Machine Learning-OB

Description

This comprehensive course covers the fundamentals of Machine Learning including supervised and unsupervised learning algorithms, model evaluation, feature engineering and more. Learn how to process and represent data, perform model evaluation, and feature engineering, and understand machine learning concepts. Discuss the future and impact of Machine Learning on society, including ethics and safety concerns. Gain a strong understanding of Machine Learning and its applications by the end of the course.

Prerequisites

This curriculum of Machine Learning (Basic & Advance) course has been designed for all levels, regardless of your prior knowledge of analytics, statistics, or coding. Familiarity with mathematics is helpful for this course.

Key Learning Outcomes:

Upon completion of this course, students will be able to:

1. Define machine learning and its different types (supervised and unsupervised) and understand their applications.
2. Apply supervised learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), and k-nearest neighbours (k-NN).
3. Implement unsupervised learning techniques, such as K-means clustering.
4. Evaluate machine learning models and perform hyperparameter tuning to improve model performance.
5. Perform feature engineering and dimensionality reduction techniques, such as feature extraction, feature selection, feature scaling, and PCA.
6. Analyse the future and trends of machine learning, including its impact on society and the ethics and safety concerns associated with machine learning.
7. Synthesize the concepts and techniques of machine learning into a comprehensive understanding of the field.

Target Audience: 

This course is ideal for anyone who wishes to learn the details of data science and pursue a career in this growing field of Artificial Intelligence, Machine Learning, Deep Learning, Data Analytics & Data Science.

Test & Evaluation

• During the program, the participants will have to take all assignments given to them for better learning.
• At the end of the program, a final assessment will be conducted.

Certification

•  All successful participants will be provided with a certificate of completion.
• Students who do not complete the course / leave it midway will not be awarded any certificate.

Delivery Mode & Duration:

Online Live Mode – 120 Hours (60 Hours Online Live sessions + 60 Hours of assignment).