MACHINE LEARNING INTERNSHIP PROGRAM

Machine learning:
      • Introduction to Machine learning.
      • A brief explanation of Machine learning algorithms.
      • Installation of machine learning algorithms
Scientific computation core libraries:
      • Introduction of file handling and modules.
      • Programming styles for those handling.
      • Introduction of scientific computation core libraries.
      • Programming styles for multidimensional array and tools.
Statistics:
      • Introduction of statistics.
      • Basic terminologies of statistics.
      • Sampling techniques.
      • Programming styles for statistics.
Data manipulation and Analysis:
      • Introduction of pandas.
      • Programming styles for pandas.
      • Steps for data manipulation and Analysis.
      • Extraction of the data and converting to local formats.
Data Visualization:
      • Introduction of Data Visualization.
      • Introduction of matplotlib
      • Programming styles for matplotlib.
      • Introduction of seaborn.
      • Programming styles for seaborn.
      • Introduction Bokeh
      • Programming styles of Bokeh
Supervised Learning
      • Introduction of Machine learning algorithms.
      • Supervised Learning concept.
      • Regression and Classification Algorithm.
      • Programming styles for Data preprocessing and feature extraction.
      • Application of Machine learning.
      • Training and testing Algorithms.
Un-Supervised Learning:
      • Introduction of Un-Supervised Learning concept.
      • Handling Un-labeled Dataset.
      • Programming styles for Data preprocessing and feature extraction of unsupervised algorithm.
      • Application of Machine learning.
      • Clustering the Unlabeled data
      • Data preprocessing and feature extraction of the raw dataset.
      • Training and Testing of the model.
      • Extraction of the model.
      • Predicting the Result.