Supervised Machine Learning Using Python
Logistic Regression, Random Forest, Naive Bayes Classifier
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: The aim of this program is to equip participants with the knowledge and skills necessary to effectively apply supervised machine learning techniques using Python. Through hands-on instruction and practical exercises, the program seeks to provide a comprehensive understanding of key concepts, algorithms, and methodologies in supervised learning, empowering participants to develop predictive models, analyze data, and make informed decisions across various domains and applications. By the end of the program, participants will have the proficiency to leverage Python libraries and tools for supervised learning tasks, enabling them to extract valuable insights, drive innovation, and solve real-world problems in diverse fields such as healthcare, finance, marketing, and more.
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 :
- Data scientists, machine learning engineers, analysts, researchers
- Professionals from diverse industries
- Those seeking proficiency in supervised machine learning with Python
- Beginners and those looking to deepen their understanding
- Individuals interested in building predictive models and extracting insights from data
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.
