Machine Learning Bootcamp: Theory and Ap

Machine Learning Bootcamp: Theory and Applications using Python

A 5-day comprehensive workshop for environmentalists and wildlife specialists

Unlock the Power of Machine Learning: Learn Python for Environmental Data Analysis and Conservation

Join our 5-day hands-on workshop designed to introduce you to machine learning and its practical applications in environmental science and wildlife conservation. Through this course, you’ll explore the power of machine learning to analyse environmental data, predict outcomes, and drive decision-making for conservation efforts. Learn to handle real-world environmental datasets, from species identification to climate pattern analysis, using Python.

No prior experience required!

Join us on this machine learning journey and contribute to meaningful advancements in environmental science.

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Mr S. Balasubramaniyam

About the Teacher

XR Developer

Independent (GIS + XR) Consultant and Researcher

Speaker description:

S. Bala Subramaniyam is a skilled XR Developer at an MNC, specializing in the development of digital twin models and augmented reality applications. With a Master of Technology in Geoinformatics from the University of Madras, he has extensive experience in integrating machine learning techniques with geospatial analysis to enhance decision-making and operational efficiency in environmental science. Bala has led numerous training sessions on GIS and machine learning applications in research, equipping students and professionals with the skills to utilize open-source spatial software effectively. Bala is also a co-inventor of two patents in the fields of GIS and XR, showcasing his innovative approach to spatial visualization and analysis. Bala’s research has been published in various prestigious journals, and he has presented at numerous conferences, where he has explored the intersections of machine learning, geospatial technologies, and environmental conservation. Join him as he shares insights on harnessing machine learning to drive impactful solutions in environmental science.

🔧 Practical Experience:

This workshop goes beyond theoretical concepts—engage in hands-on projects that will equip you with applicable skills for real-world scenarios.

🛠️ Open to All Skill Levels:

Regardless of whether you're just starting out or have years of experience, our knowledgeable instructors will adapt the curriculum to help you enhance and develop your machine learning expertise.

🌍 Transform Data into Actionable Insights:

Discover how to utilize machine learning techniques to effectively analyse environmental data, converting complex information into practical solutions for conservation.

🎓 Certification Provided:

Upon successful completion, you will receive one of two certificates to enhance your resume and showcase your proficiency in machine learning applications:

Certificate of Attendance – Awarded for attending the live sessions.
Course Completion Certificate – Granted only after passing a test following each session. The test will be shared exclusively during the live session via Google Forms.

COURSE SYLLABUS

Introduction to Machine Learning and Its Applications in Environmental Science

Overview of Machine Learning

- Definition and functioning of machine learning

- Supervised vs. unsupervised learning

- Key terminologies: algorithms, datasets, models, training, validation

Applications of Machine Learning in Environmental Science

- Real-world examples: biodiversity assessment, habitat mapping, species identification, climate change analysis

- Case studies: AI-driven species identification, satellite imagery for deforestation detection

Hands-on Session: Getting Started with Machine Learning Tools

- Introduction to popular ML tools and platforms (Google Earth Engine, TensorFlow, Scikit-learn)

- Setting up Jupyter Notebooks or using Google Colab

Python Fundamentals for Environmentalists

Introduction to Python

- Importance of Python in machine learning and data science

- Setting up the Python environment (Jupyter Notebooks, Google Colab)

Basic Python Syntax and Data Types

- Variables, data types (strings, integers, floats), basic operations

- Data structures: lists, tuples, sets, dictionaries

Control Flow in Python

- Conditional statements (if-else)

- Loops (for, while) and iteration over data structures

Functions and Modules

- Defining functions in Python

- Importing and using modules (math, pandas)

Data Collection, Preprocessing, and Exploration for Environmental Machine Learning

Understanding Environmental Data

- Types of data: satellite imagery, camera trap data, environmental sensors, GPS data, ecological surveys

- Data sources for environmental applications (GBIF, NASA Earth Data, iNaturalist)

- Importance of clean, high-quality data

Data Preprocessing Techniques

- Handling missing data, normalization, scaling

- Labelling data for supervised learning tasks (e.g., species classification from images)

- Introduction to feature engineering for environmental datasets

Hands-on Session: Data Exploration

- Using Python (pandas, numpy, matplotlib) to load and explore sample datasets (e.g., species occurrence, habitat quality)

- Basic visualizations: scatter plots, histograms, correlation matrices

Supervised Learning Models for Wildlife and Environmental Applications

Supervised Learning Models for Classification and Prediction

- Overview of classification algorithms (decision trees, random forests, support vector machines)

- Techniques for predicting species presence/absence and classifying animal behaviour

- Time series models for environmental predictions (climate patterns, species migration)

Hands-on Session: Building a Simple Classification Model

- Using a sample dataset (animal behaviour, habitat types) to create a classification model in Python with scikit-learn

- Evaluating model performance (accuracy, precision, recall)

Unsupervised Learning and Advanced Applications in Environmental Science

Unsupervised Learning for Clustering and Pattern Recognition

- K-means clustering and hierarchical clustering for habitat or population segmentation

- Detecting anomalies in environmental data (illegal wildlife activities, unusual migration patterns)

Hands-on Session: Applying Unsupervised Learning Techniques

- Using sample datasets to implement clustering algorithms and analyse results

- Discussion of practical applications in environmental science and conservation efforts

Final Project Presentation

- Participants present their projects using machine learning techniques for environmental analysis

Feedback and discussion

Details

Date: 9,10,11,14 and 15 December
Time: 7.30- 9.00 PM
Platform: Zoom

Course Fees

Original price: ₹ 1999/-
1st Early Bird: SEATS FULL
2nd Early Bird: ₹1799/- (Valid up to 19th November)
Group Discount (minimum 3 individuals): ₹1499/- per head
Organization Discount (minimum 5 individuals): ₹1399/- per head
Present Interns: ₹999/-
Ex Interns: ₹1599/-

For Registration: Contact the given email id or number

If you fall in the discount category (Group discounts/Present/Ex-Interns), you can pay the discounted amount directly to the bank account below.

Bank details

Account type-current

Account name- Wildlifearc

AC no.: 3346750332

IFSC. :KKBK0002040

UPI ID: Wildlifearc@kotak

After the payment please send a screenshot or the payment along with the name, email addresses and phone numbers of the participants via WhatsApp to Ms. Amrita Tripathy at 9041507489.

GROUP DISCOUNTS ARE TO BE PAID TOGETHER AS A GROUP TO OUR UPI ID (e.g. For 3 members, kindly pay ₹1499*3= ₹4497/- together & not separately)

For Queries contact us:

For more information, visit our website or contact us at:

Contact no: +919969968525

Email id: thenatureseyeevents@gmail.com