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Post Graduate Program in Analytics and Artificial Intelligence

The PG Program in Analytics & Artificial Intelligence is an industry-oriented program focusing on practical applications of Machine learning and Analytics to make you job-ready. <br><br>For more details, feel free to visit-https://imarticus.org/post-graduate-program-in-analytics-and-artificial-intelligence

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Post Graduate Program in Analytics and Artificial Intelligence

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  1. What are your career goals this year? Master machine learning and analytics skills Learn from the best & get mentored by an industry expert Network with leaders & other like-minded professionals Land your dream job in the dynamic analytics industry POST GRADUATE PROGRAM IN ANALYTICS & ARTIFICIAL INTELLIGENCE Technology Partner: www.imarticus.org

  2. Program Highlights The PG Program in Analytics & Artificial Intelligence is an industry-oriented program focusing on practical applications of Machine learning and Analytics to make you job-ready. It aims to help you gain practical knowledge and accelerate your entry into the roles of Data Scientist, Analyst, or Machine Learning Engineer. This project-based, multi skill course will get you comfortable dealing with different types of structured and unstructured data to solve critical business problems using machine learning and deep learning. You will also learn about the popular key elements of AI - Natural Language Processing & Computer Vision. EXPERIENTIAL LEARNING Become Python Data Science expert through multiple lab sessions, bootcamps and hackathons CURRICULUM INDUSTRY-ENDORSED CURRICULUM Master the popular tools and techniques used by most of the Data Scientists and Machine Learning Engineers INDUSTRY MENTORSHIP Guest lectures & mentorship sessions teach you cutting-edge techniques to solve complex business problems INDUSTRY CONNECT IMARTICUS IMMERSION Connect with industry experts & develop a professional network at Imarticus' alumni events CAREER SERVICES Enhance your employability through hackathons, mock interviews and interview preparation workshops EMPLOYMENT ASSISTANCE PLACEMENT ASSURANCE Guaranteed interview opportunities with our placement partners SMART CLASSROOM Learn in technologically-augmented classrooms, enhanced with live lecture recording TECH-ENABLED LEARNING LMS Get access to our state-of-the-art LMS portal to track your learning journey and revisit challenging topics 02

  3. EXPERIENTIAL LEARNING IN-CLASS PROJECTS This program is designed in a unique way and incorporates real-world projects that cover essential analytics and AI tools and techniques. This project-based learning approach will help you internalize key concepts and learn how to practically apply various Analytics and AI concepts efficiently. Predicting Brand of a Car by its Specifications, using K-Means Clustering 1 10 Real Estate Price Prediction using Linear Regression 2 11 Bankruptcy Prediction using Logistic Regression Predicting Credit Card Default for a Bank, using SVM Building image classification model to identify hand-written digits in an image, using CNN on TensorFlow Identifying Good and Bad Customers for Granting Credit, using Decision Trees 3 12 Calculating the estimated probability of credit default to manage the risk of a bank, using ANN on Keras 4 13 Forecasting the Sale of Furniture of a Superstore, using Time Series Predicting Term Deposit Subscriptions for a Bank, using Decision Tree and Random forest 5 14 Classifying consumer complaint of a number of products, using RNN 6 15 Predicting occurrence of Breast Cancer, using KNN Classifying music in different genres, using LSTM 7 16 Predicting House Prices using Real Estate Data, using Linear Regression Object detection, using Computer Vision Classifying Type of Flower based on Botanical Data, using Logistic regression 8 17 Sentiment analysis, using NLP Predicting the Close Value of a Financial Firm's Stock, using Neural Network 9 *Some of these projects might change during the course. CAPSTONE PROJECT At the end of the course, you will work on one Capstone Project. Through 4 weeks of extensive project work, you will practice the applications of Machine Learning skills on real-world problems. The project will be evaluated by industry experts and can be showcased to prospective employers as a powerful aspect of your portfolio. 03

  4. TRAINING METHODOLOGY INSTRUCTION REINFORCEMENT ASSESSMENTS SELF-PACED INTERACTIVE LEARNING & LIVE LECTURES QUIZZES, ASSIGNMENTS & EXAMS PRACTICAL HANDS-ON LEARNING Learn the basics on a highly engaging Coding Ninjas platform followed by live classes with our expert faculty. Hands on experience with real-world projects to solve critical business problems. Participate in bootcamp & hackathon. Work on quizzes and assignments to test your knowledge, along with mock interviews & exams. Benefits: àIn-depth understanding of concepts àReal-time interaction & query àResolution àHands-on experience Benefits: àGauge your progress throughout the program àIdentify areas of improvement and learning gaps àBuild confidence for the program's placement phase Benefits: àDevelop competency to solve business problems through machine learning techniques àBe job ready from Day 1 Used for: Live instruction by expert faculty and hands-on practice. Used for: Learning real-world applications of key tools and techniques used in the industry. Used for: Ensuring consistent progress over the course of the program and preparing for placements INDUSTRY ENDORSED CURRICULUM The PGP in Analytics & AI features a cutting-edge industry-aligned curriculum, that offers the perfect blend of statistics, technical and business knowledge. The curriculum has been designed in consultation with multiple industry leaders to ensure that you learn exactly what employers need. PREP MODULE INTRODUCTION TO PYTHON Python Installation | Anaconda and Jupyter Notebook | Variable in Python | Data Types | Python Numbers | Limit of Integers | Arithmetic Operators | Taking Inputs CONDITIONS AND LOOPS Boolean Datatype | Conditional statement (IF ELSE) | Relational and logical operators | Using Else If | Nested Conditionals | While Loop | Primality Checking | Nested Loops INTRODUCTION TO PYTHON & STATISTICS STRINGS & LISTS String inbuilt functions | String slicing | Lists | Lists inbuilt functions | List Slicing | Multi-dimensional Lists FUNCTIONS Functions | Functions using strings and lists | Score of Variable | Default parameters in functions TUPLES, DICTIONARY AND SETS Tuples | Tuples Functions | Dictionary | Adding or removing Data in Dictionary | Sets | Functions in sets 04

  5. INDUSTRY ENDORSED CURRICULUM PREP MODULE OOPS Introduction | Create class & object | Instance Attributes | Class Attributes | Methods | Constructors | Class Methods & Statis Methods | Working with files NUMPY Introduction | Create NuPy arrays | Slicing & indexing | Mathematical Operations | Boolean Indexing | NumPy Broadcasting PANDAS Introduction | Accessing data in Pandas | Manipulating Data in Data Frame | Handling NAN | Handling string in Data PLOTTING GRAPH (DATA VISUALIZATION) Plotting Graphs | Customixzing Graph | Bubble Chart | Pie Chart | Histogram | Bar Graph INTRODUCTION TO PYTHON & STATISTICS STATISTICS - SAMPLING & POPULATION Introduction to Statistics | Data Types | Sample & Population | Simple Random Sampling | Satisfied Sampling | Cluster Sampling | Systematic Sampling | Categories of Statistics DESCRIPTIVE STATISTICS Measures in Descriptive Statistics | Measures in Central Tendency | Measures of Spread | Range & IQR | Variance and Standard Deviation | Measure of Position INFERENTIAL STATISTICS Inferential Statistics | Hypothesis Testing | Type 1 and Type 2 errors | Correlation MODULE 1 LINEAR REGRESSION Implementing Simple & Multiple Linear Regression with Python | Making sense of result parameters | Model validation | Handling outliers, categorical variables, autocorrelation, multicollinearity, heteroskedasticity | Prediction and Confidence Intervals DATA ANALYSIS WITH PYTHON LOGISTIC REGRESSION Implementing Logistic Regression with Python | Making sense of result parameters: Wald Test, Likelihood Ratio Test Statistic, Chi-square Test | Goodness of fit measures | Model validation: Cross Validation, Sensitivity, Specificity. ROC Curve, Confusion Matrix 05

  6. INDUSTRY ENDORSED CURRICULUM MODULE 1 DECISION TREES Implementing Decision Trees using Python | Homogeneity | Entropy | Information Gain | Gini Index | Standard Deviation Reduction | Vizualizing & Prunning a Tree | Implementing Random Forests using Python | Random Forest Algorithm | Important hyper-parameters of Random Forest for tuning the model | Variable Importance | Out of Bag Error DATA ANALYSIS WITH PYTHON TIME SERIES Handling time series data | Holt-Winters Model | ARIMA Model | ACF/PACF Functions DIMENSIONALITY REDUCTION - PCA Introduction to Dimensionality Reduction | Principal Component Analysis (PCA) | Factor Analysis (FA) | Difference between PCA and FA MODULE 2 INTRODUCTION TO MACHINE LEARNING Introduction to Machine Learning | Machine Learning Modelling Flow | How to treat Data in ML | Parametric & Non-parametric ML Algorithm | Types of Machine Learning | Performance Measures | Bias-Variance Trade-Off | Overfitting & Underfitting | Resampling Methods | Ensemble Methods SCIKIT-LEARN Introduction to SciKit Learn | Data Processing using Scikit-learn | Feature Extraction | Run Machine Learning Algorithms Both for Unsupervised and Supervised Data | Supervised Methods: Classification & Regression | Unsupervised Methods: Clustering, Gaussian Mixture Models | Performance Measurement Metrics | Decide What's the Best Model for Every Scenario OPTIMISATION TECHNIQUES Introduction to Optimization | Optimization Strategies | Batch Gradient Descent | Stochastic Gradient Descent | Nesterov Accelerated Gradient | Adagrad | Root Mean Squared Propagation | Adaptive Moment Estimation Procedure MACHINE LEARNING ALGORITHMS ML ALGORITHMS: SUPERVISED LEARNING Linear Regression with Stochastic Gradient Descent | Logistic Regression with Stochastic Gradient Descent | K-Nearest Neighbour | Eager Methods vs. Lazy Methods | Nearest Neighbor Classification | Building kD-Trees | Support Vector Machine | Perceptron Algorithm ML ALGORITHMS: UNSUPERVISED LEARNING What is Clustering? | K-means Algorithm | Types of Clustering | Evaluating K-means Clusters 06

  7. INDUSTRY ENDORSED CURRICULUM MODULE 2 ENSEMBLE ALGORITHMS Resampling Methods | Bootstrap Sampling | Bootstrap Aggregation | Bagging | Boosting | Stack generalization MACHINE LEARNING ALGORITHMS MODULE 3 NEURAL NETWORKS Understanding Neural Networks | The Biological Inspiration | Perceptron Learning & Binary Classification | Backpropagation Learning | Object Recognition KERAS Keras for Classification and Regression in Typical Data Science Problems | Setting up KERAS | Different Layers in KERAS | Creating a Neural Network | Training Models and Monitoring | Artificial Neural Networks TENSORFLOW Introducing Tensorflow | Tensorflow 1.0 Vs Tensorflow 2.0 | New Features | Neural Networks using Tensorflow | Debugging and Monitoring DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK(CNN) Intro to CNN | Convolutional operations and Image Features | ReLu | Pooling | Feature Maps & Filters | Image Augumentation | Fully Connected Layer | Training a CNN | Image Classification RECURRENT NEURAL NETWORK (RNN) Introduction to RNN | Sequential Modeling | RNN Network Structure | Different Types of RNNs | Bidirectional RNN | Limitations of RNN LONG SHORT TERM MEMORY (LSTM) Introduction to LSTM | Applications of LSTM | CalculusChain rule and LSTM as solution to Vanishing Gradient Problem | LSTM Architecture | Variants on LSTM ARTIFICIAL INTELLIGENCE MODULE 4 NATURAL LANGUAGE PROCESSING - I Introduction to NLP & NLTK | Sentence Segmentation & Tokenization | Extracting Tokens – Regular Expression | Stemming, Lemmatization | Part of Speech – POS | Named Entity Recognition (NER) | Stop Words Removal (English) | Corpora/ Corpus | TF-IDF | Word Vectorizer | Applications of NLP NATURAL LANGUAGE PROCESSING NATURAL LANGUAGE PROCESSING - II Introduction to Spacy | Tokenization | POS tagging | Dependency Parsing | NER | Computing Similarity | Visualizations using displaCy | Applications of Spacy | Naïve Bayes Classifier 07

  8. INDUSTRY ENDORSED CURRICULUM MODULE 5 COMPUTER VISION Introduction to Computer Vision | OpenCV to work with image files | image manipulation including smoothing, blurring | Translation, rotation,cropping thresholding, and morphological operations | Open and Stream video with OpenCV | Create Color Histograms with OpenCV | Corner, edge, and grid detection techniques with OpenCV | Face Recognition | Template matching COMPUTER VISION ADVANCED COMPUTER VISION YOLO model | Object detection | Transfer Learning using Keras MODULE 6 CAREER SERVICES Assessments | Resume building workshop | Interview preparation workshops | Mock interviews JOB PREPARATION 08

  9. MENTORSHIP A dedicated student engagement manager and an industry mentor will guide you on the most suitable career path based on your skills and interests and resolve your career-related queries throughout your learning journey with Imarticus. They will help you with: ACADEMIC ASSISTANCE à Provide unparalleled 1:1 support and guidance à Help execute in-class assignments and case studies à Discuss and identify learning gaps and offer solutions such as refresher sessions and one-on-one project feedback CAREER ASSISTANCE à Maintain close interaction with students during the career assistance and placements phase of the program à Talk you through industry insights and best practices à Provide you with interview tips and job search advice MONITOR PROGRESS à Set learning goals à Discuss your progress status with trainers and other industry mentors on a regular basis to ensure consistent advancement RESEARCH SHOWS THAT THROUGH MENTORSHIP YOU ARE: more likely to get a raise 20% 5x Source: more likely to get promoted IMARTICUS IMMERSION Imarticus Immersion is an industry-driven networking event that we organize for our students to provide them with an opportunity to: Participate in the batch convocation ceremony Network with industry veterans Gain valuable insights from industry speakers Connect with Imarticus’ alumni group 09

  10. CAREER SERVICES The Career Assistance Services (CAS) team works hand-in-hand with you from the first placement session during the program launch right until the final mock interviews on course completion. We thoroughly prepare you to be interview-ready and ensure you land your dream job. Mock Interviews by Domain Experts Hackathons Resume Building Sessions by Hiring Experts Placement Portal Access for Job Opportunities Placement Drives with Hiring Partners, assured interview opportunities 1:1 Career Mentorship by Industry Expert CAREER SERVICES PLACEMENT PARTNERS ... and more 10

  11. DIVERSE JOB ROLES Our students receive placement opportunities across diverse job roles at leading firms with a salary hike of 40% to 80%. DATA SCIENTIST DATA SCIENCE ENGINEER MACHINE LEARNING ENGINEER ARTIFICIAL INTELLIGENCE ANALYST DATA ANALYST MACHINE LEARNING CONSULTANT BUSINESS ANALYTS WEB & SOCIAL MEDIA ANALYST CERTIFICATION On completion of the Post Graduate Program in Analytics & AI, aspirants will receive an industry-endorsed Certificate of Completion. Certificate of Completion Awarded to Suganya L as prescribed by the Institute for John Smith upon successful completion of the curriculum Post Graduate Program In Data Analytics Case Study Partners: Harish Thakkar Head of Faculty Nikhil Barshikar Director February, 2019 Imarticus Learning Private Limited www.imarticus.org 11

  12. SMART CLASSROOMS Never Miss a Class! All your lectures and classes are recorded and archived in our state-of-the-art learning management system. The lectures are then made available to our students to enable them to refer to the lectures and brush up on challenging concepts. BENEFITS: à Digitally enhanced learning experience à High quality HD smart lecture recording system (get access to recorded lectures in HD quality) à Access recordings anytime anywhere LEARNING MANAGEMENT SYSTEM Our postgraduate students receive exclusive access to our hi-tech learning management system (LMS) that ensures a seamless self-paced online learning experience. KNOWLEDGE REPOSITORY 24/7 access to high-quality self-paced videos curated by industry leaders SELF-PACED LEARNING Anytime access to all your recorded lectures, presentations and study material TRACK YOUR PROGRESS Track and monitor your learning curve for the duration of the course HONE YOUR SKILLS Work on quizzes and assignments to test your knowledge through the LMS OFFLINE LEARNING Access your lectures and study material in offline mode to learn anytime, anywhere! 12

  13. FACULTY Our teaching staff comprises specialists and working professionals from renowned Financial Services and Analytics firms such as JP Morgan, Nomura, Genpact, Accenture, Citibank and Barclays and possess over 150 years of combined domain expertise that ensures your learning is industry-relevant and extremely job-specific. 4.8 4.7 4.7 4.7 4.6 Overall Rating Experiential Learning & Practicality Presentation Skills & Delivery Enthusiasm for the Subject Course Preparation & Organisation *Indicative faculty profiles: DR. D. PRADEEP KUMAR | Data Science | Machine Learning | Data Mining Dr. D. Pradeep Kumar holds over 6 years of research experience in Machine Learning, Data Mining, Soft computing, Time Series Forecasting and related topics and over 3 years of full-time teaching experience at an autonomous engineering college. Dr. Pradeep is a qualified UGC-NET lectureship and GATE CS. His specialties include research and development of various soft computing hybrid models of time series forecasting and applying them in banking and finance and related domains; Learning new programming languages and programming the solutions of different problems. Dr. D. Pradeep Kumar has been nominated by Analytics India Magazine as one of the top 10 most prominent Data Science academicians in India. VINAY BORHADE | Python, ML, Deep Learning and R Programming Vinay’s tech expertise includes AI – Machine Learning, Python, PL-SQL, and Big Data – Netezza, Java/J2EE. Having served more than 10 years with Bank of America (Merrill Lynch), he has worked on projects like Finance, Liquidity and Capital Risk (Regulatory Reporting) and has won repeat business from clients for BOA using technologies like Machine Learning, Capitalize: Data Analytics, Quartz, Python, IBM Netezza, Oracle (Hexadata). Vinay started his Career with Patni Computer Systems and Zinc as Sr. Software Engineer and has gained knowledge and expertise in BFSI domain. He is a B.E in computers from Mumbai and has strong techno-functional skills. ARUNKUMAR NAIR | Artificial Intelligence | Machine Learning | Python | Big Data Arunkumar has over 19 years IT experience in Big Data Analytics, Data Visualization Data Warehouse, ,24X7 DBA, Cloud and application projects and 2 years of onsite experience in the USA and the Middle East. He holds extensive hands on technical expertise, architecture and provided solutions and has the ability to give technology vision, ramp-up and manage large teams. He has Worked for clients like Rocky Mountain, Navteq(Nokia), M&T Bank, WeightWatchers, Hollywood Media, SHRM USA. Arunkumar is passionate about Analytics because he can drive emerging Big Data technology and align it with business growth. SRIRAMAN RAJAGOPALAN | Business Analytics | Big Data | Machine Learning Sriraman has over 20 years of experience in the domains of business analytics, big data and machine learning. The first 15 years of his career were in the IT/IT-ES sector where he worked as a CRM technical-functional consultant. Over the last 5 years, he has worked as an independent analytics consultant for various corporate clients, where he has developed and deployed multiple analytics projects. 13

  14. INDUSTRY LANDSCAPE Analytics and AI enable organizations to improve and optimize their products and services. These are key in analyzing and generating valuable business insight from the huge amount of structured and unstructured data in an organization, ultimately empowering business. IN DEMAND SKILL SETS Machine Learning Deep Learning Data Science Neural Networks Natural Language Processing Artificial Intelligence Computer Vision AI and machine learning have the potential to create an additional $2.6T in value by 2020 in Marketing and Sales, and up to $2T in manufacturing and supply chain planning. 1 2 Gartner predicts the business value created by AI will reach $3.9T in 2022. 3 IDC predicts worldwide spending on cognitive & Artificial Intelligence systems will reach $77.6B in 2022. The machine learning market size is expected to grow $8.8 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the 4 5 forecast period. Artificial Intelligence will create 2.3 million Machine Learning jobs by 2020, growing exponentially every year. 11 14

  15. Student Reviews Speaking about my experience, I really loved and enjoyed every step of learning Data Science with Imarticus. Continuous engagement in the deliverance of important knowledge with simultaneous practical exposure made me compatible with the learning experience. The curriculum is extremely informative and outstanding by nature. Their learning atmosphere is highly unique. The trainers and the professors are equally supportive and are eager to clear your doubts and lacunas. By doing the Data Analytics course here, I secured a job for me. I believe Imarticus Learning is a great and outstanding institute. One who is looking forward to kick-start his or her career in Data Analytics needs to go for Imarticus. Their teaching faculty is highly experienced and deliver the knowledge effectively. Not only the curriculum is extensive and informative, but you get to work on the real- world problems related to Data Analytics. Whenever any doubts or confusion arises, you will find yourselves accompanied by an experienced faculty to solve the problems. - Karen Soares - Febin George Placed at: Placed at: Admission The Post Graduate Program in Analytics and Artificial Intelligence is ideal for experienced professionals who are interested in working in the Analytics and AI industry, and are keen on enhancing their technical skills and business understanding of Machine Learning & Deep Learning. It is also suitable for fresh graduates who want to build a career in Machine Learning & AI. WHO IS THIS COURSE FOR? This is an ideal course for Engineers, Software/IT Professionals, Data Professionals and Professionals with strong domain experience. The candidate should have basic knowledge of programming and mathematics. Admission Process STEP 3 IN PERSON INTERVIEW: Communication, motivation STEP 2 PRE-ASSESSMENT: Online aptitude test STEP 1 BASIC QUALIFICATION CHECK: Academic certificates 15

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