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 **

**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

**Data Preparation**

The concepts you will learn:

Missing Value treatment, outlier identification, Data cleansing

**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

**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

**Nearest Neighbor Classification**

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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

**Naïve Bayes Classification**

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The concepts you will learn:

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

**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

**Support Vector Machine Classifiers**

The concepts you will learn:

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

**Ensemble Approaches**

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The concepts you will learn:

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

**Artificial Neural Network**

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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

**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

**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

**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

**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.