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Author: Admin | 2025-04-28
Week 1: Introductory overview of Text Mining- Introductory Thoughts - Data Mining vs. Text Mining- Text Mining and Text Characteristics- Predictive Text Analytics- Text Mining Problems- Prediction & Evaluation- Python as a Data Science PlatformPython for Analytics- Introduction to Python Installation- Jupyter Notebook IntroductionWeek 2: Python Basics- Python Programming Features- Commands for common tasks and control- Essential Python programming concepts & language mechanicsBuilt in Capabilities of Python- Data structures: tuples, lists, dicts, and setsWeek 3: Built in Capabilities of Python- Functions, Namespaces, Scope, Local functions, Writing more reusable generic functions Week 4: Built in Capabilities of Python- Generators- Errors & Exception Handling- Working with filesNumerical Python- N-dimensional array objectsWeek 5: Numerical Python- Vectorized array operations- File management using arrays- Linear algebra operations- Pseudo-random number generation- Random walksPython pandas- Data structures: Series and DataFrameWeek 6: Python pandas- Applying functions and methods- Descriptive Statistics- Correlation and CovarianceWorking with Data in Python- Working with CSV, EXCEL files- Working with Web APIs Week 7: Working with Data in Python- Filtering out missing data, Filling in the missing data, removing duplicates- Perform transformations based on mappings- Binning continuous variables- Random sampling and random reordering of rows- Dummy variables- String and text processing- Regular expressions- Categorical typeData Visualization using Python- Matplotlib Library- Plots & SubplotsWeek 8: Text mining modeling using NLTK- Text Corpus- Sentence Tokenization- Word Tokenization- Removing special Characters- Expanding contractions- Removing Stopwords- Correcting words: repeated characters- Stemming & lemmatization- Part of Speech Tagging- Feature Extraction- Bag of words model- TF-IDF model- Text classification problem- Building a classifier using support vector machine
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