Supervised Machine Learning: Classification 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

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.

Course and certificate fees

Fee Structure

Particulars	Fee 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.