Python Intro,IDE and Python Packages
Python Programming
Python Data Types – Dictionary, List and Set
Numpy Packages – Array Handling and Manupulation
Pandas Packages – Dataframe and Loading Excel, CSV File
Matplotlib Packages – Line graph and Visualisation
Histogram, Scatter Diagram, Box Plot and Bar Graph
Area Chart, Dual Axis, Array reshaping, reverse matrix analysis
Python – Operators and String Manupulation Control Structures(IF,IF-ELSE,IF-ELIF-ELSE,WHILE & FOR LOOP)
Python – Data Preparation Process
Python – Functions WITH and WITHOUT arguments
Python – File Processing and Data Collection Methods
Python – Time Series Analysis and Forcasting
Python – Simple Predictive Analysis
Data Science with Python
Data Science Application across Multiple Domain and Business Function
Data Science Project LifeCycle
Multiple Predictive Model using Python
Python – Simple and Multiple Predictive Model in Practical
Python Correlation Analysis
Python Classication Model Buildin
Data Science – Experimental Design Analysis
Classication Technique – Discriminant Analyssi
Data Science – Association Rule – Apriori Algorithm
Data Science – Building Recommendation System – (Market Basket Analysis) Data Architecture Design, Data Warehousing and it’s Schema Design Image Processing and Image Extraction Image Processing and Object Recognition Summarisation of Data Science Algorithm (Data Science Process)
Machine Learning Introduction and it’s Modules, Overview of Supervised Learning Algorithm, Overview of UnSupervised Learning Algorithm, How Machine Learning helps to automate the Business Process, Real Time Application of Machine Learning
Simple Linear Regression, Multiple Linear Regression, Assumptions of Linear Regression, Linear Regression Case Study, Linear Regression Project – Real Estate Model Building
Logistic Regression Concepts, Odds Ratio, Logit Function/ Sigmoid Function, Cost function for logistic regression, Application of logistic regression to multi-class classication, Assumption in Logistics Regression, Evaluation Matrix : Confusion Matrix, Odd’s Ratio And ROC Curve, Advantages And Disadvantages of Logistic Regression, Project Attrition and Bank Loan Modelling
ANOVA and ANCOVA Concepts, Coding of ANOVA, Application of ANOVA and ANCOVA
Discriminant Analysis, Statistics Associated with Discriminant Analysis, Eigen Value, Case Study with Discriminant Analysis
Naïve Bayes Concepts, Python Execution of Naïve Bayes, Conditional Probability, Bayes Theorem, Building model using Naive Bayes, Naive Bayes Assumption, Laplace Correction, NLP with Naive Bayes
Distance as Classier, Euclidean Distance, Manhattan Distance, KNN Basics, KNN for Regression & Classication
Basics of SVM, Margin Maximization, Kernel Trick, RBF / Poly / Linear
Decision Tree Concepts, Random Forest Concepts, Decision Tree and Random Forest Coding, Decision Tree and Random Forest – Attrition Project, Decision Tree and Random Forest – Bank Loan Modelling
Eigenvalues and Eigenvectors, Orthogonal Transformation, Using PCA
Clustering Methods, Agglomerative Clustering, Divisive Clustering, Dendogram, Basics of KMeans, Finding value of optimal K, Elbow Method, Silhouette Method
Apriori Algorithm, MBA – Market Basket Analysis, Multi level Association Rule, Application of Association Rule
Introduction about Correlation Analysis, Construction of Correlation Matrix, Person Product Movement Correlation, Partial Correlation, Non Metric Correlation
Time Series Analysis, Data Preparation, Stationary Data, Trends /Seasonility, ARIMA Model, SARIMA & Other Models
Deep Learning Fundamentals
Working of Neural Networks
Gradient Descent and Back Propagation
Activation Function
Tensorflow Introduction
Building Artificial Neural Networks (ANN)
Deep Learning-ANN-classification
Computer-Vision-opencv-part1-overview
Computer-Vision-opencv-part2-face_detection
intro to CNN
Introduction to RNN & Sequence prediction using RNN
Introduction to LSTM,
Sequence prediction using LSTM
Applications in text analytics , stock prediction , time series data
Basics of NLP
Removing Stop Words
Stemming & lemmatization
Parts of speech tagging
TFIDF vectorizer
Senmiment Analysis
Text Classification with Linear Models
Language Modelling with Probabilistic Graphical Models and Neural Networks
Word Embeddings and Topic Models
Machine Translation and Sequence-To-Sequence Models
Model-Based Reinforcement Learning (Dynamic Programming)
Model-Free Reinforcement Learning (SARSA, Monte Carlo, Q-Learning)
Approximate and Deep Reinforcement Learning (Deep Q-Learning)
Policy Gradient Reinforcement Learning
Advanced Topics on Exploration and Planning
What is BigData |
Characterstics of BigData |
Problems with BigData |
Handling BigData |
Linux Commands |
HDFS Commands |
SQOOP ARCH and HANDSON: How Import data from Target RDBMS TO HDFS. USecase1: With Primary Key and Without Primary Key useCase2: Boundary Query Without columns and With Columns UseCase3: Incremenatl Load Usecase4: How to Import all tables at a time Usecase5: How to Import all Tables with Exclude Tables UseCase6: How to Create Sqoop Job UseCase7: How to Use $Conditions in Sqoop UseCase8: How to Import data from RDBMS to HIVE TABLE Usecase9: How to Process Semi Structured data using Sqoop Usecase10: Sqoop Export from HDFS to RDBMS |
HIVE ARCH AND HANDSON: Different Types OF Tables In Hive PARTITIONING Different Types Of Partitioning Bucketing How to Perform Both Partitioning and Bucketing using one table Joins(Reducer Side Joins and MapSide Joins) How to Semi Structured Data using Hive Different File Format In Hive How to perform Updates and Deletes in Hive Hive Complex Types Hive UDf |
HBASE ARCH AND HANDSON: Differnce Between Hive,SQL and HBASE How to create tables,insert,update and delete How to import data from rdbms to HBASE using Sqoop How to Load CSV DATA INTO HBASE TABLE HIVE to HBASE INTEGRATION |
PIG AND MAPREDUCE |
SCALA: What Is Scala Differnce between JAVA and SCALA SCala Variables For,While and Do while Loop Condiotional Statements String,String Methods,String Interpolation Functions Higher Order Functionss Anonymous Functions Closure Function Currying Function Collections(Array,set,tuple,map and list) File Handling Exception Handling Traits |
Spark vs Map Reduce Architecture of Spark Spark Shell introduction Creating Spark Context Spark Project with Maven in Eclipse Cache and Persist in Spark File Operations in Spark RDD: What is RDD Transformations and Actions Loading data through RDD key-value pair RDD Pair RDD oeprations Running spark application with Spark-shell Deploying Application With Spark-Submit Spark-SQL: introduction to Spark SQL Hive vs SparkSQL Processing different fileformats using Spark SQL DataFrames DAG Lineage Graph Cluster types Optimizers Structured Streaming RDDs to Relations |
Spark Streaming: introduction to Spark Streaming Architecture of spark Streaming SparkStreaming vs Flume introduction to Kafka Kafka Architecture Spark Streaming integration with Kafka Overview Real Time Examples |
Tableau introduction
Different types of visualization using Tableau
Tableau Dashboard Creation
Tableau Story line creation
Time series using Tableau
Different types of Joins Using Tableau
Tableau Features – Filters and format the Column
Real time project using Tableau
Tableau Highlighter
Data Blending using Tableau
Table Calculation using Tableau
Parameters and Set using Tableau
Advanced Data Preparation using Tableau
Hierarchical clustering using Tableau
Complete Course Revision using Tableau
What is R? And Why R?-Different “flavors” of R-Installing R Studio DesktopUnderstanding R Studio-Installing Packages and Libraries in R Studio-Setting Your Work Directory.
Data Variables-Data Types – Operators – Keywords – ExceptionsFunctions
Vectors and Lists – Strings and Matrices – Arrays and Factors – Data Frames – Packages.
R- CSV files Read and Write and analyze the data – R- Excel files Read and Write and analyze the data
Introduction to Visualisation – Line Plots and Bar Charts – Pie Chart and Histogram – Scatter Plots and Parallel Coordinates – Advanced Plotting – Exporting Plots and Other Plotting Packages
Linear Regression Analysis – Formulation of Regression Model – Bivariate Regression – Statistics Associated with Bivariate Regression Analysis – Conducting Bivariate Regression Analysis – Multiple Regressions – Conducting Multiple Regression – Mapping Bivariate Regression with Real Time Example.
Logistic Function – Single Predictor Model – Determine Logistic Cut off – Estimated Equation for Logistic Regression
Factor Analysis Introduction – Factor Analysis Model – Statistics associated with Factor Analysis – Conducting Factor Analysis – Construction of Factor Analysis – Factor Analysis Method – Principal Component Analysis – Rotation Method – Mapping Factor Analysis with Real Time Example
Cluster Analysis Introduction – Statistics associated with Cluster Analysis – Conducting Cluster Analysis – Classification of Clustering Procedure – Hierarchical Clustering – Non Hierarchical Clustering
Association Rule Introduction – Apriori Algorithm – Multiple Association Rules – Market Basket Analysis (MBA) – Application of Apriori Algorithm and Market Basket Analysis
Naïve Bayes Introduction – Probabilistic Basics and Probabilistic Classification – Characteristics of Naïve Bayes – Real Time Case study using Naïve Bayes – Advantage and Shortcoming of Naïve Bayes
K – Nearest Neighbour Introduction – K – Nearest Neighbour Algorithm – Pre-Processing your dataset for KNN – How to measure “Nearby” – Choosing “K” and High “K” vs. Low “K” – Real Time case study using KNN – Advantage and Disadvantage of KNN
What is a Decision Tree? – How to create Decision Tree – Choosing and Identifying attributes for Decision Tree – Entropy and Information Gain with Intuitions – Pruning Trees and its types – Forward Pruning and Backward Pruning – Sub tree Replacement and Raising – Real time case study with Decision Tree
Ensample of Decision Tree.
Linear SVM using Hyperplane – Non-Linear Hyperplane using Kernal Trick and Advantage and Disadvantage of SVM
RFM Segmentation and Analysis – Propensity Modelling and its application – Churn Modelling using Operational Analytics – Fundamentals and Modelling Framework – Industry application – Market Basket Analysis using Marketing Analytics – Fundamentals and Analysis Framework – Industry Application – Price and In store Promotion using Retail Analytics – Price Elasticity and Optimization – Promotion Effectives using Analytics
Unsupervised Machine Learning – Merger and Acquisitions Analytics
Bank Loan Modeling – Automation of loan eligibility process – Dream Housing Finance Company
Prediction of English Premier League (EPL) Championship
Zomato Delivery Performance Analysis
Employee Attrition Rate Analysis
Team Deposit Plan – Machine Learning Classification – Portuguese Bankinh Institution
Predicting house prices for using supervised Machine Learning
Predictive Analytics with model simulationm – Ames Housing Authority.
Employee Termination Analysis
Principal Component Analysis – Dimension Reduction – LKP Share & Securities.
Telecom Chrum Case Study Using Sklearn
Handwritten Digit Classification Using ANN
Recommendation Engine
Sentiment Analyser
Building Chatbot
SMS Spam,Classifier
Twitter Sentiment Analyser
Analysis the Real-time Stock market using Regression
Predict the Medical condition of Person
Iris Flower classification is done using sepal and petal
Will person survive on titanic ship .
Will classify the person detect with cancer or not Customer Segmentation : Customer will divided into segments and behavior will analyze
Person will be loan defaulters in future of not .
kids Handwritten digits will be classified
test the wine quality and classifiy it.
On completion of the Post Graduate Program in Machine Learning and Artificial Intelligence, aspirants will receive an Industry-endorsed Certificate along with Internship Certificate.
Our Industry mentor team will guide you with:
– Provide unparalled 1:1 support and guidance
– Help execute in-class assignments and Projects
– Discuss & identify learning gaps and other solutions such as refresher sessions and one-on-one project feedback
-Set learning goals
-Discuss your progress status with trainers and other industry mentors on a regular basis to ensure consistent advancement
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