Data Science course content
✓Online Instructor-led (by working professional) Training
✓Duration:30 hrs
♦ Overview of Analytics
Overview of Analytics
♦ Linear regression
Introduction,Mathematical Formulation,Gradient decent algorithm,Measure of Erec,Polynomial data regression,Model selection,Subset selection
♦ Data Preparation
How to prepare data,Outlier treatment,Many values treatment,Categorical variables,Variable transformation,Sampling
♦ Logistic Regression
Introduction,Decision Boundary,Non Linear,Cost ,Gradient de algorithm,Evaluating classification,Lift and cumulative gain client,Rose cap and over sampling,Asymmetric cats,Multi class classification
♦ Modelling Diagnostics
Modelling Diagnostics
♦ Principal component Analysis
Introduction,Use cases and application,Choosing the number of component
♦ Clustering
What is clustering,Types of clustering,K-Mean clustering,Choosing the number of clusters,Data preparation of clustering Analysis3,Hierarchical clustering-Common Clustering Algorithms,K-means,Fuzzy Mean Shift etc,Representing data,Feature Selection,Vectorization,Representing Vectors
♦ Decision Trees
Introduction,Hunt's algorithm in decision tree construction,Design issues & pattern
♦ Market Basket
Introduction,Generating rule,Performance measurement,Designing Baskets based on ,Products,Customer preference,Areas,Brands
♦ Neural networks
Introduction,Training neural networks,Prediction using neural networks,Building recommendation using neural networks ,classification using neural networks
♦ Introduction to Machine Learning & Mahout
Utilities, Matrix, Vectors and Collections in Mahout,Approach to deploy Machine Learning,Preparing Data for Machine Learning
♦ Basic concepts of networking
User based recommendation,Item based recommendation,Implementing a recommender using map reduce
♦ Classification
Evaluating a classifier,Developing a classifier
♦ Pattern Mining
Frequent Pattern Mining Algorithms,End to End POC for creation Recommendation Enginer
Exercise and assignment