Research

Python for Interdisciplinary Research

  • Live instructor-led course
  • Internship with live projects
  • Industry-relevant curriculum
  • Online and Offline training available
  • Placement support until you get placed

Learn the fundamentals of machine learning using Python, tailored for interdisciplinary research. This course covers essential Python programming, data analysis, and visualization techniques, as well as key machine learning algorithms. Whether you're a beginner or an advanced researcher, you'll gain hands-on experience with tools like NumPy, Pandas, and Scikit-Learn, enabling you to apply machine learning to a wide range of scientific and academic fields.

  • Intermediate
  • Last updated 21 August, 2024
  • English
Course Description

This course provides a comprehensive introduction to machine learning using Python, tailored for interdisciplinary research. You'll start by mastering Python basics, including programming concepts, data structures, and file operations. The course then dives into key libraries like NumPy and Pandas for data analysis and statistical computing.

You'll learn essential machine learning techniques, covering both supervised and unsupervised learning, data preprocessing, feature engineering, and model evaluation. Practical sessions include hands-on projects with real-world datasets, focusing on regression, classification, clustering, and dimensionality reduction.

Additionally, the course offers insights into applying machine learning in various research domains, supported by case studies. By the end, you'll be equipped to integrate machine learning models into your research, utilizing Python's powerful tools and techniques.

What you’ll learn
  • Understand Python Programming
  • Master Data Analysis Tools
  • Enhance Research Capabilities
  • Implement Real-World Solutions
  • Integrate Machine Learning in Research
Introduction to Python - Overview of Python, including its role in research.
Python Basics - Data Types, Variables, and Operators, Control Structures: If Statement, If-Else Statement, Elif Statement, Nested Conditionals, Loops and Conditionals: For Loop, Using the range() function, While Loop, Nested Loops, Loop Control Statements: Break Statement, Continue Statement, Pass Statement.
Python Functions - Defining Functions, Positional Arguments, Keyword Arguments, Default Parameters, Variable-Length Arguments, Return Type, Scope and Lifetime of Variables, Anonymous Functions (Lambda Expressions), Applying lambda functions with map(), filter(), and reduce(), Recursion, Error Handling in Functions.
Python Modules - Importing and Using Modules.
Data Structures - Lists, Tuples, Dictionaries, and Sets, Operations in different data structures, Sample exercises.
File I/O Operations - Basic File I/O Operations, Open, Read, write functions.
NumPy for Numerical Computing - Arrays and Matrix Operations, Basic Statistical Functions.
Pandas for Data Manipulation - DataFrames: Creation, Indexing, and Slicing, Handling Missing Data, Grouping and Aggregating Data.
Data Visualization with Matplotlib and Seaborn - Basic Plotting: Line Plots, Scatter Plots, and Histograms, Customizing Plots, Visualizing Distributions and Relationships.
What is Machine Learning? - Introduction to machine learning and its significance.
Types of Machine Learning - Supervised Learning, Unsupervised Learning, Reinforcement Learning.
Machine Learning Workflow - Data Collection, Preprocessing and Feature Engineering, Model Training and Evaluation.
Introduction to Scikit-Learn - Basic Structure and Usage, Loading and Splitting Datasets.
Evaluation Metrics - Accuracy, Precision, Recall, F1 Score, Confusion Matrix.
Data Preprocessing Techniques - Handling Missing Values, Encoding Categorical Data, Normalization and Standardization.
Feature Engineering - Feature Selection and Dimensionality Reduction, Principal Component Analysis (PCA), Feature Scaling.
Linear Regression - Simple and Multiple Linear Regression, Assumptions and Interpretations.
Polynomial Regression - Application of Polynomial Regression.
Use Cases - Predicting continuous outcomes (e.g., stock prices, chemical reactions).
Logistic Regression - Binary and Multiclass Classification, ROC and AUC.
Decision Trees and Random Forests - Understanding Decision Boundaries, Feature Importance.
Use Cases - Disease prediction, sentiment analysis.
K-Means Clustering - Distance Metrics, Choosing the Number of Clusters (K).
Hierarchical Clustering - Dendrograms and Agglomerative Clustering.
Use Cases - Market segmentation, image segmentation.
Principal Component Analysis (PCA) - Application of PCA in data analysis.
Use Cases - Reducing complexity in high-dimensional data.
Support Vector Machines (SVM) - Linear and Non-linear SVMs, Kernels, Hyperparameter Tuning.
Introduction to Neural Networks - Basic Architecture, Activation Functions.
Introduction to Deep Learning (Optional) - Overview of deep learning concepts and applications.
Use Cases - Spatial image classification, Chemical Compound Classification.
Case Studies - Analysis of machine learning applications in interdisciplinary research areas.
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Frequently Asked Questions

This course is designed for researchers, students, and professionals from various disciplines who want to apply machine learning techniques using Python in their research projects.

Basic knowledge of programming is recommended but not mandatory. The course starts with an introduction to Python, making it accessible for beginners.

You will learn Python programming, data analysis with libraries like NumPy and Pandas, and machine learning techniques such as regression, classification, and clustering. The course also covers practical applications in interdisciplinary research.

The course duration is 30 hours, typically spread over 10 days.

You will work primarily with Python and its libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn. The course will also guide you in setting up your development environment.

Yes, the course includes hands-on labs and exercises, ensuring that you gain practical experience in applying the concepts learned.

Absolutely. The course is designed to equip you with skills that can be directly applied to various research fields, enhancing your ability to solve complex research problems using machine learning.

Yes, the course is also beneficial for industry professionals looking to incorporate machine learning into their work or to upskill in data analysis and Python programming.
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