Data Science : Begineer to Advance

Chapters

1Data Science Foundations and Workflow

2Python Programming Essentials for Data Science

3Working with Data Sources and SQL

4Data Wrangling with NumPy and Pandas

5Data Cleaning and Preprocessing

6Exploratory Data Analysis and Visualization

7Probability and Statistics for Data Science

8Machine Learning Foundations

9Supervised Learning Algorithms

10Unsupervised Learning and Dimensionality Reduction

11Model Evaluation, Validation, and Tuning

12Feature Engineering and ML Pipelines

13Time Series Analysis and Forecasting

14Natural Language Processing

Text Preprocessing and Tokenization Bag of Words and TF IDF N gram Features and Smoothing Sentiment Analysis Basics Topic Modeling LSA and LDA Word Embeddings Word2Vec and GloVe Sequence Models RNN and LSTM Transformers and Attention Transfer Learning with BERT Named Entity Recognition Text Classification Pipelines Question Answering Overview Summarization Basics Evaluation Metrics for NLP Ethics and Bias in NLP

15Deep Learning, Deployment, and MLOps

Courses/Data Science : Begineer to Advance/Natural Language Processing

Natural Language Processing

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Process and model text data from traditional vectorization to modern transformer based approaches for NLP applications.

Content

15 of 15

Ethics and Bias in NLP

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