ARTIFICIAL INTELLIGENCE COURSE IN CUTTACK

MODULE 1 : DATA SCIENCE COURSE INTRODUCTION 

• CDS Course Introduction
• 3 Phase Learning
• Learning Resources
• Assessments & Certification Exams
• DataMites Mobile App
• Support Channels
  

MODULE 2 : DATA SCIENCE ESSENTIALS 

• Introduction to Data Science
• Evolution of Data Science
• Data Science Terminologies
• Data Science vs AI/Machine Learning
• Data Science vs Analytics
  

MODULE3 : DATA SCIENCE DEMO 

• Business Requirement: Use Case
• Data Preparation
• Machine learning Model building
• Prediction with ML model
• Delivering Business Value

MODULE 4 : ANALYTICS CLASSIFICATION 

• Types of Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics
  

MODULE 5 : DATA SCIENCE AND RELATED FIELDS 

• Introduction to AI
• Introduction to Computer Vision
• Introduction to Natural Language Processing
• Introduction to Reinforcement Learning
• Introduction to GAN
• Introduction to  Generative Passive Models
  

MODULE 6 : DATA SCIENCE ROLES & WORKFLOW

• Data Science Project workflow
• Roles: Data Engineer, Data Scientist, ML Engineer and MLOps Engineer
• Data Science Project stages

MODULE 7 : MACHINE LEARNING INTRODUCTION

• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
  

MODULE 8 : DATA SCIENCE INDUSTRY APPLICATIONS 

• Data Science in Finance and Banking
• Data Science in Retail
• Data Science in Health Care
• Data Science in Logistics and Supply Chain
• Data Science in Technology Industry
• Data Science in Manufacturing
• Data Science in Agriculture
  

MODULE 1 : PYTHON BASICS 

• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Operator’s precedence and associativity

MODULE 2 : PYTHON CONTROL STATEMENTS 

• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements
  

MODULE 3 : PYTHON DATA STRUCTURES 

• Basic data structure in python
• String object basics and inbuilt methods
• List: Object, methods, comprehensions
• Tuple: Object, methods, comprehensions
• Sets: Object, methods, comprehensions
• Dictionary: Object, methods, comprehensions
  

MODULE 4 : PYTHON FUNCTIONS 

• Functions basics
• Function Parameter passing
• Iterators
• Generator functions
• Lambda functions
• Map, reduce, filter functions
  

MODULE 5 : PYTHON NUMPY PACKAGE 

• NumPy Introduction
• Array – Data Structure
• Core Numpy functions
• Matrix Operations
  

MODULE 6 : PYTHON PANDAS PACKAGE 

• Pandas functions
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis
  

MODULE 1 : OVERVIEW OF STATISTICS 

• Descriptive And Inferential Statistics
• Basic Terms Of Statistics
• Types Of Data
  

MODULE 2 : HARNESSING DATA 

• Random Sampling
• Sampling With Replacement And Without Replacement
• Cochran's  Minimum Sample Size
• Simple Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Systematic Random Sampling
• Biased Random Sampling Methods
• Sampling Error
• Methods Of Collecting Data
  

MODULE 3 : EXPLORATORY DATA ANALYSIS 

• Exploratory Data Analysis Introduction
• Measures Of Central Tendencies: Mean, Median And Mode
• Measures Of Central Tendencies: Range, Variance And Standard Deviation
• Data Distribution Plot: Histogram
• Normal Distribution
• Z Value / Standard Value
• Empherical Rule  and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance
  

MODULE 4 : HYPOTHESIS TESTING 

• Hypothesis Testing Introduction
• P- Value, Confidence Interval
• Parametric Hypothesis Testing Methods
• Hypothesis Testing Errors : Type I And Type Ii
• One Sample T-test
• Two Sample Independent T-test
• Two Sample Relation T-test
• One Way Anova Test
  

MODULE 5 : CORRELATION AND REGRESSION 

• Correlation Introduction
• Direct/Positive Correlation
• Indirect/Negative Correlation
• Regression
• Choosing Right Method

MODULE 1: MACHINE LEARNING INTRODUCTION 

• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
  

MODULE 2: PYTHON NUMPY & PANDAS PACKAGE 

• NumPy & Pandas functions
• Array – Data Structure
• Core Numpy functions
• Matrix Operations
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis
  

MODULE 3: VISUALIZATION WITH PYTHON 

• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
• Advanced Python Data Visualizations
  

MODULE 4: ML ALGO: LINEAR REGRESSSION 

• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python
  

MODULE 5: ML ALGO: KNN 

• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python
  

MODULE 6: ML ALGO: LOGISTIC REGRESSION 

• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python
  

MODULE 7: PRINCIPLE COMPONENT ANALYSIS (PCA) 

• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python
  

MODULE 8: ML ALGO: K MEANS CLUSTERING 

• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Modeling in Python
  

MODULE 1: MACHINE LEARNING INTRODUCTION 

• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
  

MODULE 2: ML ALGO: LINEAR REGRESSION 

• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python
  

MODULE 3: ML ALGO: LOGISTIC REGRESSION 

• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python
  

MODULE 4: ML ALGO: KNN 

• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python
  

MODULE 5: ML ALGO: K MEANS CLUSTERING 

• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works: K Means theory
• Modeling in Python
  

MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA) 

• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python
  

MODULE 7: ML ALGO: DECISION TREE 

• Random Forest Ensemble technique
• How it works: Bagging Theory
• Modeling and Evaluation in Python
  

MODULE 8 : ML ALGO: NAÏVE BAYES 

• Introduction to Naive Bayes
• How it works: Bayes' Theorem
• Naive Bayes For Text Classification
• Modeling and Evaluation in Python
  

MODULE 9: GRADIENT BOOSTING, XGBOOST 

• Introduction to Boosting and XGBoost
• How it works: weak learners' concept
• Modeling and Evaluation of in Python
  

MODULE 10: ML ALGO: SUPPORT VECTOR MACHINE  (SVM) 

• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python
  

MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN) 

• Introduction to ANN
• How It Works: Back prop, Gradient Descent
• Modeling and Evaluation of ANN in Python
  

MODULE 12: ADVANCED ML CONCEPTS 

• Adv Metrics (Roc_Auc, R2, Precision, Recall)
• K-Fold Cross validation
• Grid And Randomized Search CV In Sklearn
• Imbalanced Data Set : Smote Technique
• Feature Selection Techniques
  

MODULE 1: TIME SERIES FORECASTING - ARIMA 

• What is Time Series?
• Trend, Seasonality, cyclical and random
• Autoregressive Model (AR)
• Moving Average Model (MA)
• Stationarity of Time Series
• ARIMA Model
• Autocorrelation and AIC 

MODULE 2: FEATURE ENGINEERING 

• Introduction to Features Engineering
• Transforming Predictors
• Feature Selection methods
• Backward elimination technique
• Feature importance from ML modeling
  

MODULE 3: SENTIMENT ANALYSIS 

• Introduction to Sentiment Analysis
• Python packages: TextBlob, NLTK
• Case study: Twitter Live Sentiment Analysis
  

MODULE 4: REGULAR EXPRESSIONS WITH PYTHON 

• Regex Introduction
• Regex codes
• Text extraction with Python Regex
  

MODULE 5: ML MODEL DEPLOYMENT WITH FLASK 

• Introduction to Flask
• URL and App routing
• Flask application – ML Model deployment
  

MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL 

• MS Excel core Functions • Pivot Table
• Advanced Functions (VLOOKUP, INDIRECT..)
• Linear Regression with EXCEL
• Goal Seek Analysis
• Data Table
• Solving Data Equation with EXCEL
• Monte Carlo Simulation with MS EXCEL
  

MODULE 7: AWS CLOUD FOR DATA SCIENCE

• Introduction of cloud
• Difference between GCC, Azure,AWS
• AWS Service ( EC2 and S3 service)
• AWS Service (AMI), AWS Service (RDS)
• AWS Service (IAM), AWS (Athena service)
• AWS (EMR), AWS, AWS (Redshift)
• ML Modeling with AWS Sage Maker 

MODULE 8: AZURE FOR DATA SCIENCE 

• Introduction to AZURE ML studio
• Data Pipeline and ML modeling with Azure
  

• MODULE 1: DATABASE INTRODUCTION 

  • DATABASE Overview
  • Key concepts of database management
  • CRUD Operations
  • Relational Database Management System
  • RDBMS vs No-SQL (Document DB)
    

  MODULE 2: SQL BASICS 

  • Introduction to Databases
  • Introduction to SQL
  • SQL Commands
  • MY SQL  workbench installation
  • Comments • import and export dataset
    

  MODULE 3: DATA TYPES AND CONSTRAINTS 

  • Numeric, Character, date time data type
  • Primary key, Foreign key, Not null
  • Unique, Check, default, Auto increment
    

  MODULE 4: DATABASES AND TABLES (MySQL) 

  • Create database
  • Delete database
  • Show and use databases
  • Create table, Rename table
  • Delete table, Delete  table records
  • Create new table from existing data types
  • Insert into, Update records
  • Alter table
    

  MODULE 5: SQL JOINS 

  • Inner join
  • Outer join
  • Left join
  • Right join
  • Cross join
  • Self join
    

  MODULE 6: SQL COMMANDS AND CLAUSES 

  • Select, Select distinct
  • Aliases, Where clause
  • Relational operators, Logical
  • Between, Order by, In
  • Like, Limit, null/not null, group by
  • Having, Sub queries
    

  MODULE 7: DOCUMENT DB/NO-SQL DB 

  • Introduction of Document DB
  • Document DB vs SQL DB
  • Popular Document DBs
  • MongoDB basics
  • Data format and Key methods
  • MongoDB data management
    

MODULE 1: GIT  INTRODUCTION 

• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture

MODULE 2: GIT REPOSITORY and GitHub 

• Git Repo Introduction
• Create New Repo with Init command
• Copying existing repo
• Git user and remote node
• Git Status and rebase
• Review Repo History
• GitHub Cloud Remote Repo
  

MODULE 3: COMMITS, PULL, FETCH AND PUSH 

• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo
  

MODULE 4: TAGGING, BRANCHING AND MERGING 

• Organize code with branches
• Checkout branch
• Merge branches
  

MODULE 5: UNDOING CHANGES 

• Editing Commits
• Commit command Amend flag
• Git reset and revert

MODULE 6: GIT WITH GITHUB AND BITBUCKET 

• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
• Bitbucket Git account

MODULE 1: BIG DATA INTRODUCTION 

• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction
  

MODULE 2: HDFS AND MAP REDUCE 

• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing  stages concepts
• Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort
• Hands-on Map Reduce task
  

MODULE 3: PYSPARK FOUNDATION 

• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs
  

MODULE 4: SPARK SQL and HADOOP HIVE 

• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
• Working with Spark SQL Query Language
  

MODULE 5: MACHINE LEARNING WITH SPARK ML 

• Introduction to MLlib Various ML algorithms supported by MLib
• ML model with Spark ML
• Linear regression
• logistic regression
• Random forest
  

MODULE 6: KAFKA and Spark 

• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Operations
  

MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION 

• What Is Business Intelligence (BI)?
• What Bi Is The Core Of Business Decisions?
• BI Evolution
• Business Intelligence Vs Business Analytics
• Data Driven Decisions With Bi Tools
• The Crisp-Dm Methodology
  

MODULE 2: BI WITH TABLEAU: INTRODUCTION 

• The Tableau Interface
• Tableau Workbook, Sheets And Dashboards
• Filter Shelf, Rows And Columns
• Dimensions And Measures
• Distributing And Publishing
  

MODULE 3 : TABLEAU: CONNECTING TO DATA SOURCE 

• Connecting To Data File , Database Servers
• Managing Fields
• Managing Extracts
• Saving And Publishing Data Sources
• Data Prep With Text And Excel Files
• Join Types With Union
• Cross-Database Joins
• Data Blending
• Connecting To Pdfs
  

MODULE 4 : TABLEAU : BUSINESS INSIGHTS 

• Getting Started With Visual Analytics
• Drill Down And Hierarchies
• Sorting & Grouping
• Creating And Working Sets
• Using The Filter Shelf
• Interactive Filters
• Parameters
• The Formatting Pane
• Trend Lines & Reference Lines
• Forecasting
• Clustering
  

MODULE 5 : DASHBOARDS, STORIES AND PAGES 

• Dashboards And Stories Introduction
• Building A Dashboard
• Dashboard Objects
• Dashboard Formatting
• Dashboard Interactivity Using Actions
• Story Points
• Animation With Pages
  

MODULE 6 : BI WITH POWER-BI 

• Power BI basics
• Basics Visualizations
• Business Insights with Power BI
  

MODULE 1: ARTIFICIAL INTELLIGENCE OVERVIEW 

• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence
• Why Artificial Intelligence Now?
• Ai Terminologies
• Areas Of Artificial Intelligence
• Ai Vs Data Science Vs Machine Learning
  

MODULE 2: DEEP LEARNING INTRODUCTION 

• Deep Neural Network
• Machine Learning vs Deep Learning
• Feature Learning in Deep Networks
• Applications of Deep Learning Networks
  

MODULE 3: TENSORFLOW FOUNDATION 

• TensorFlow Installation and setup
• TensorFlow Structure and  Modules
• Hands-On: ML modeling with TensorFlow
  

MODULE 4: COMPUTER VISION INTRODUCTION 

• Image Basics
• Convolution Neural Network (CNN)
• Image Classification with CNN
• Hands-On: Cat vs Dogs Classification with CNN Network
  

MODULE 5: NATURAL LANGUAGE PROCESSING (NLP) 

• NLP Introduction
• Bag of Words Models
• Word Embedding
• Language Modeling
• Hands-On: BERT  Algorithm
  

MODULE 6: AI ETHICAL ISSUES AND CONCERNS 

• Issues And Concerns Around Ai
• Ai And Ethical Concerns
• Ai And Bias
• Ai: Ethics, Bias, And Trust
  

MODULE 1: NEURAL NETWORKS 

• Structure of neural networks
• Neural network - core concepts
• Feed forward algorithm
• Backpropagation
• Building neural network from scratch using Numpy
  

MODULE 2: IMPLEMENTING DEEP NEURAL NETWORKS 

• Introduction to neural networks with tf2.X
• Simple deep learning model in Keras (tf2.X)
• Building neural network model in TF2.0 for MNIST dataset
  

MODULE 3: DEEP COMPUTER VISION - CNN 

• Convolutional neural networks (CNNs)
• Introduction
• CNNs with Keras
• Transfer learning in CNN
• Style transfer
• Flowers dataset with tf2.X
• Examining x-ray with CNN model
  

MODULE 4 : RECURRENT NEURAL NETWORK 

• RNN introduction
• Sequences with RNNs
• Long short-term memory networks
• LSTM RNNs and GRU
• Examples of RNN applications
  

MODULE 5: NATURAL LANGUAGE PROCESSING (NLP) 

• Natural language processing
• Introduction
• NLP with RNNs
• Creating model
• Transformers and BERT
• State of art NLP and projects
  

MODULE 6: REINFORCEMENT LEARNING 

• Markov decision process
• Fundamental equations in RL
• Model-based method
• Dynamic programming model free methods
  

MODULE 7: DEEP REINFORCEMENT LEARNING 

• Architectures of deep Q learning
• Deep Q learning
• Policy gradient methods
  

MODULE 8: GENERATIVE ADVERSARIAL NETWORK (GAN) 

• Gan introduction
• Core concepts of GAN
• Building GAN model with TensorFlow 2.X
• GAN applications
  

MODULE 9: DEPLOYING DL MODELS IN THE CLOUD (AWS) 

• Amazon web services (AWS)
• AWS SageMaker Overview
• Sage Makers from Data pipeline to deployments
• Deploying deep learning models WS Sage maker
  

Artificial Intelligence refers to the development of computer systems that can perform tasks autonomously, utilizing advanced algorithms and models to analyze data, make predictions, and make decisions. Its goal is to augment human capabilities and solve complex problems across various applications.

The preference between AI and ML depends on the desired area of expertise. AI encompasses a broader range of technologies, while ML focuses on algorithms for machine learning. The choice should be based on personal interests, career goals, and specific applications one wishes to work on.

Pursuing a career as an AI engineer involves obtaining a degree or certification in computer science, AI, or a related field. It is important to gain proficiency in programming languages used in AI, participate in AI research, contribute to open-source projects, and stay updated on emerging AI technologies.

The career prospects for AI engineers are thriving. As organizations recognize the potential of AI, the demand for AI professionals continues to rise. AI engineers can expect diverse projects, collaboration with multidisciplinary teams, and ample opportunities for growth and advancement.

Artificial intelligence refers to the broader field of developing intelligent machines, while machine learning is a specific approach within AI that focuses on training machines to learn from data. Machine learning algorithms enable machines to automatically identify patterns and make predictions.

Gaining practical experience through internships or entry-level positions in AI projects is valuable for individuals without prior experience. Many organizations offer opportunities specifically designed for beginners, allowing them to build skills and gain industry exposure.

A strong educational background in computer science, AI, data science, or related fields is typically essential for a career in AI. Proficiency in programming languages, mathematics, and machine learning principles is also beneficial.

Yes, becoming proficient in Artificial Intelligence is considered challenging due to the interdisciplinary nature of the field, the need to adapt to rapid advancements, and the continuous learning required to stay at the forefront of the industry.

To gain knowledge in Artificial Intelligence in Cuttack, individuals should meet certain prerequisites, such as enrolling in AI courses or degree programs, self-study using online resources, attending workshops or conferences, engaging in practical projects, and gaining hands-on experience in the field.

Incorporating AI into organizations offers advantages such as automation of tasks, improved decision-making, enhanced customer experiences, and innovation. AI can provide valuable insights from data, increase efficiency and productivity, and drive revenue growth.