Quiz Logisitic Regression
App Items
  • Demo Lab: Logistic Regression
  • Practice Lab: Logistic Regression

Module 2: K Nearest Neighbors

Videos
  • K nearest neighbors for classification
  • K nearest neighbors decision boundary
  • K nearest neighbors distance measurement
  • K Nearest Neighbors Pros and Cons
  • K nearest neighbors with feature scaling
  • K nearest neighbors notebook - part 1
  • K nearest neighbors notebook - part 2
  • K nearest neighbors notebook - part 3
Reading
  • Summary/Review
Quizzes
  • K Nearest Neighbors
  • K Nearest Neighbors Labs
  • Module 2 Graded Quiz - KNN
App Items
  • Demo Lab: K Nearest Neighbors
  • Practice Lab: K Nearest Neighbors

Module 3: Support Vector Machines

Videos
  • Introduction to support vector machines
  • Classification with support vector machines
  • The support vector machines cost function
  • Regularization in support vector machines
  • Introduction to support vector machines gaussian kernels
  • Support vector machines gaussian kernels - part 1
  • Support vector machines gaussian kernels - part 2
  • Support Vector Machines Workflow
  • Implementing support vector machines kernel models
  • [Optional] Support vector machines notebook - part 1
  • [Optional] Support vector machines notebook - part 2
  • [Optional] Support vector machines notebook - part 3
Reading
  • Summary/Review
Quizzes
  • Support Vector Machines
  • Support Vector Machines Kernels
  • Support Vector Machines Labs
  • Module 3 Graded Quiz: Support Vector Machines
App Items
  • Demo Lab: Support Vector Machines
  • Practice Lab: Support Vector Machines

Module 4: Decision Trees

Videos
  • Overview of Classifiers
  • Introduction to decision trees
  • Building a decision tree
  • Entropy-based splitting
  • Other decision tree-splitting criteria
  • Pros and cons of decision trees
  • [Optional] Decision trees notebook - part 1
  • [Optional] Decision trees notebook - part 2
  • [Optional] Decision trees notebook - part 3
Readings
  • [Optional] Download Assets for Demo Lab: Decision Trees
  • Summary/Review
Quizzes
  • Decision Trees
  • Decision Trees Labs
  • Module 4 Graded Quiz: Decision Trees
App Items
  • Demo Lab: Decision Trees
  • Practice Lab: Decision Trees

Module 5: Ensemble Models

Videos
  • Ensemble-Based Methods and Bagging - Part 1
  • Ensemble-Based Methods and Bagging - Part 2
  • Ensemble-Based Methods and Bagging - Part 3
  • Random Forest
  • [Optional] Bagging Notebook - Part 1
  • [Optional] Bagging Notebook - Part 2
  • [Optional] Bagging Notebook - Part 3
  • Review of Bagging
  • Overview of Boosting
  • Adaboost and Gradient Boosting Overview
  • Adaboost and Gradient Boosting Syntax
  • Stacking
  • [Optional] Boosting Notebook - Part 1
  • [Optional] Boosting Notebook - Part 2
  • [Optional] Boosting Notebook - Part 3
Readings
  • [Optional] Download Assets for Demo Lab: Bagging 
  • [Optional] Download Assets for Demo Lab: Boosting and Stacking 
  • Summary/Review
Quizzes
  • Bagging
  • Random Forest
  • Bagging Labs
  • Boosting and Stacking
  • Boosting and Stacking Labs
  • Module 5 Graded Quiz
App Items
  • Practice Lab: Random Forest
  • Demo Lab: Bagging
  • Practice Lab: Bagging
  • Demo Lab: Boosting and Stacking
  • Practice Lab: Ada Boost
  • Practice Lab: Stacking For Classification with Python
  • Practice Lab: (Optional) Gradient Boosting

Module 6: Modeling unbalanced classes

Videos
  • Model Interpretability
  • Examples of Self-Interpretable and Non-Self-Interpretable Models
  • Model-Agnostic Explanations
  • Surrogate Models
  • Introduction to Unbalanced Classes
  • Upsampling and Downsampling
  • Modeling Approaches: Weighting and Stratified Sampling
  • Modeling Approaches: Random and Synthetic Oversampling
  • Modeling Approaches: Nearing Neighbor Methods
  • Modeling Approaches: Blagging
Reading
  • Summary/Review
Quizzes
  • Practice: Model interpretability
  • Modeling Unbalanced Classes
  • Module 6 Graded Quiz
Peer Review
  • Course Final Project
App Items
  • Practice Lab: Model Interpretability
  • Practice Lab: Modeling Imbalanced Classes

Admission details


Filling the form

The students have to follow the listed steps to get enrolled for the Supervised Machine Learning: Classification online course.

Step 1: Candidates have to visit the provided link-https://www.coursera.org/learn/supervised-machine-learning-classification

Step 2: The students then are required to click the “enrol” button to get themselves registered in the course.

Step 3: Thereafter they need to sign up with a registered account. Then they can enrol on the programme and access it. 

Scholarship Details

Financial aid is available but the amount has not been specified.

How it helps

The Supervised Machine Learning: Classification certification benefits the students by allowing them a platform to learn and upgrade their professional skills. The Supervised Machine Learning: Classification online course has been formulated by IBM and can be persuaded by students in the online platform.  The certificate is globally recognised. The professionals who will join the course will be guided by industry officials and experts. The Supervised Machine Learning: Classification certificate can be shared by the students over professional platforms. Furthermore, the students will get to access multiple quizzes, lectures and assignments to practice their skills. The course will thus be helping the students to procure better job opportunities. The students in the Supervised Machine Learning: Classification programme will also get to access a seven days trial programme.

Instructors

Mr Mark J Grover

Mr Mark J Grover
Digital Content Delivery Lead
IBM

Mr Miguel Maldonado

Mr Miguel Maldonado
Machine Learning Curriculum Developer
IBM

Ms Svitlana Kramar

Ms Svitlana Kramar
Data Science Content Developer
IBM

Other Masters

Mr Joseph Santarcangelo

Mr Joseph Santarcangelo
Data Scientist
IBM

Ph.D

FAQs

Who is conducting the programme?

The online course is being offered by IBM.

For how long is the course being scheduled?

The Supervised Machine Learning: Classification certification course has been scheduled for a time frame of 11 hours.

Will the classes be held online?

The Supervised Machine Learning: Classification training classes will be held online.

When can I get access to the course guides?

Students can access the course materials only after they get enrolled in the programme.

From where can the syllabus details be accessed?

The students can access the details of the Supervised Machine Learning: Classification online course from the website of Coursera.

What are the course registration fees?

The course registration fee is free for a 7 days trial period. Later, candidates can make the fee payment and enrol for the course. 

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