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Supervised Machine Learning Using Python
Logistic Regression, Random Forest, Naive Bayes Classifier
Enroll now for early access of e-LMS
Online/ e-LMS
Self Paced
Beginners
8 Weeks
About
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
Program Structure
- Â Introduction to Data Science, Artificial Intelligence and Machine Learning
- Applications of ML
- Types of ML Algorithms
- Supervised ML
- Linear Regression (Simple and Multiple)
- Â Logistic Regression
- Decision Tree
- Random Forest
- Naive Bayes Classifier
- Implementation of Supervised ML using Python
- Mini Project
Participant’s Eligibility
- 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
Important Dates
Registration Ends
2023-03-25
Indian Standard Timing 12:00 PM
Program Dates
2023-03-25 to 2023-03-26
Indian Standard Timing IST 1:00 PM ONWARDS
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.
Mentor Profile
Designation: Professor
Affiliation: Datta Meghe Institute of Education and Research
Dr. Chitra Dhawale is a Professor in the Science and Technology Department of Datta Meghe Institute of Education and Research (Deemed to be University). She received her Ph.D. Degree in Computer Science from S.G.B.Amravati University in 2009. She is having 25+ years of teaching and research experience in computer science. Her area of Expertise includes: Data Science, Machine Learning, Deep Learning, Python Programming, R Programming, Tableau. She/he is the author of 02 books, 08 book chapters and 78 research papers.
Fee Structure
Standard Fee: INR 10,998 USD 198
Discounted Fee: INR 5499 USD 99
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
List of CurrenciesBatches
Live
Certificate
Program Assessment
Certification to this program will be based on the evaluation of following assignment (s)/ examinations:
| Exam | Weightage |
|---|---|
| Mid Term Assignments | 50 % |
| Project Report Submission (Includes Mandatory Paper Publication) | 50 % |
To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.
Program Deliverables
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Future Career Prospects
- Data Scientist: With proficiency in supervised machine learning techniques using Python, you’ll be well-equipped for roles as a data scientist. You can analyze large datasets, develop predictive models, and extract valuable insights to drive business decisions across various industries.
- Machine Learning Engineer: As a machine learning engineer, you can specialize in designing, implementing, and deploying machine learning systems. You’ll work on building and optimizing predictive models for applications such as recommendation systems, natural language processing, and computer vision.
- AI Researcher: If you’re interested in advancing the field of artificial intelligence, this program can serve as a solid foundation for pursuing a career as an AI researcher. You can explore cutting-edge algorithms and techniques, contribute to research projects, and develop innovative solutions to complex problems.
- Data Analyst: Many organizations rely on data analysts to interpret data, perform statistical analysis, and generate actionable insights. With skills in supervised machine learning using Python, you can excel in roles that involve data analysis and visualization, helping businesses make data-driven decisions.
- Business Intelligence Analyst: Business intelligence analysts leverage data to provide strategic insights and support decision-making processes within organizations. Your expertise in supervised machine learning can enhance your ability to uncover patterns, trends, and opportunities that drive business growth.
Job Opportunities
- Quantitative Analyst: Analyze financial data and develop models for trading strategies and risk management.
- Consultant: Offer expertise in data science and machine learning to help companies develop custom solutions and optimize processes.
- Research Scientist: Conduct research in academia or industry to advance the field of machine learning.
- Software Engineer: Develop software applications with machine learning capabilities for various industries.
- Product Manager: Lead the development of data-driven products and services based on machine learning technologies.
Current Participants* Analytics
Country
Profession
Affiliation
Note: The information shown in the above-mentioned analytics is live and may include information that is not completely correct like spelling mistakes, grammatical mistakes , factual errors or even mis representation as this is what participants have entered, the information is currently not edited and or filtered , but at later stages they will be filtered to provide true data representation.
Enter the Hall of Fame!
Take your research to the next level!
Achieve excellence and solidify your reputation among the elite!
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Recent Feedbacks In Other Workshops
The course was more theoretical than practical.
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