Supervised Machine Learning Using Python
Logistic Regression, Random Forest, Naive Bayes Classifier
Online/ e-LMS
Self Paced
Beginners
2 Days (2 Hours/ Day)
4 Hours
Program 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.
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.
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
Program 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: Professor, 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
Student
INR. 1599
USD. 70
Ph.D. Scholar / Researcher
INR. 1599
USD. 70
Academician / Faculty
INR. 1599
USD. 70
Industry Professional
INR. 1599
USD. 70
Standard Fee: INR 11,998 USD 200
Discounted Fee: INR 5999 USD 100
Certificate
Program Assesment
- Relevance: Does the program cover topics and techniques relevant to your learning goals and professional needs in the field of supervised machine learning?
- Content Depth: Assess the depth and breadth of the program’s content. Does it cover fundamental concepts as well as advanced topics? Are there practical examples and real-world applications included?
- Â Experience: Evaluate the extent of hands-on experience provided in the program. Does it offer opportunities to apply supervised learning algorithms using Python in practical exercises or projects?
- Instructor Quality: Consider the quality of instruction. Are the instructors knowledgeable and experienced in the subject matter? Do they provide clear explanations and helpful guidance throughout the program?
- Learning Resources: Review the availability and quality of learning resources, such as lecture materials, code samples, datasets, and additional readings. Do these resources support your understanding and application of the concepts?
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.
