Data Science

Learn Data Science Training in Bangalore at RelQSoft by real time expert. We offer placement assistance for all Data Science Students.

Course Curriculum

Data Science 

  1. Introduction to Machine Learning

 

The concepts you will learn:

 

What is Machine Learning, Supervised/unsupervised Learning, Gradient Descent, Learning Rate, Training & Testing, Performance, Algorithms, Machine Learning Structure

 

  1. Data Preparation

 

The concepts you will learn:

 

Missing Value treatment, outlier identification, Data cleansing

 

  1. Linear Models

 

The concepts you will learn:

Introduction to regression, Why do regression analysis, Types of regression analysis,OLS    regression, Assumptions, Dependent and independent variable(s),Steps to implement a regression model, Simple linear regression, Understanding terminology of each of the output of linear regression, Correlation, Strength of linear association, Least-squares or regression line, linear regression model, Correlation coefficient R, Multiple regression, Regression diagnostics, Model Selection

 

  1. Logistic Regression

 

The concepts you will learn:

Binary response regression model, Linear regression output of proposed model, Problems with linear probability model, Logistic function, Logistic regression & its interpretation, Odds ratio, Goodness of fit measures, confusion Matrix

 

  1. Nearest Neighbor Classification

 

The concepts you will learn:

Instance based classifiers, Nearest-Neighbor classifiers, Lazy vs. Eager learning, k-NN variations, How to determine the good value for k, When to consider nearest neighbors, nearest neighbor issues

 

 

  1. Naïve Bayes Classification

 

The concepts you will learn:

Naive Bayes learning, Conditional probability, Bayesian theorem: basics, The Bayes classifier, Model parameters, Naive Bayes training

 

  1. Decision Trees

 

The concepts you will learn:

Key requirements, Decision tree as a rule set, How to create a decision tree, Choosing attributes,ID3 heuristic, Entropy, Subtree Replacement, Tree induction, Splitting based on ordinal attributes, How to determine the best split, Measure of impurity: GINI, Splitting based on GINI, Attributes binary, Strengths and weakness of decision trees

 

  1. Support Vector Machine Classifiers

 

The concepts you will learn:

Support vector machines for classification, Linear discrimination, Nonlinear discrimination, SVM mathematically, Extensions, Application, Kernel functions

 

 

  1. Ensemble Approaches

 

The concepts you will learn:

Ensemble approaches, Bagging model, Boosting, The AdaBoost algorithm, Gradient boosting, Random forests, Disadvantages

 

 

  1. Artificial Neural Network

 

The concepts you will learn:

Background of brain and neuron, neural networks, Neurons diagram, Neuron models- step function, Perceptron, Network architectures, Single-layer feed-forward

 

  1. Cluster Analysis

 

The concepts you will learn:

Types of data in cluster analysis, A categorization of major clustering methods, Partitioning methods, Hierarchical methods, Density-based methods, Grid-based methods, Model-based clustering methods

 

 

 

 

  1. Principal Component Analysis

 

The concepts you will learn:

Curse of dimensionality, Dimension reduction, Why factor or component analysis?, Principal component analysis, PCs variance and least-squares, Eigenvectors of a correlation matrix, Factor analysis

 

  1. Forecasting Principles

 

The concepts you will learn:

Basic time series and it’s components, Moving averages (simple & exponential),R’Â’s inbuilt function ts(),Plotting of time series, Business forecasting using moving average methods, The ARIMA model, Application of ARIMA model in business

 

 

  1. Recommendation Systems

 

The concepts you will learn:

 

Association Rules, Eclat, Collaborative Filtering

In addition to it, real-world use-cases will be undertaken to explain each and every concept. Participants are free to discuss their own use-cases in the session and we can assist in solving it.