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Introduction To DataScience

• Getting started with ANACONDA

• Downloading and installing ANACONDA managing environment and instructions on ANACONDA, navigating the SPYDER and JUPYTER notebook interface.

• Downloading datasets, data exploration and analysis, presenting your data.

• Python for Data Science.

  • Data types

  • Control Structures

  • Functions

  • User Defined Functions

  • Python Packages

  • Numpy,Pandas,Matplotlib,TensorFlow

Regression Analysis

• Categories of machine learning

• Linear Regression Models

Classification

• Logistic Regression Models

• Neural Network Models

Clustering

• Clustering Concepts

• K-Means Clustering

Support Vector Machines

Random Forests (Ensemble Machine Learning)

Decision Trees

Unsupervised Learning. (Dimensionality Reduction Techniques)

Principle Component Analysis (PCA)

K-Nearest Neighbors