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Contents03About the Course04Key Features ofArtificial Intelligence Engineer Master’s Program05About IBM and Simplilearn Co-Developed Programs06Learning Path Visualization07Program Outcomes09Who Should Enroll10Courses10Step 1: Introduction to Artificial Intelligence11Step 2: Statistics Essentials13Step 3: Python for Data Science14Step 4: Data Science with Python16Step 5: Machine Learning18Step 6: Deep Learning Fundamentals19Step 7: Deep Learning with TensorFlow20Step 8: Natural Language Processing21Step 9: Capstone Project22Electives23Certificates and Badges24Advisory Board Members2

About the CourseThis Artificial Intelligence Master’sProgram provides training for themost sought after skills requiredfor a successful career in ArtificialIntelligence. As you undertakeyour Artificial Intelligence Engineertraining, you will master the conceptsof Deep Learning, Machine Learning,Natural Language Processing plusthe programming languages neededto excel in an Artificial IntelligenceCareer with exclusive training andcertification from IBM. In this course,co-developed with IBM, you willlearn to design intelligent models,advanced artificial neural networks,leveraging predictive analytics tosolve real-time decision-makingproblems.3

KeyFeaturesIndustry-recognizedcertificates fromIBM and SimplilearnPortfolio worthycapstonedemonstratingmasteredconcepts15 Real-life projectsproviding hands-onindustry training20 In-demandskills 1,200 worth ofIBM cloud credits4

About IBM and SimplilearnCo-Developed ProgramsA joint partnership with Simplilearnand IBM introduces students toan integrated blended learning,making them an expert in ArtificialIntelligence and Data Science. Theprogram co-developed with IBMwill make students industry readyfor Artificial Intelligence and DataScience job roles. IBM is a leadingcognitive solution and cloud platformcompany, headquartered in Armonk,New York, offering a plethora oftechnology and consulting services.Each year, IBM invests 6 billion inresearch and development and hasachieved five Nobel Prizes, nine USNational Medals of Technology, fiveUS National Medals of Science, sixTuring Awards, and 10 Inductions inUS Inventors Hall of Fame.About SimplilearnSimplilearn is a leader in digital skillstraining, focused on the emergingtechnologies that are transforming ourworld. Our blended learning approachdrives learner engagement and backedby the industry’s highest completionrates. Partnering with professionals andcompanies, we identify their unique needsand provide outcome-centric solutions tohelp them achieve their professional goals.5

Learning Path - Artificial IntelligenceIntroduction toArtificial IntelligenceStatisticsEssentialsPython forData ScienceData Sciencewith PythonMachineLearningDeep LearningFundamentalsElectivesDeep Learningwith IBM Watson for ChatbotsAccelerated Deep Learning with GPUMachine Learning with R6

Artificial Intelligence EngineerMaster’s Program OutcomesLearn about the major applicationsMaster the skills and tools usedof Artificial Intelligence acrossby the most innovative Artificialvarious use cases across variousIntelligence teams across the globefields like customer service, financialas you delve into specializations, andservices, healthcare, etc.gain experience solving real-worldchallenges.Implement classical ArtificialIntelligence techniques such asDesign and build your own intelligentsearch algorithms, neural networks,agents and apply them to createand tracking.practical Artificial Intelligenceprojects including games, MachineLearning models, logic constraintsatisfaction problems, knowledgebased systems, probabilistic models,Gain the ability to apply ArtificialIntelligence techniques for problem-agent decision-making functions andmore.solving and explain the limitationsof current Artificial Intelligencetechniques.7

Understand the concepts ofMaster and comprehend advancedTensorFlow, its main functions,topics such as convolutional neuraloperations, and the executionnetworks, recurrent neural networks, deep networks, and highlevel interfaces.Understand and master the conceptsand principles of Machine Learning,Understand the fundamentals ofincluding its mathematical andNatural Language Processing usingheuristic aspects.the most popular library; Python’sNatural Language Toolkit (NLTK).8

Who Should Enroll in this Program?With the demand for ArtificialIntelligence in a broad range ofindustries such as banking andfinance, manufacturing, transportand logistics, healthcare, homemaintenance, and customerservice, the Artificial Intelligencecourse is well suited for a varietyof profiles like:Developers aspiring to be an‘Artificial Intelligence Engineer’or Machine Learning engineersAnalytics managers who areleading a team of analystsInformation architects whowant to gain expertisein Artificial IntelligencealgorithmsGraduates looking to build acareer in Artificial Intelligenceand Machine Learning9

Introduction to Artificial IntelligenceSimplilearn’s Introduction to Artificial Intelligence course is designed tohelp learners decode the mystery of Artificial Intelligence and understandits business applications. The course provides an overview of ArtificialIntelligence concepts and workflows, Machine Learning, Deep Learning,and performance metrics. You’ll learn the difference between supervised,unsupervised, and reinforcement learning—be exposed to use cases, andsee how clustering and classification algorithms help identify ArtificialIntelligence business applications.Key Learning ObjectivesMeaning, purpose, scope, stages, applications, and effects of ArtificialIntelligenceFundamental concepts of Machine Learning and Deep LearningDifference between supervised, semi-supervised and unsupervisedlearningMachine Learning workflow and how to implement the stepseffectivelyThe role of performance metrics and how to identify their essentialmethodsCourse curriculumLesson 1 - Decoding Artificial IntelligenceLesson 2 - Fundamentals of Machine Learning and Deep LearningLesson 3 - Machine Learning WorkflowLesson 4 - Performance Metrics10 www.simplilearn.comSTEP123456789

STEPStatistics EssentialStatistics is the science of assigning a probability to an event basedon experiments. It is the application of quantitative principles tothe collection, analysis, and presentation of numerical data. Ace thefundamentals of Data Science, statistics, and Machine Learning with thiscourse. It will enable you to define statistics and essential terms related toit, explain measures of central tendency and dispersion, and comprehendskewness, correlation, regression, distribution. You will be able to makedata-driven predictions through statistical inference.Key Learning ObjectivesUnderstand the fundamentals of statisticsWork with different types of dataHow to plot different types of dataCalculate the measures of central tendency, asymmetry, and variabilityCalculate correlation and covarianceDistinguish and work with different types of distributionEstimate confidence intervalsPerform hypothesis testingMake data-driven decisionsUnderstand the mechanics of regression analysisCarry out regression analysisUse and understand dummy variablesUnderstand the concepts needed for data science even with Pythonand R!11 www.simplilearn.com123456789

Course curriculumLesson 1 - IntroductionLesson 2 - Sample or population data?Lesson 3 - The fundamentals of descriptive statisticsLesson 4 - Measures of central tendency, asymmetry, and variabilityLesson 5 - Practical example: descriptive statisticsLesson 6 - DistributionsLesson 7 - Estimators and estimatesLesson 8 - Confidence intervals: advanced topicsLesson 9 - Practical example: inferential statisticsLesson 10 - Hypothesis testing: IntroductionLesson 11 - Hypothesis testing: Let’s start testing!Lesson 12 - Practical example: hypothesis testingLesson 13 - The fundamentals of regression analysisLesson 14 - Subtleties of regression analysisLesson 15 - Assumptions for linear regression analysisLesson 16 - Dealing with categorical dataLesson 17 - Practical example: regression analysis12

STEPPython for Data ScienceKickstart your learning of Python for Data Science with this introductorycourse and familiarize yourself with programming. Carefully crafted byIBM, upon completion of this course you will be able to write your Pythonscripts, perform fundamental hands-on data analysis using the Jupyterbased lab environment, and create your own Data Science projects usingIBM Watson.Key Learning ObjectivesWrite your first Python program by implementing concepts ofvariables, strings, functions, loops, conditionsUnderstand the nuances of lists, sets, dictionaries, conditions andbranching, objects and classesWork with data in Python such as reading and writing files, loading,working, and saving data with PandasCourse curriculumLesson 1 - Python BasicsLesson 2 - Python Data StructuresLesson 3 - Python Programming FundamentalsLesson 4 - Working with Data in PythonLesson 5 - Working with NumPy arrays13 www.simplilearn.com123456789

STEPData Science with PythonThis Data Science with Python course will establish your mastery ofData Science and analytics techniques using Python. With this Pythonfor Data Science Course, you’ll learn the essential concepts of Pythonprogramming and gain in-depth knowledge in data analytics, MachineLearning, data visualization, web scraping, and natural languageprocessing. Python is a required skill for many Data Science positions,so jump start your career with this interactive, hands-on course.Key Learning ObjectivesGain an in-depth understanding of Data Science processes, datawrangling, data exploration, data visualization, hypothesis building,and testing. You will also learn the basics of statisticsInstall the required Python environment and other auxiliary tools andlibrariesUnderstand the essential concepts of Python programming such asdata types, tuples, lists, dicts, basic operators and functionsPerform high-level mathematical computing using the NumPy packageand its vast library of mathematical functionsPerform scientific and technical computing using the SciPy packageand its sub-packages such as Integrate, Optimize, Statistics, IO, andWeavePerform data analysis and manipulation using data structures andtools provided in the Pandas packageGain expertise in Machine Learning using the Scikit-Learn packageGain an in-depth understanding of supervised learning andunsupervised learning models such as linear regression, logisticregression, clustering, dimensionality reduction, K-NN and pipeline14 www.simplilearn.com123456789

Use the Scikit-Learn package for natural language processingUse the matplotlib library of Python for data visualizationExtract useful data from websites by performing web scraping usingPythonIntegrate Python with Hadoop, Spark, and MapReduceCourse curriculumLesson 1: Data Science OverviewLesson 2: Data Analytics OverviewLesson 3: Statistical Analysis and Business ApplicationsLesson 4: Python Environment Setup and EssentialsLesson 5: Mathematical Computing with Python (NumPy)Lesson 6 - Scientific computing with Python (Scipy)Lesson 7 - Data Manipulation with PandasLesson 8 - Machine Learning with Scikit–LearnLesson 9 - Natural Language Processing with Scikit LearnLesson 10 - Data Visualization in Python using matplotlibLesson 11 - Web Scraping with BeautifulSoupLesson 12 - Python integration with Hadoop MapReduce and Spark15

STEPMachine LearningSimplilearn’s Machine Learning course will make you an expert in MachineLearning, a form of Artificial Intelligence that automates data analysis toenable computers to learn and adapt through experience to do specifictasks without explicit programming. You will master Machine Learningconcepts and techniques, including supervised and unsupervised learning,mathematical and heuristic aspects, and hands-on modeling to developalgorithms and prepare you for your role with advanced Machine Learningknowledge.Key Learning ObjectivesMaster the concepts of supervised and unsupervised learning,recommendation engine, and time series modelingGain practical mastery over principles, algorithms, and applications ofMachine Learning through a hands-on approach that includes workingon four major end-to-end projects and 25 hands-on exercisesAcquire thorough knowledge of the statistical and heuristic aspects ofMachine LearningImplement models such as support vector machines, kernel SVM,naive Bayes, decision tree classifier, random forest classifier, logisticregression, K-means clustering and more in PythonValidate Machine Learning models and decode various accuracymetrics. Improve the final models using another set of optimizationalgorithms, which include Boosting & Bagging techniquesComprehend the theoretical concepts and how they relate to thepractical aspects of Machine Learning16 www.simplilearn.com123456789

Course curriculumLesson 1: Introduction to Artificial Intelligence and Machine LearningLesson 2: Data PreprocessingLesson 3: Supervised LearningLesson 4: Feature EngineeringLesson 5: Supervised Learning-ClassificationLesson 6: Unsupervised learningLesson 7: Time Series ModellingLesson 8: Ensemble LearningLesson 9: Recommender SystemsLesson 10: Text Mining17

STEPDeep Learning FundamentalsThis course by IBM is designed to help you learn the fundamentals ofDeep Learning. It will make you familiar with the concepts of DeepLearning, Convolutional neural networks, and the effectiveness of DeepLearning. Be part of a rapidly growing field in Data Science; there’s nobetter time than now to get started with neural networks.Key Learning ObjectivesGain understanding of Deep LearningUnderstand Deep Learning models such as convolutional networks,recurrent nets, Autoencoders, Recursive Neural Tensor Nets, and DeepLearning Use CasesComprehend Deep Learning platforms and software librariesCourse curriculumLesson 1 - Introduction to Deep LearningLesson 2 - Deep Learning ModelsLesson 3 - Additional Deep Learning ModelsLesson 4 - Deep Learning Platforms and Software Libraries18 www.simplilearn.com123456789

Deep Learning with TensorFlowThis Deep Learning with TensorFlow course by IBM will refine yourMachine Learning knowledge and make you an expert in Deep Learningusing TensorFlow. Master the concepts of Deep Learning and TensorFlowto build artificial neural networks and traverse layers of data abstraction.This course will help you learn to unlock the power of data and prepareyou for new horizons in Artificial Intelligence.Key Learning ObjectivesUnderstand the difference between linear and non-linear regressionComprehend Convolutional Neural Networks and their applicationsGain familiarity on Recurrent Neural Networks (RNN) andAutoencodersLearn how to filter with Restricted Boltzmann MachineCourse curriculumLesson 1 - Introduction to TensorFlowLesson 2 – Convolutional Neural Networks (CNN)Lesson 3 – Recurrent Neural Networks (RNN)Lesson 4 - Unsupervised LearningLesson 5 - Autoencoders19 www.simplilearn.comSTEP123456789

STEPNatural Language ProcessingThis Natural Language Processing course will give you a detailed lookat the science behind applying Machine Learning algorithms to processlarge amounts of natural language data. You will learn the concepts ofNatural Language understanding, Feature Engineering, Natural LanguageGeneration, Speech Recognition techniques.Key Learning ObjectivesLearn how to perform text processing and find a patternFind the most relevant document by applying TF-IDFWrite a script for applying parts-of-speech and extraction on focuswordsCreate your own NLP moduleClassify the cluster for articlesCreate a basic speech modelConvert speech to textCourse curriculumLesson 1 - IIntroduction to Natural Language ProcessingLesson 2 - Feature Engineering on Text DataLesson 3 - Natural Language Understanding TechniquesLesson 4 - Natural Language GenerationLesson 5 - Natural Language Processing LibrariesLesson 6 - Natural Language Processing with Machine Learning andDeep LearningLesson 7 - Speech Recognition Technique20 www.simplilearn.com123456789

Artificial Intelligence Capstone ProjectSimplilearn’s Artificial Intelligence Capstone project will allow you toimplement the skills you learned in the masters of Artificial Intelligence.With dedicated mentoring sessions, you’ll know how to solve a realindustry-aligned problem. You’ll learn various Artificial Intelligence-basedsupervised and unsupervised techniques like Regression, SVM, Tree-basedalgorithms, NLP, etc. The project is the final step in the learning path andwill help you to showcase your expertise to employers.Key Learning ObjectivesSimplilearn’s online Artificial Intelligence Capstone course will bring youthrough the Artificial Intelligence decision cycle, including ExploratoryData Analysis, building and fine-tuning a model with cutting edgeArtificial Intelligence-based algorithms and representing results. Theproject milestones are as follows:Exploratory Data Analysis - In this step, you will apply various dataprocessing techniques to determine the features and correlationbetween them, transformations required to make the data sense, newfeatures, construction, etc.Model Building and fitting - This will be performed using MachineLearning algorithms like regression, multinomial Naïve Bayes, SVM,tree-based algorithms, etc.Unsupervised learning - Clustering to group similar kind oftransactions/reviews using NLP and related techniques to devisemeaningful conclusions.Representing results - As the last step, you will be required to exportyour results into a dashboard with useful insights.21 www.simplilearn.comSTEP123456789

Elective CourseIBM Watson for ChatbotsThis course provides a practical introduction on how tobuild a chatbot with Watson Assistant without writing anycode and then deploy your chatbot to a real website inless than five minutes. It will teach you to plan, build, test,analyze, and use your first chatbot.Accelerated Deep Learning with GPUIn this Accelerated Deep Learning course with GPU by IBM,you will learn how to use accelerated hardware to overcomethe scalability problem in Deep Learning. The coursewill begin with a quick review of Deep Learning, how toaccelerate a Deep Learning model. It will then progress toDeep Learning in the Cloud and distributed Deep Learning.Machine Learning with RIn this course, you will learn how to write R code, learnabout R’s data structures, and create your own functions.With the knowledge gained, you will be ready to undertakeyour first very own data analysis. You’ll further learn aboutSupervised versus Unsupervised Learning, look into howStatistical Modeling relates to Machine Learning, and make acomparison of each using R.22

CertificatesC E R T I F I C AT EO F AC H I E V E M E N TARTIFICIALINTELLIGENCE ENGINEERT H I S I S T O C E R T I F Y T H ATJOHN DOEHas successfully graduated from the Artificial intelligence EngineerMasters Program summa cum laude having completed all mandatedcourse requirements and industry projects with distinctionDate: / /2019Krishna Kumar, CEOUpon completion of this Master’s Program, you will receivethe certificates from IBM and Simplilearn in the ArtificialIntelligence courses in the learning path. These certificateswill testify to your skills as an expert in Artificial Intelligence.Upon program completion, you will also receive an industryrecognized Master’s Certificate from Simplilearn.23

Advisory board memberMike TamirNo. 1 AI & Machine Learning Influencer,Head of Data Science - Uber ATGNamed by Onalytica as the Number 1 influencerin Artificial Intelligence and Machine Learning,Mike serves as the Head of Data Science forUber ATG’s self-driving engineering team andas a member of The University of California inBerkeley data science faculty.24

USASimplilearn Americas, Inc.201 Spear Street, Suite 1100, San Francisco, CA 94105United StatesPhone No: 1-844-532-7688INDIASimplilearn Solutions Pvt Ltd.# 53/1 C, Manoj Arcade, 24th Main, Harlkunte2nd Sector, HSR LayoutBangalore - 560102Call us at: 25

This Artificial Intelligence Master's Program provides training for the most sought after skills required for a successful career in Artificial Intelligence. As you undertake your Artificial Intelligence Engineer training, you will master the concepts of Deep Learning, Machine Learning, Natural Language Processing plus

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