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The IT industry is undergoing a seismic shift, and the demand for skilled Artificial Intelligence & Business Intelligence professionals is skyrocketing. A post-graduate program in Artificial Intelligence & Business Intelligence Program positions you at the forefront of this transformation. Emerging India Analytics’ program equips you with the expertise to unlock a thriving career in this dynamic field, empowering you to solve complex challenges and drive innovation through the power of data and intelligent systems.
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Introduction
ARTIFICIAL INTELLIGENCE & BUSINESS INTELLIGENCE
The IT industry is undergoing a seismic shift, and the demand for skilled Artificial Intelligence & Business Intelligence professionals is skyrocketing. A post-graduate program in Artificial Intelligence & Business Intelligence Program positions you at the forefront of this transformation. Emerging India Analytics’ program equips you with the expertise to unlock a thriving career in this dynamic field, empowering you to solve complex challenges and drive innovation through the power of data and intelligent systems.
Tools
Meet Your Mentors
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Bidhan Sen
Data Scientist & Data Science/Analytics Trainer
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Bidhan Sen
Data Scientist & Data Science/Analytics Trainer
Bidhan Sen has a rich background in data analytics, with comprehensive experience in tools like Power BI, Tableau, Python, SQL, and more. His journey spans across several companies, including Tata Consultancy Services, Brillio Technologies, and Amazon, where he has held roles varying from System Engineer to Senior Data Engineer. His responsibilities have encompassed ETL processes, creating reports and dashboards, data analysis, and client interaction across various projects.
His role at Brillio Technologies involved extensive work using Power BI and Tableau to create insightful reports for clients, analyzing historical sales data, developing dashboards, and providing actionable insights for business strategies. Additionally, his experience at Amazon showcases his ability to handle end-to-end ETL tasks, root cause analysis, and effective communication with clients.
Bidhan holds a BE in Mechanical Engineering from M. Visvesvaraya Institute of Technology. He also holds a PL-300 certification as a Microsoft Certified Data Analyst. His skill set includes not only technical proficiency but also a strong understanding of statistics, mathematics, and machine learning algorithms.
Outside of work, Bidhan has interests in badminton, meditation, and cooking.
- Phone: +91 88605 99698, 0120 457 5657
- Email: care@emergingindiagroup.com
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Uttam Grade
Artificial Intelligence & Business Intelligence Trainer
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Uttam Grade
Artificial Intelligence & Business Intelligence Trainer
Uttam Grade, Data Scientist and Data Science Trainer has many years of demonstrated ability as Data science & Mathematics enthusiast to deliver value via advanced data science & analytics. He has successfully delivered his knowledge with premier institutions like Imarticus & Edyoda Institutions. He has taken multiple live YouTube session with the organization and successfully added values to the business. He demonstrated his skills into multiple domains like Retail, Banking, Technology domains in data-driven decision making by leveraging math & programming with conceptual knowledge in Machine Learning, Natural Language Processing, Deep Learning, Predictive modelling & Statistical Modelling. He is driven to deliver results by quickly grasping business priorities, striding through ambiguity, and effectively designing solution approaches pertaining to real life applications for problems in the online delivery. He is passionate about sharing knowledge in data science and analytics field through quality training and coaching.
He holds B.Sc. (Mathematics), M.Sc. (Mathematics), M.S (Machine Learning & Artificial Intelligence) and certifications such as Python 101 for Data Science, Data Analytics NIIT, AWS Solution Architects, Oracle Database Certified Associate, Decision Sciences and Alteryx Core.
- Phone: +91 88605 99698, 0120 457 5657
- Email: care@emergingindiagroup.com
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Dr Lakshmi Sree Kailasam
Senior IT Consultant and Artificial Intelligence & Business Intelligence c,Trainer
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Dr Lakshmi Sree Kailasam
Senior IT Consultant and Artificial Intelligence & Business Intelligence c,Trainer
Dr. Lakshmi has 16+ years of work experience in various domains like ISO, Scrum, Agile and Project Management, Cyber Security, Capital Markets, Healthcare, CPG, FMCG, Retails, HR Analytics, Digital Marketing, Information Technology, Infrastructure Outsourcing, Insurance, Spend Analytics, Cost Modelling. She has worked as a Business Analyst, Data Analyst, Business Intelligence, Data Visualization specialist.
She has a BE (IT), MBA, Professional Doctorate in Management, Doctorate in Management Studies. She is currently pursuing Human Rights Law and Cyber Law & Forensics.
She also holds various certifications such as Business Analyst / Business Analytics – Software Technology Group, SQL Server 2012, VB.NET - Software Technology Group, Tableau Desktop – Tableau Software Company, ISO Certified, Sharepoint 2007 / 2010 – Business User, VBA, Open Office Certified, Six Sigma- Yellow Belt, Green Belt Trained.
- Phone: +91 88605 99698, 0120 457 5657
- Email: care@emergingindiagroup.com
lEARN WITH A WORLD CLASS CURRICULUM
Module 1. Data Science with Advanced Excel, Python, Statistics, Machine Learning, Deep Learning and R-Programming
Orientation (Introduction to Data Science, Scope of Data Science)
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📚No. of Lectures:1
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⏳Duration of Lecture:1.5 hours
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📝Assesment:0
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🌟Assignment: 0
- Microsoft Excel Overview, Formatting excel and shortcuts.
- Basic Formulas, Sorting data, Filtering data, Column chart.
- Pie chart, Pivot tables, Vlookup, Match Function, Lookup Function.
- VBA, Interview Questions.
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📚No. of Lectures: 4
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⏳Duration of Lecture: 12 Hours
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📝Assessment: 3
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🌟Assignment: 2
- Why Python, PEP-8, Variables,Data Types, Operators,Strings, Indexing
- Block Structure, Data Structures.
- Functions, Lamda Function ,Modules, Class.
- Intro to Numpy, Pandas,
- Pandas, Matplotlib.
- Seaborn.
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📚No. of Lectures: 6
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⏳Duration of Lecture: 18 Hours
-
📝Assessment: 3
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🌟Assignment: 2
- Descriptive stats [Sample, Population, Mean, Median, Mode, Std Deviation, Variance, Range, IQR, Outliers]
- skewness, kurtosis,Probability
- Probability Distributions- Discrete Probability Distribution [Probability Mass Function, Binomial Distribution, Poisson Distribution, Discrete Uniform Distribution
- Probability Distributions- Continuous Probability Distribution [Probability Density Function, Normal Distribution, Continuous Uniform, Exponential Distribution, Central Limit Theorem, Confidence Interval
- Hypothesis testing [Z test: One Sample , Student T distribution: One Sample T-test, Two Sample T-test]
- Hypothesis testing : [Chi Square- Chi-Square goodness of fit, Chi square test of independence]
- Annova [one Way Annova]
- Covariance, Correlation [ Pearson, Spearman, Linear]
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📚No. of Lectures: 4
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⏳Duration of Lecture: 12 Hours
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📝Assessment: 4
-
🌟Assignment: 2
- Introduction to ML, Encoding, Feature Scaling[Normalization, Standardization], Linear regression
- AUC ROC Curve, Confusion Matrix, MSE, MAE,RMSE, R2 Score, Adj R2 Score, F1 Score, Accuracy
- Logistic Regression,Decision Trees
- Cross Validation [K-fold cross-validation, Hold-out cross-validation, Stratified k-fold cross-validation]
- Naive Bayes,KNN
- SVM,Random Forest
- Type 1 and Type 2 error, Lasso Regression, Ridge Regression
- Ada Boost, Adagrad
- XgBoost
- PCA,K-Means
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📚No. of Lectures: 10
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⏳Duration of Lecture: 30 Hours
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📝Assessment: 4
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🌟Assignment: 4
- Introduction to Neural Network, Foreward Propagation, Activation Function
- Activation Function[ Linear, Sigmoid, Tanh, ReLu, Leaky ReLu, ELU, Softmax, Swish]
- Optimizers[ SGD, RMSProp, Adam, Adadelta, Adamax, Adagrad,Nadam]
- Loss [Classification- BinaryCrossentropy class/ CategoricalCrossentropy class/ SparseCategoricalCrossentropy class/ Poisson class/ binary_crossentropy function/ categorical_crossentropy function/ sparse_categorical_crossentropy function]
- Loss[Regression- MeanSquaredError class, MeanAbsoluteError class, MeanAbsolutePercentageError class MeanSquaredLogarithmicError class, CosineSimilarity class]
- Weight Initialization [ Xavier Weight Initialization, Normalized Xavier Weight Initialization, He weight Initialization]
- Padding, Pooling, Convolution
- NN Implemention
- Callbacks [ ReduceLROnPlateau, ModelCheckpoint]
- Callbacks [Earlystopping, TensorBoard], Keras Tuner
- Neural Network Implementation
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📚No. of Lectures: 11
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⏳Duration of Lecture: 33 Hours
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📝Assesment: 3
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🌟Assignment: 2
- Introduction to TSA, Component of TSA
- Methods to check stationarity [ADF (Augmented Dickey-Fuller), KPSS (Kwiatkowski–Phillips–Schmidt–Shin)]
- Time Series Analysis – AR
- Time Series Analysis [Moving Average, Simple Moving Average (SMA), Cumulative Moving Average (CMA) Exponential Moving Average (EMA)]
- ACF, PACF, ARMA, ARIMA
- LSTM, GRU
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📚No. of Lectures: 6
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⏳Duration of Lecture: 33 Hours
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📝Assesment: 3
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🌟Assignment: 2
- What is R Programming, Variables and Data Type in R.
- Logical Operators,Vectors,List,Matrix,Data Frame,Flow Control, Functions in R.
- Data Manipulation in R- dplyr, Data Manipulation in R- tidyr.
- Data Visualization In R,
- 📚No. of Lectures: 4
- ⏳Duration of Lecture: 12 Hours
- 📝Assessment: 2
- 🌟Assignment: 0
Project Discussion and Doubts Class.
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📚No. of Lectures: 1
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⏳Duration of Lecture: 1.5 Hours
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🌟Projects: 5
Module 2. Business Intelligence Using SQL,Tableau and Power Bl
Orientation Session(Introduction to Business Intelligence)
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📚No. of Lectures: 1
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⏳Duration of Lecture: 1.5 Hours
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📝Assessment: 0
-
🌟Assignment: 0
- BASIC OF DATABASE,TYPES OF DATA BASE,DATA TYPES,SQLOPERATORS,EXPRESSION (BOOLEAN,DATE,NUMERIC), CREATE,INSERT
- DROP,TRUNCATE,DELETE,ALTER, UPDATE,SELECT, RANGE OPERATER,IN,WILDCARD, LIKE CLAUSE
- CONSTRAINT,AGGREGATION FUNCTION,GROUP BY,ORDER BY , HAVING,
- JOINS,CASE,COMPLEX QUERIES,DOUBT CLEARING
- Trigger, Stored Procedures, Common Table Expression, Index, Except, Exists, Grouping set
- Pivot, Rollup, Cube, Constraints, Partition
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📚No. of Lectures: 6
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⏳Duration of Lecture: 18 Hours
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📝Assessment: 3
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🌟Assignment: 2
- Tableau Desktop, Tableau products
- Data import, Measures, Filters
- Data transformation, Marks, Dual Axis
- Pareto Analysis, Table calculation, Multiple data source blending
- Advanced charting techniques, Tableau prep tool
- Manage worksheets, Data visualization, Dashboarding,Project
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📚No. of Lectures: 6
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⏳Duration of Lecture: 18 Hours
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📝Assessment: 3
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🌟Assignment: 2
- POWER BI PLATFORM, PROCESS FLOW
- FEATURES, DATASET,BINS
- PIVOTING, QUERY GROUP,DAX FUNCTION
- Multiple data source blending
- Basic of SSIS for ETL
- FORMULA, CHARTS, REPORTS, DASHBOARDS
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📚No. of Lectures: 6
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⏳Duration of Lecture: 18 Hours
-
📝Assessment: 3
-
🌟Assignment: 1
Project Discussion and Doubts Class.
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📚No. of Lectures: 1
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⏳Duration of Lecture: 1.5 Hours
-
🌟Projects: 3 – 5
Module 3. BIG DATA
Orientation- Introduction to Big Data Analytics
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📚No. of Lectures: 1
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⏳Duration of Lecture: 1.5 Hours
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📝Assessment: 0
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🌟Assignment: 0
- Introduction, Installation, Data Modelling , Database operation- create and drop, Collection- create and drop Data types, Insert, Query
- Update, Delete, Find, Limit , Skip, Create Index, ObjectID
- Aggregate, Replication, Shrading, Dumping and Restore, Mongostat and Mongotop,
- MongoDB client setup using Java, Reference and Embedded Relationship, DbRefs, Cover Query, $explain, $hint
- findAndModify, Indexing Array and subfield, ObjectId, Mapreduce, Text Search, Index, RegeX
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📚No. of Lectures: 5
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⏳Duration of Lecture: 15 Hours
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📝Assessment: 2
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🌟Assignment: 1
- Installation, Syntax [main()/printIn()/print()], Variable [String, Int, Boolean, float, char]
- Datatypes, Type casting, Operators, conditions, loop [ for, while], switch, break, continue
- array, methods [ parameters, overloading, recursion]
- Class [attributes, constructors, modifiers, oops, encapsulation, polymorphism, enums, abstraction, interface, exceptions, iterator, hashmap, hashset,threads], file handling
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📚No. of Lectures: 5
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⏳Duration of Lecture: 15 Hours
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📝Assessment: 2
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🌟Assignment: 1
- Types of Data,Introduction to Bigdata(History,V’s of Bigdata, Advantages & Disadvantages of BigData ), Use of Bigdata in different sectors, Introduction to Hadoop, Scaling (Horizontal and Vertical), Challenges in Scaling, Concept and challenges in parallel computing, Distributed Computing and use in Hadoop,Intro to Tools in Hadoop, Life cycle of Bigdata Analytics
- On Premises Installation Oracle Virtual Box and setup of VM & Ubuntu, Basic Linux command,Download and Installation of Hadoop,Introduction to Hadoop, Core components of Hadoop, Hadoop working Principle,HDFS Architecture
- VM creation on Cloud (AZURE), Configuration & Insight to Single Node Hadoop Deployment(bsshrc, hadoop-env, core-site, hdfs-site,mapred-site,yarn-site) , Format HDFS Namenode.
- Hadoop Commands and implementation
- Mapreduce, Mapreduce Implementation
- Mapreduce Implementation, Introduction to Hive, Hive Installation,Hive Implementaion,
- Hive Query Language, SQL Opeartions
- HIVE_SQL Opeartions
- Introduction to Sqoop, Hbase, Installation of Sqoop, Installation of Hbase, Installation of Spark, Hbase Query
- Hbase Query, PySpark Query
- PIG Installation and Query
- Oozie
- Flume [Introduction , Architecture, Environment, Implementation]
- Scala [ introduction, Architecture, Environment, Variable, Functionsclass objects]
- Scala [ Operators, Traits, Implementation]
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📚No. of Lectures: 16
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⏳Duration of Lecture: 48 Hours
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📝Assessment: 6
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🌟Assignment: 3
Project Discussion and Doubts Class.
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📚No. of Lectures: 1
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⏳Duration of Lecture: 1.5 Hours
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🌟Projects: 3-5
MODULE 4: ARTIFICIAL INTELLIGENCE
Introduction to text mining, text processing using python.
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📚No. of Lectures: 1
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⏳Duration of Lecture: 3 Hours
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📝Assessment: 0
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🌟Assignment: 0
- Introduction to text mining, text processing using python
- Introduction to NLTK, Tokennization, Stemming, Bag of words
- Sentiment Analysis, Name- Entity Recognition
- Text Segmentation, Text Mining, Text Classification
- LDA
- Automatic speech recognition (Librosa/gardio/transformers)
- Automatic speech recognition (Librosa/gardio/transformers)
- 📚No. of Lectures: 7
- ⏳Duration of Lecture: 21 Hours
- 📝Assessment: 4
- 🌟Test/Projects
- Introduction to Image Processing, Feature Detection, OpenCV
- Convolution, Padding, Pooling & its Mechanisms
- Forward Propagation & Backward Propagation for CNN
- CNN Architectures like AlexNet, VGGNet, InseptionNet, ResNet, Transfer Learning
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📚No. of Lectures: 4
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⏳Duration of Lecture: 12 Hours
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📝Aassessment: 2
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🌟Assignment: 1
- Introduction, YOLO Architecture, Working Methods
- Application, Pre trained object detection
- Custom trained object detection, Darknet preparation, Data collection, Image labelling, Sync Compilation and testing
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📚No. of Lectures: 3
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⏳Duration of Lecture: 9 Hours
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📝Aassessment: 1
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🌟Assignment: 1
- RL Framework, Component of RL Framework, Exampes of Systems
- Types of RL Systems, Q-Learning
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📚No. of Lectures: 4
-
⏳Duration of Lecture: 12 Hours
-
📝Aassessment: 1
-
🌟Assignment: 1
Project Discussion and Doubts Class.
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📚No. of Lectures: 1
-
⏳Duration of Lecture: 1.5 Hours
-
🌟Projects: 3-5
MODULE 5: Elective 01
- Probability, Probability distribution, Discrete Probability Distribution, Probability Mass Function and Binomial Distribution
- Poisson Distribution, Discrete Uniform Distribution, Continuous Probability Distribution, Probability Density Function, Normal Distribution
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📚No. of Lectures: 2
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⏳Duration of Lecture: 6 Hours
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📝Aassessment: 0
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🌟Assignment: 0
- Continuous Uniform, Exponential Distribution, Hypothesis Testing, Bayes Theorem and Correlation, Hypothesis Testing
- Z Distribution, Student’s T distribution, Chi-Square, Bayes Theorem, Correlation, Pearson Correlation, Linear Correlation
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📚No. of Lectures: 2
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⏳Duration of Lecture: 6 Hours
-
📝Aassessment: 2
-
🌟Assignment: 1
- K-fold cross-validation , Hold-out Cross-validation and Stratified k-fold cross-validation
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📚No. of Lectures: 1
-
⏳Duration of Lecture: 3
-
📝Assessment: 0
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🌟Assignment:0
- Hyperparameter Tuning, Ensemble Learning, Lasso Regression, Ridge, Regression, Adaboost, Adagrad
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📚No. of Lectures: 1
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⏳Duration of Lecture: 3 Hours
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📝Aassessment: 0
-
🌟Assignment: 0
- Classification, Binary Crossentropy class, Categorical Crossentropy class, Sparse Categorical Crossentropy class
- Poisson class, binary_crossentropy function, categorical_crossentropy function, sparse_categorical_crossentropy function
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📚No. of Lectures: 2
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⏳Duration of Lecture: 6 Hours
-
📝Assesment: 0
-
🌟Assignment: 0
- Regression, MeanSquaredError class, MeanAbsoluteError class, MeanAbsolutePercentageError class, MeanSquaredLogarithmicError class, CosineSimilarity class
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📚No. of Lectures: 0
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⏳Duration of Lecture: 3 Hours
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📝Assesment: 0
-
🌟Assignment: 1
MODULE 6: Elective 02
- Trigger, Stored Procedures, Common Table Expression and Index
- Except, and Exists, Grouping set, Pivot, Rollup, Constraints, Partition
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📚No. of Lectures: 2
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⏳Duration of Lecture: 3 Hours
-
📝Assessment: 0
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🌟Assignment: 0
- Pareto Analysis, Table calculation, Multiple data source blending
- Advanced Charting Techniques and Tableau Prep tool, Multiple data source blending
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📚No. of Lectures: 1
-
⏳Duration of Lecture: 3 Hours
-
📝Assessment: 0
-
🌟Assignment: 0
- Basic of SSIS for ETL, Interview Questions
-
📚No. of Lectures: 1
-
⏳Duration of Lecture: 3 Hours
-
📝Assessment: 0
-
🌟Assignment: 0
Project Discussion and Doubts Class.
-
📚No. of Lectures: 1
-
⏳Duration of Lecture: 1.5 Hours
-
🌟Projects: 5-7
Course Certificates
Career Services By emergingindiagroup
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Placement Assistance
Placement opportunities are provided once the learner is moved to the placement pool upon clearing Placement Readiness Test (PRT)
Exclusive access
Exclusive access to our dedicated job portal and apply for jobs. More than 400 hiring partners’ including top start-ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.
Mock Interview Preparation
Students will go through a number of mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.
1 on 1 Career Mentoring Sessions
Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learners’ educational background, past experience, and future career aspirations.
Career Oriented Sessions
Over 10+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions and that will help you stay on track with your up skilling objective.
Resume & LinkedIn Profile Building
Get assistance in creating a world-class resume & LinkedIn Profile from our career services team and learn how to grab the attention of the hiring manager at profile shortlisting stage
Real World Projects
Projects will be a part of your Advance Post Graduate Certification Data Science & Artificial Intelligence to solidify your learning. They ensure you have real-world experience in Data Science and AI.
- Practice 20+ Essential Tools
- Designed by Industry Experts
- Get Real-world Experience
Real State Analytics
Real Estate Analytics will involve supervised learning with an ensemble of various regression algorithms where we will optimize the predictions based on the error rate.
Solar Power Efficiency
The project will encompass three target variables that we will predict using the supervised machine learning algorithms for regression problems and minimize the error by tuning the hyperparameters.
Recommendation Engine
In the Recommendation Engine project, we will use singular value decomposition to draw out relevant recommendations for music and movie selections based on the historical data points.
Stock Price Prediction
Forecasting a stock price based on time series analysis and forecasting using ARIMA, Seasonal ARIMA and other deep learning techniques to forecast stock prices based on historical data points.
Weather Forecasting
Ensemble of supervised machine learning algorithms to predict and forecast the weather based on historical data points, and optimization of the model to reduce the error in predictions using hyperparameter tuning and other data modeling techniques.
Image Classification
The project involves processing image data that is fed to the convolutional neural networks which then efficiently predicts the species of the input that is provided during the predictions.
Gesture Recognition
In this computer vision project, we will extract frames in real time and feed it to an already trained neural network that will correctly classify the gesture that is most relevant to the specified frame in real time.
American Sign Language Recognition
In this project we will use computer vision and deep learning to feed real time frames that will correctly predict the ASL references to the signs made in the video and generate the audio for the same as well.
Sentiment Analysis
Classic NLP problem encompasses processing textual data and then feeding the information to the deep learning model which then processes and predicts the sentiment associated with the text in both supervised and unsupervised learning settings.
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Admission Details
The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.
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Submit Application
Tell us a bit about yourself and why you want to join this program
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Application Review
An admission panel will shortlist candidates based on their application
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Admission
Selected candidates will be notified within 1week.
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Others Payment Options
We provide the following options for one-time payment.
![Provider](https://emergingindiagroup.com/wp-content/uploads/2024/03/provider.png)
Internet Banking
![Debit Cards](https://emergingindiagroup.com/wp-content/uploads/2024/03/debit-cards.png)