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Data Science & Machine Learning

Specialization in Computer Vision

About This Course

The Data Science and Machine Learning course with a specialization in computer vision covers image processing, object detection, and pattern recognition using advanced algorithms and deep learning techniques.

  • 8 Months Course
  • 4 Months Internship
  • Certificate

Tools and Programs You Will Learn

scikit-learn
icons8-jupyter-100
icons8-keras-100
icons8-pytorch-100
icons8-opencv-100

Key Points

  • Expert Instructors to Learn from industry leading professionals.
  • Flexible Learning to Study anytime anywhere at your pace
  • Interactive Courses to Engaging materials for the best experience
  • Affordable Excellence and High-quality education at a reasonable cost

Scope

Computer Vision Engineer | Image Processing Specialist | Deep Learning Researcher (Computer Vision) | Computer Vision Scientist | Autonomous Vehicle Engineer | Robotics Vision Engineer | Video Analytics Specialist | Biomedical Image Analyst

Curriculum

The Rootsys International has been exclusively founded to groom young leaders in the art of savoir-vivre or social etiquette, Our curriculum and programmes for complete personality enhancement are designed to be a life changing experience.

MODULE 1 Foundations of Data Science

– Introduction to data science and its applications

– Data lifecycle and key concepts

– Ethics and best practices in data science

– Introduction to machine learning algorithms and models.

– Supervised, unsupervised, and reinforcement learning.

– Model evaluation and validation techniques.

– Introduction to Jupyter Notebook: Understanding the interactive

computing environment for data science and machine learning.

– Exploring Jupyter Notebook interface and features: Markdown cells, code cells, and keyboard shortcuts.

– Data exploration and visualization with Jupyter Notebook: Using libraries like Matplotlib and Seaborn.

– Collaboration and sharing with Jupyter Notebook: Exporting notebooks, version control.

– Introduction to Google Colab: Overview of the cloud-based Jupyter Notebook environment provided by Google.

– Google Colab for collaborative projects: Real-time collaboration, sharing

notebooks, and accessing resources like GPUs and TPUs for training models

– Python fundamentals: Variables, data types, operators, and control structures.

– Functions and modules: Defining, calling, and organizing code into reusable units

– Object-oriented programming (OOP) in Python: Classes, objects, inheritance,and polymorphism.

– File handling and input/output operations: Reading from and writing to files.

– Error handling and exceptions: Handling runtime errors gracefully.

– Advanced Python topics: Decorators, generators, context managers, and comprehension techniques.

– Fundamentals of statistics: descriptive statistics, probability distributions.

– Linear algebra essentials: vectors, matrices, matrix operations.

– Calculus basics: derivatives, integrals.

– Introduction to Git: Version Control System.

– Basic Git Commands.

– Branching and Merging.

– Collaboration with Git.

– Practicing Command Prompt to seek codes and logics.

– Using the codes and logics in the appropriate circumstanses.

AI tools we use common:

ChatGPT, Gemini, Copilot, Blackbox,Phind

– Introduction to NumPy: Arrays, indexing, and operations.

– Data Manipulation with Pandas: Series, DataFrames, and Data Wrangling.

– Data Cleaning and Preprocessing Techniques.

– Working with Missing Data and Outliers.

– Introduction to Data Visualization: Importance and Principles.

– Visualization with Matplotlib: Creating plots, charts, and graphs.

– Advanced Visualization with Seaborn: Styling and customization.

– Introduction to Relational Databases.

– SQL fundamentals for data retrieval and manipulation.

– Data access with Python using SQLAlchemy.

– NoSQL databases: Overview of document, key-value, and columnar

databases.

– Advanced data manipulation and analysis with Pandas.

– Handling missing data and outliers.

– Time series analysis and manipulation.

– Introduction to Flask Web Framework.

– Building RESTful APIs for serving machine learning models.

– Introduction to Flask: Setting up a Flask project, routing, and request handling.

– RESTful architecture principles: HTTP methods, status codes, and resource endpoints.

– Building API endpoints: Handling requests, processing data, and returning responses.

– API documentation: Generating documentation with tools like Swagger and Postman.

– Basics of neural networks.

– Fundamentals of Deep Learning: Neural Networks, Activation Functions.

– Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

– Convolutional Neural Networks (CNNs) for image classification.

– Fundamentals of Computer Vision

– Applications of Computer Vision in Various Fields: Exploring real-world

applications in healthcare, automotive, robotics, and more

– Image Representation and Digital Image Basics using NumPy and PIL.

– Image Filtering and Convolution Operations: Applying filters and

convolution techniques with OpenCV and skimage.filters.

– Image Transformation and Enhancement Techniques.

– Feature Detection and Description Algorithms with OpenCV and scikit-learn.

– Feature Matching and Correspondence Techniques.

– Feature Representation and Descriptor Extraction.

– Understanding object detection methods such as R-CNN, YOLO, and SSD.

– Localization Algorithms.

– Training Custom Object Detector using deep learning frameworks like TensorFlow and PyTorch.

– Applications of Object Detection: real-world applications of object detection in areas such as autonomous driving, surveillance, and image-based search engines.

– Basics of Object Tracking and Object Tracking Techniques with OpenCV and scikit-image.

– Online and Offline Object Tracking Algorithms.

– Multiple Object Tracking (MOT) and Tracking by Detection.

– Face detection algorithms: Viola-Jones, HOG-based methods.

– Face recognition techniques: Eigenfaces, LBPH (Local Binary Patterns

– Histograms).

– Flask Web Framework- Recognize.

– Understanding Stable Diffusion for text-to-image generation.

– Implementing text-to-image generation using pre-trained models.

– Fine-tuning Stable Diffusion models for specific applications.

– Real-world Applications of Computer Vision in Industry and Research.

– Project Planning and Problem Definition.

– Data Collection, Annotation, and Preparation.

– Project Implementation, Evaluation, and Deployment.

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