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Artificial Intelligence

Objectives

We comprehensive Master’s program in Artificial Intelligence to become a certified Artificial Intelligence Engineer. As a part of this AI Engineer course co-created with IBM, you will learn various aspects of AI like Machine Learning with Python, Deep Learning with TensorFlow, Artificial Neural Networks, Statistics, Data Science, SAS Advanced Analytics, Tableau Business Intelligence, Python, and R programming, Build Chatbots with Watson Assistant, and MS Excel through hands-on projects. Moreover, you will also get exclusive access to IBM Cloud Platforms

Target Audience

  • Software Engineers and Data Analysts
  • Business Intelligence Professionals
  • Those looking for a career in Data Science

Course Modules

Module 1: Introduction to Data Science with R

  • What is Data Science?
  • Significance of Data Science in today’s data-driven world, its applications of, lifecycle, and its components
  • Introduction to R programming and RStudio

Module 2: Data Exploration

  • Introduction to data exploration
  • Importing and exporting data to/from external sources
  • What are data exploratory analysis and data importing?
  • DataFrames, working with them, accessing individual elements, vectors, factors, operators, in-built functions, conditional and looping statements, user-defined functions, and data types

Module 3: Data Manipulation

  • Need for data manipulation
  • Introduction to the DPLYR package
  • Selecting one or more columns with select(), filtering records on the basis of a condition with filter(), adding new columns with mutate(), sampling, and counting
  • Combining different functions with the pipe operator and implementing SQL-like operations with SQLDF

Module 4: Data Visualization

  • Introduction to visualization
  • Different types of graphs, the grammar of graphics, the ggplot2 package, categorical distribution with geom_bar(), numerical distribution with geom_hist(), building frequency polygons with geom_freqpoly(), and making a scatterplot with geom_pont()
  • Multivariate analysis with geom_boxplot
  • Univariate analysis with a barplot, a histogram and a density plot, and multivariate distribution
  • Creating bar plots for categorical variables using geom_bar(), and adding themes with the theme() layer
  • Visualization with Plotly, frequency plots with geom_freqpoly(), multivariate distribution with scatter plots and smooth lines, continuous distribution vs categorical distribution with box-plots, and subgrouping plots
  • Working with co-ordinates and themes to make graphs more presentable, understanding plotly and various plots, and visualization with ggvis
  • Geographic visualization with ggmap() and building web applications with shinyR

Module 5: Introduction to Statistics

  • Why do we need statistics?
  • Categories of statistics, statistical terminology, types of data, measures of central tendency, and measures of spread
  • Correlation and covariance, standardization and normalization, probability and the types, hypothesis testing, chi-square testing, ANOVA, normal distribution, and binary distribution

Module 6: Machine Learning

  • Introduction to Machine Learning
  • Introduction to linear regression, predictive modeling, simple linear regression vs multiple linear regression, concepts, formulas, assumptions, and residuals in Linear Regression, and building a simple linear model
  • Predicting results and finding the p-value and an introduction to logistic regression
  • Comparing linear regression with logistics regression and bivariate logistic regression with multivariate logistic regression
  • Confusion matrix the accuracy of a model, understanding the fit of the model, threshold evaluation with ROCR, and using qqnorm() and qqline()
  • Understanding the summary results with the null hypothesis, F-statistic, and
  • Building linear models with multiple independent variables

Module 7: Logistic Regression

  • Introduction to logistic regression
  • Logistic regression concepts, linear vs logistic regression, and math behind logistic regression
  • Detailed formulas, logit function and odds, bivariate logistic regression, and Poisson regression
  • Building a simple binomial model and predicting the result, making a confusion matrix for evaluating the accuracy, true positive rate, false-positive rate, and threshold evaluation with ROCR
  • Finding out the right threshold by building the ROC plot, cross-validation, multivariate logistic regression, and building logistic models with multiple independent variables
  • Real-life applications of logistic regression

Module 8: Decision Trees and Random Forest

  • What is classification? Different classification techniques
  • Introduction to decision trees
  • Algorithm for decision tree induction and building a decision tree in R
  • Confusion matrix and regression trees vs classification trees
  • Introduction to bagging
  • Random forest and implementing it in R
  • What is Naive Bayes? Computing probabilities
  • Understanding the concepts of Impurity function, Entropy, Gini index, and Information gain for the right split of node
  • Overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning, pruning a decision tree and predicting values, finding out the right number of trees, and evaluating performance metrics

Module 9: Unsupervised Learning

  • What is Clustering? Its use cases
  • what is k-means clustering? What is canopy clustering?
  • What is hierarchical clustering?
  • Introduction to unsupervised learning
  • Feature extraction, clustering algorithms, and the k-means clustering algorithm
  • Theoretical aspects of k-means, k-means process flow, k-means in R, implementing k-means, and finding out the right number of clusters using a scree plot
  • Dendrograms, understanding hierarchical clustering and implementing it in R
  • Explanation of Principal Component Analysis (PCA) in detail and implementing PCA in R

Module 10: Association Rule Mining and Recommendation Engines

  • Introduction to association rule mining and MBA
  • Measures of association rule mining: Support, confidence, lift, and apriori algorithm, and implementing them in R
  • Introduction to recommendation engines
  • User-based collaborative filtering and item-based collaborative filtering, and implementing a recommendation engine in R
  • Recommendation engine use cases

Module 11: Introduction to Artificial Intelligence

  • Introducing Artificial Intelligence and Deep Learning
  • What is an artificial neural network? TensorFlow: The computational framework for building AI models
  • Fundamentals of building ANN using TensorFlow and working with TensorFlow in R

Module 12: Time Series Analysis

  • What is a time series? The techniques, applications, and components of time series
  • Moving average, smoothing techniques, and exponential smoothing
  • Univariate time series models and multivariate time series analysis
  • ARIMA model
  • Time series in R, sentiment analysis in R (Twitter sentiment analysis), and text analysis

Module 13: Support Vector Machine (SVM)

  • Introduction to Support Vector Machine (SVM)
  • Data classification using SVM
  • SVM algorithms using separable and inseparable cases
  • Linear SVM for identifying margin hyperplane

Module 14: Naïve Bayes

  • What is the Bayes theorem?
  • What is Naïve Bayes Classifier?
  • Classification Workflow
  • How Naive Bayes classifier works and classifier building in Scikit-Learn
  • Building a probabilistic classification model using Naïve Bayes and the zero probability problem

Module 15: Text Mining

  • Introduction to the concepts of text mining
  • Text mining use cases and understanding and manipulating the text with ‘tm’ and ‘stringR’
  • Text mining algorithms and the quantification of the text
  • TF-IDF and after TF-IDF

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    Program Schedules : Session Starts From

    9th July 2021

    Online Live

    23rd July 2021

    Classroom

    Certification (Artificial Intelligence)

    We comprehensive Master’s program in Artificial Intelligence to become a certified Artificial Intelligence Engineer. As a part of this AI Engineer course co-created with IBM, you will learn various aspects of AI like Machine Learning with Python, Deep Learning with TensorFlow, Artificial Neural Networks, Statistics, Data Science, SAS Advanced Analytics, Tableau Business Intelligence, Python, and R programming, Build Chatbots with Watson Assistant, and MS Excel through hands-on projects. Moreover, you will also get exclusive access to IBM Cloud Platforms

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