Supervised Machine Learning Using Python
Don’t Just Analyze Data, Make It Work For You
Skills you will gain:
About Program:
An immersive program on “Introduction to Data Science, Artificial Intelligence, and Machine Learning,” where participants will delve into the fundamentals and applications of these cutting-edge technologies. Led by industry experts, the program will cover essential topics such as supervised learning algorithms, including linear regression, logistic regression, decision trees, random forests, and naive Bayes classifier, along with hands-on implementation using Python. Participants will have the opportunity to engage in practical exercises and a mini-project, gaining valuable insights and skills to leverage data for informed decision-making and predictive analytics. Don’t miss this chance to enhance your expertise in data science and machine learning and stay ahead in today’s data-driven world.
Aim: To understand the basic concepts of supervised machine learning and the different types of supervised learning algorithms.
Program Objectives:
- Machine Learning Engineer
- Data Scientist
- Data Analyst
- Business Intelligence Analyst
- Artificial Intelligence Developer
- Data Engineer
- Research Scientist
- Predictive Modeler
- Analytics Consultant
- Machine Learning Researcher
What you will learn?
- Introduction to Data Science, Artificial Intelligence and Machine Learning
- Applications of ML
- Types of ML Algorithms
- Supervised ML
- Linear Regression (Simple and Multiple)
- Logistic Regression
- Decision Tree
- Random Forest
- Naive Bayes Classifier
- Implementation of Supervised ML using Python
- Mini Project
Fee Plan
Intended For : Students, Scientist, Developer, Engineer, PhD Scholars, Academician, Industry Professionals
Career Supporting Skills
Program Outcomes
- Comprehensive Understanding: Participants will gain a thorough understanding of the principles and applications of data science, artificial intelligence, and machine learning, enabling them to grasp the fundamental concepts underlying predictive analytics and data-driven decision-making.
- Practical Skills Development: Through hands-on exercises and implementation using Python, participants will develop practical skills in applying supervised learning algorithms such as linear regression, logistic regression, decision trees, random forests, and naive Bayes classifier to real-world datasets.
- Ability to Apply ML Techniques: Participants will be equipped with the knowledge and skills necessary to apply machine learning techniques to solve business problems, optimize processes, and extract insights from data across various industries.
- Mini Project Completion: By working on a mini-project, participants will have the opportunity to apply the concepts and techniques learned throughout the program to a real-world scenario, gaining practical experience and demonstrating their proficiency in data science and machine learning.
