Machine Learning: Foundations, Tools, and Future Trends

Mastering the Fundamentals and Exploring the Future of Machine Learning

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MODE
Virtual (Google Meet)

TYPE
Mentor Based

LEVEL
Moderate

DURATION
5 Days

Start Date
25 -March -2025

Time
5 PM IST

About

The Machine Learning: Foundations, Tools, and Future Trends workshop provides an in-depth introduction to the principles of machine learning, covering key concepts, tools, and applications. Participants will learn about supervised and unsupervised learning techniques, model training, evaluation, and deployment. The workshop will also highlight future trends such as deep learning, AI ethics, and automation in ML development.

Aim

To equip participants with a strong foundation in machine learning (ML), introduce essential tools and frameworks, and explore emerging trends shaping the future of ML applications across industries.

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Workshop Objectives

To introduce participants to the fundamentals of machine learning.
To provide hands-on experience with ML tools and libraries.
To train participants in data preprocessing, model building, and evaluation.
To explore the deployment and optimization of ML models.
To discuss emerging trends and ethical considerations in ML.

Workshop Structure

Day 1: Understanding Machine Learning & Its Ecosystem

Introduction to Machine Learning (ML) and its relationship with AI
How ML works: Datasets, Algorithms, and Functions
Types of Machine Learning: Supervised, Unsupervised, Semi-Supervised, Reinforcement Learning
Difference between Machine Learning & Deep Learning
Python & ML: Why Python is the preferred language?
Industry Applications: AI-ML in healthcare, finance, security, and automation
Q&A Session

Day 2: ML Models & Algorithms – Building the Foundation

Understanding ML Models: Regression, Classification, Clustering
Supervised Learning: Linear & Logistic Regression, Decision Trees
Unsupervised Learning: K-Means Clustering, PCA
Deep Learning Overview (Introduction to Neural Networks)
Model Optimization: Overfitting, Underfitting, Bias-Variance Tradeoff
Case Study: Real-world ML Model Implementation
Q&A Session

Day 3 : Hands-on with Python & ML Tools

Setting up Python for ML (Jupyter Notebook, Anaconda)
Important Python Libraries

Day 4 : Hands-on with Python & ML Tools

Step-by-Step Building ML Models in Python
Implementing Machine Learning Algorithms using python
Q&A Session

Day 5: Real-World Applications & Career Pathways in ML

Career Paths in Machine Learning: ML Engineer, Data Scientist, AI Researcher
How to Master ML: Certifications, Courses, and Learning Platforms
Building Your ML Portfolio: Open-source projects & Kaggle competitions
Industry Collaboration: ML use-cases in academia & corporate settings
Live Q&A & Closing Remarks

Participant’s Eligibility

Students, researchers, and professionals interested in AI/ML
Data scientists and software developers
Business analysts and engineers exploring ML applications
Entrepreneurs and innovators looking to integrate ML into their businesses

Important Dates

Registration Ends

2025-03-25
Indian Standard Timing 3:00 PM

Workshop Dates

2025-03-25 to 2025-03-29
Indian Standard Timing 5 PM

Workshop Outcomes

Strong foundation in machine learning principles and applications
Hands-on experience with ML tools and frameworks
Ability to build, evaluate, and deploy ML models
Awareness of ethical considerations and future trends in ML
Preparedness for advanced studies or career opportunities in ML and AI

Mentor Profile

Name: DR. CHITRA DHAWALE
Designation: Professor
Affiliation: Datta Meghe Institute of Education and Research (Deemed to be University)

Dr. Chitra Dhawale is a Professor in the Science and TechDeactivatedlogy 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. 2500
USD. 65

Ph.D. Scholar / Researcher

INR. 3000
USD. 75

Academician / Faculty

INR. 3999
USD. 85

Industry Professional

INR. 5999
USD. 125

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 Currencies

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