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Cognitive Computing

Python & AI Assignments Repository

This repository contains a series of Python-based assignments covering basic programming, data manipulation, visualization, machine learning, NLP, deep learning, and cognitive systems applications. Each assignment builds progressively, introducing new libraries and concepts.

Assignments Overview

1. Python Fundamentals

  • Description: Covers basic Python programming concepts such as variables, loops, conditionals, and functions.
  • Libraries: None (pure Python)

2. Data Structures in Python

  • Description: Focuses on Python data structures including lists, tuples, and dictionaries.
  • Libraries: None (pure Python)

3. Data Manipulation with Pandas

  • Description: Demonstrates data operations using Pandas, including reading CSV files, filtering, grouping, and aggregation.
  • Libraries: pandas

4. Numerical Computation with NumPy

  • Description: Covers numerical operations using NumPy, including arrays, vectorized operations, and matrix manipulations.
  • Libraries: numpy

5. NumPy and Data Visualization with Matplotlib

  • Description: Combines NumPy operations with Matplotlib plotting for data visualization.
  • Libraries: numpy, matplotlib.pyplot

6. Exploratory Data Analysis (EDA) with Pandas, NumPy, Matplotlib, and Seaborn

  • Description: Implements EDA on datasets using NumPy, Pandas, and visualization libraries Matplotlib and Seaborn.
  • Libraries: numpy, pandas, matplotlib.pyplot, seaborn

7. Advanced Data Analysis and Visualization

  • Description: Extends EDA with advanced visualization techniques and statistical analysis using the same libraries as Assignment 6.
  • Libraries: numpy, pandas, matplotlib.pyplot, seaborn

8. Machine Learning with Logistic Regression

  • Description: Implements a classification model using Logistic Regression on datasets like Iris, including preprocessing, train-test split, and evaluation.
  • Libraries: sklearn.datasets, sklearn.linear_model, sklearn.model_selection, sklearn.preprocessing, sklearn.metrics, matplotlib.pyplot, seaborn

9. Natural Language Processing (NLP) Fundamentals

  • Description: Covers NLP preprocessing tasks such as tokenization, stemming, lemmatization, and text cleaning.
  • Libraries: nltk, re

10. Text Feature Extraction and Deep Learning for NLP

  • Description: Extends NLP by implementing vectorization (CountVectorizer, TfidfVectorizer), cosine similarity, and sequence modeling with Keras LSTM networks.
  • Libraries: nltk, sklearn.feature_extraction.text, tensorflow.keras, numpy

11. Cognitive Assistant for Medicine Label Reading

  • Description: Implements a cognitive system to read and interpret labels on medicine bottles using computer vision and NLP techniques.
  • Libraries: numpy, pandas, tensorflow (optional for OCR/ML)

12. Healthcare Chatbot Deployment

  • Description: Deploys a chatbot for healthcare applications using ChatterBot, capable of answering user queries interactively.
  • Libraries: chatterbot, chatterbot.trainers

Conclusion

This repository demonstrates progressive learning in Python and AI, starting from basic programming, moving to data analysis, visualization, machine learning, NLP, deep learning, and cognitive systems. Each assignment is self-contained and provides hands-on experience with the respective concepts and libraries.