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Definition ,scope and applications, Current Industry scenario and requirement of skill set, Hardware & software requirement for execution, Frameworks for implementation & comparison, Software installation with necessary IDE
Python basic & revision, Python data structures & function, Pandas installation and applications for machine learning, Numpy installation and application for machine learning, Matplotlib installation and visualization applications, Seaborn installation and visualization applications
Basic statistics techniques for ML, Data types in ML and preprocessing methods, Data exploration techniques for ML, Types of machine learning methods, Overview of implementation of machine learning, Introduction to Scikit Learn Library
First tutorial of basic ML with supervised method, Feature selection criteria of data used in ML, Bias and variance influence on ML model, Resampling methods and its requirement, Evaluation metrics of ML model, Regression methods implementation using Scikit Learn, Classification methods types using Scikit Learn
Tutorial on logical regression Decision Tree implementation, KNN method implementation, SVM method Implementation, Naive Bayes method implementation, Ensemble methods need and types, Random forest method implementation
Regularisation methods and implementation, Algorithmic tuning with hyper parameters, Unsupervised methods, K means clustering method and implementation, PCA method implementation
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