Day 23 RNN, LSTM introduction
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Day – 1 : Introduction, Python Installation, Syntax, variables, data types
Day – 2 : Data types, conditional statements, looping statements
Day – 3 : Functions, lambda, class & object
Day – 4 : Class & object, Modules/packages/libraries
Day – 5 : Machine learning introduction, pathway, data collection
Day – 6 : Pandas Library
Day – 7 : Exploratory data analysis project with pandas on Sales dataset
Day – 8 : Numpy Library
Day – 9 : Visualization with Matplotlib & Seaborn Library
Day – 10 : Exploratory data analysis project with pandas & visualization libraries
(population dataset)
Day – 11 : scikit-learn library : Scaling, encoding, handling null values
Day – 12 : scikit-learn library : Outlier detection & handling, imbalanced dataset
Day – 13 : scikit-learn library : train test split, feature selection
Day – 14 : Machine learning algorithms : linear model, support vector machines
Day – 15 : Machine learning algorithms : Decision tree, ensemble algorithms
Day – 16 : Machine learning algorithms : Naive bayes, KNN, clustering algorithms
Day – 17 : Hyperparameter optimization, Metrics
Day – 18 : Machine learning classification project – Credit card fraud detection
Day – 19 : Machine learning Regression project – Calories burnt prediction
Day – 20 : Machine learning clustering project – Spotify music recommendation
Day – 21 : Deep learning introduction
Day – 22 : ANN project – Churn prediction project
Day – 23 : RNN, LSTM introduction
Day – 24 : Time series analysis introduction
Day – 25 : Time series algorithms
Day – 26 : Time series prediction project – Carbon dioxide emission prediction
Day – 27 : Time series prediction with Neural networks – Stock price prediction
Day – 28 : NLP introduction
Day – 29 : NLP project – Fake news classification using NLP
Day – 30 : NLP project with Neural networks – Movie rating prediction using NLP
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