Machine Learning Notes:

 Machine Learning:

 • Concept of Artificial Intelligence 

 • Types of AI 

 • Fields in which AI is used 

 • Machine learning concept 

 • Types of machine learning 

 • Developmental phases of Machine Learning application 

 • Loading data set 

 • Cleaning dataset 

 • Concept of Supervised machine learning 

 • Concept of Unsupervised machine learning 

 • Libraries used for machine learning 

 • Introduction of PIP utility 

 • Environment setup for Machine Learning 

 • Introduction to Data Science 

 • Types of data • Data set and its classification 

 • Volume, Variety, and Velocity of data 

 • Features of and labels from data set 

 • Training dataset and Testing data set 

 • Data encoding in dataset 

 • Split activity to divide dataset 

 • Pandas library installation 

 • Data set manipulation using pandas library 

 • Series , DataFrame and Panel in Pandas 

 • Numpy installation 

 • Numeric calculations using Numpy 

 • Scipy installation 

 • Anaconda installation 

 • Features of Anaconda and its use in ML 

 • Installation of Matplotlib library 

 • Visualisation techniques using Matplotlib 

 • Supervised machine learning using Classification 

 • Decision Tree algorithm for Classification 

 • K Nearest Neighbour algorithm for Classification 

 • Implementation of K Nearest Neighbour algorithm 

 • Supervised machine learning using Regression 

 • Types of Regression algorithms 

 • Linear Regression algorithms 

 • Logistic Regression algorithms

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