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Computer Vision and Natural Language Processing
BeginnerFree LearningComputer Vision

Computer Vision and Natural Language Processing

by UptoSkills Team, Divy Aakarsh

Missions

10

Quests

60

Games

27

XP

340

Coins

35

Free Learning

Certificate optional — ₹499, or ₹199 on Pro

Self-paced
Certificate

Key Benefits

Earn XP & Level Up

Gain 340 XP to unlock badges and achievements

Collect Coins

Earn 35 coins to redeem rewards

Professional Certificate

QR-verified and recruiter-checkable — ₹499, or ₹199 on Pro

Unlock Achievements

Complete challenges and earn exclusive badges

About This League

This league provides foundational knowledge and practical skills in Computer Vision and Natural Language Processing. Students will learn core concepts and implement algorithms using Python. The curriculum prepares learners for placement and internship opportunities in AI roles.

What you will learn

Upon completion, you will be able to:

  • Implement foundational image processing techniques using Python and libraries like OpenCV.
  • Build and evaluate basic image classifiers using algorithms such as KNN and SVM.
  • Process text data and represent it numerically using methods like TF-IDF and word embeddings.
  • Develop simple sentiment analysis models and understand techniques for text classification.
  • Grasp the fundamentals of deep learning for both vision (CNNs) and text (RNNs).

What this league covers

ModuleWhat it covers
Foundations of Image UnderstandingUnderstand image representation, load and display images with Python, and explore basic image manipulation and edge detection fundamentals.
Text Processing EssentialsLearn NLP basics, clean text data, and master tokenization, stop word removal, stemming, lemmatization, and the Bag-of-Words model.
Feature Extraction in ImagesExplore keypoint detection, SIFT and SURF algorithms, corner and blob detection, and apply feature extraction techniques in Python.
Text Vectorization TechniquesMaster TF-IDF, understand N-grams, learn about Word2Vec, and implement TF-IDF using Scikit-learn.
Image Classification FundamentalsBuild and evaluate simple image classifiers using KNN and SVM, and learn data augmentation for image classification.
Sentiment Analysis BasicsUnderstand sentiment analysis, explore lexicon and rule-based approaches, and train machine learning models for sentiment classification.
Introduction to Deep Learning for VisionGrasp the basics of neurons, layers, activation functions, and introduction to Convolutional Neural Networks (CNNs).
Deep Learning for Text: Recurrent Neural NetworksLearn about sequential data, RNNs, LSTMs, GRUs, and basic RNNs for text generation.
Object Detection BasicsUnderstand object detection principles, bounding boxes, anchor boxes, and explore a simple object detection pipeline.
Text Classification and Named Entity RecognitionApply CNNs and RNNs for text classification, and build systems for Named Entity Recognition (NER).

Learners are equipped to build foundational AI applications in vision and NLP.

A look inside

Computer Vision and Natural Language Processing screen 1Computer Vision and Natural Language Processing screen 2Computer Vision and Natural Language Processing screen 3Computer Vision and Natural Language Processing screen 4

Prerequisites

  • Comfortable writing Python, including lists, dictionaries and functions.
  • Understand supervised learning, training/test splits and overfitting.
  • Comfortable with arrays and matrix operations.

Learning Objectives

  • Understand the fundamental concepts of image representation, including pixels, color spaces, and basic image processing techniques like filtering and edge detection.
  • Identify and explain the core components of Natural Language Processing (NLP), such as tokenization, stemming, lemmatization, and stop word removal.
  • Implement simple image classification models using pre-trained deep learning architectures for introductory computer vision tasks.
  • Apply basic text preprocessing techniques to clean and prepare textual data for subsequent NLP analysis.
  • Describe the relationship between computer vision and NLP, and identify common applications where both fields are integrated.
  • Explain the purpose and basic functionality of popular NLP libraries and tools (e.g., NLTK, spaCy) for text analysis.