Guys! I hope you all are enjoyed reading my earlier article Part – I 10/20, and I trust that would be useful for you. Let’s discuss the rest of the project quickly.

11. Learn to prepare data for your next machine learning project.

Problem Statement & Solution

When you’re dealing with NLP based problem statement, we must focus on “Text Data” preparation before you can start using it for any NLP algorithm. The foremost step is that text cleaning and processing is an important task in every machine learning project, even if we are working on the text-based task and making sense of textual data. So, when dealing with text, we must take extra causes for Text Classification, Text Summarization, understanding Tokenization, and Bag of Words preparation.

The big challenge here is constructing features from Text Data and creating synthetic features, which are really critical tasks. On top of it how to apply machine learning models to develop classifiers is also tricky.

Indeed, this project would help to understand the text classification and susceptible analysis area in various domains.

Key take away and Outcome and of this project.

  • Understanding on
    • NLTK library for NLP
    • Stop words and use in the context of NLP
    • The difference between NLP, NLG, and NLU.
    • TFIDF vector and its significance
    • Text Specific Analytics
      • Sentiment Analysis
      • Text Classification
      • Topic Modelling
      • Text Summarization
      • Tokenization and Bag of Words
    • Part of Speech (POS) tagging
  • Difference between Lemmatization and Stemming
  • What is Binary Text classification and Text classification?
  • How to apply
    • NLP pre-processing for training model
    • Linear SVC for binary classification
    • One Vs. Rest Classifier for Multi-Label Classification
    • Multi-Label Binarizer for Multi-Label Classification
  • Understanding the evaluation metrics
    • Precision
    • F1-score
    • Recall

12. Time Series Forecasting with LSTM Neural Network Python

Problem Statement & Solution

 Always Time series prediction problems are a challenging form of predictive modelling problems. The Long Short-Term Memory (LSTM) network is a type of recurrent neural network used in deep learning since very large architectures can be successfully trained. It is used to add the complexity of sequences and dependence among the input variables.

 LSTM is a type of recurrent neural network that can learn the dependence between items in sequence order. This type itself promises to be able to understand the context required to make predictions in terms of TIME-SERIES…

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