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IISPL Noida Data Analytics Machine Earning Module
- 1. Data Analytics & Machine Learning PROF. (Dr.) S. PATHAK , PH.D, M.TECH, Senior Data Scientist & Er. J.K. JHA ( Corporate Trainer, BIG Data & Machine Learning ) Reach: info@iispl.co.in www.iispl.co.in IISPL ACADEMY
- 2. Introduction to Analytics & Data Analysis tools What is data analytics? Importance of analytics. Introduction to various analysis techniques Applications of data analysis in various industries Introduction to SAS/R/Python/SPSS Basics of programing in SAS/R/Python/SPSS Data handling in SAS/R/Python/SPSS BI reporting in SAS/R/Python/SPSS Performing statistical analysis on SAS/R/Python/SPSS Analyzing the data with simple descriptive statistics Variance and standard deviation IISPL ACADEMY
- 3. Data Validation & Cleaning Introduction to validating and cleaning data Examining data errors when reading raw data files Validating data with the CONTENTS, PRINT, FREQ, MEANS and UNIVARIATE procedures. Cleaning invalid data: Missing value identification and treatment. Outlier identification and treatment Project Work IISPL ACADEMY
- 4. Introduction to machine learning: What is machine learning? Learning system model Training and testing Performance Algorithms Machine learning structure What are we seeking? Learning techniques IISPL ACADEMY
- 5. Nearest neighbor classification: 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 Condensing Nearest neighbor issues Project Work IISPL ACADEMY
- 6. IISPL ACADEMY Naive Bayes classification Naive Bayes learning Conditional probability Bayesian theorem: basics The Bayes classifier Model parameters Naive Bayes training Types of errors Sensitivity and specificity ROC curve Holdout estimation Cross-validation
- 7. Decision Trees - Part I Key requirements Decision tree as a rule set How to create a decision tree Choosing attributes ID3 heuristic Entropy Pruning trees - Pre and post Subtree Replacement Raising Decision Trees - Part II Tree induction Splitting based on ordinal attributes How to determine the best split Measure of impurity: GINI Splitting based on GINI Attributes binary Categorical -GINI Strengths and weakness of decision trees IISPL ACADEMY
- 8. Ensemble Approaches Ensemble approaches Bagging model Boosting The Ada Boost algorithm Gradient boosting Random forests RIF RIC Advantages Disadvantages IISPL ACADEMY
- 9. Artificial Neural Network Background of brain and neuron Neural networks Neurons diagram Neuron models- step function Ramp func etc Perceptrons Network architectures Single-layer feed-forward Artificial Neural Network continued Multi layer feed-forward NN (FFNN) Back propagation NN design issues Recurrent network architecture Supervised learning NN Self organizing map Network structure SOM algorithm IISPL ACADEMY
- 10. Project I Mentee can select project from predefined set of AcadGild projects or they can come up with their own ideas for their projects Mentee can select project from predefined set of AcadGild projects or they can come up with their own ideas for their projects IISPL ACADEMY
- 11. Support Vector Machine Classifiers Support vector machines for classification Linear discrimination Nonlinear discrimination SVM mathematically Extensions Application in drug design Data classification Kernel functions Project IISPL ACADEMY
- 12. Linear Models in R Introduction to regression Why do regression analysis Types of regression analysis OLS regression Dependent and independent variable(s) Steps to implement a regression model Simple linear regression Understanding terminology of each of the output of linear regression Project IISPL ACADEMY
- 13. Correlation and Regression Correlation Strength of linear association Least-squares or regression line Linear regression model Correlation coefficient R Multiple regression Regression diagnostics Assumptions in Regression Analysis The assumptions Assumption 1 and explanation- residuals and non normality Assumption 2 and explanation- heteroscedasticity Assumption 3 and explanation- additivity Assumption 4 and explanation- linearity ; Independence assumption; Residual plots Project IISPL ACADEMY
- 14. Model Selection in R Fitting the model Diagnostic plots Comparing models Cross validation Variable selection Relative importance AIC Dummy variable Box cox transformations Creating the model Residuals vs fitted Residuals vs regression Diagnostic plots IISPL ACADEMY
- 15. Logistic Regression 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 What is cluster analysis? Project IISPL ACADEMY
- 16. Introduction to Cluster Analysis 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 Supervised classification Project IISPL ACADEMY
- 17. Principal Component Analysis (PCA) 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 PCA process steps Project IISPL ACADEMY
- 18. Forecasting Principles 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 Project IISPL ACADEMY
- 19. IISPL ACADEMY Final Project Project Work With Sophisticated Statistical and Mathematical Tools & Techniques
- 20. Thanking You !! With You Until Success & Beyond… IISPL ACADEMY