ARTIFICIAL INTELLIGENCE COURSE IN ITANAGAR

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 simulation of human intelligence in machines that are programmed to think, learn, and problem-solve like humans. It encompasses various technologies and techniques that enable machines to perform tasks that typically require human intelligence, such as speech recognition, decision-making, problem-solving, and visual perception.

The field of Artificial Intelligence (AI) does not have a single credited inventor. It has evolved over time through the contributions of numerous researchers and scientists. Some notable figures in the early development of AI include Alan Turing, John McCarthy, Marvin Minsky, and Allen Newell.

• Virtual assistants like Siri, Google Assistant, and Alexa that use natural language processing and machine learning to understand and respond to user queries.
• Recommendation systems used by platforms like Netflix, Amazon, and Spotify to suggest personalized content based on user preferences and behavior.
• Autonomous vehicles that rely on AI algorithms and sensors to navigate and make real-time decisions on the road.
• Fraud detection systems in the banking industry that use AI to analyze patterns and identify suspicious activities.
• Medical diagnosis systems that leverage AI to analyze patient data and provide insights for accurate disease detection.

The current state of AI technology is rapidly advancing. AI has made significant progress in various domains, including computer vision, natural language processing, robotics, and machine learning. Cutting-edge AI techniques, such as deep learning and reinforcement learning, have enabled breakthroughs in areas like image recognition, speech synthesis, and game-playing AI. However, AI still faces challenges in areas like common-sense reasoning and understanding human emotions.

An AI Engineer Course typically covers the fundamental concepts, tools, and techniques used in developing AI systems. It includes topics such as machine learning, deep learning, data preprocessing, model evaluation, and deployment. Students learn programming languages such as Python, and they gain hands-on experience in implementing AI algorithms and building AI models.

An AI Expert Course is a more advanced program that delves deeper into specialized topics within AI. It covers advanced machine learning techniques, neural networks, natural language processing, computer vision, and other advanced AI algorithms. The course aims to develop expertise in specific AI domains and enables participants to tackle complex AI challenges.

The AI Engineer Course focuses on providing a broad understanding of AI concepts and practical implementation skills. It covers foundational topics and techniques required to build AI models and applications. On the other hand, the AI Expert Course delves into advanced AI topics and specializes in specific AI domains, providing in-depth knowledge and expertise in those areas.

a. Improved efficiency and productivity: AI can automate repetitive tasks, optimize processes, and streamline operations, leading to increased efficiency and productivity.

b. Enhanced decision-making: AI systems can analyze vast amounts of data, extract meaningful insights, and support decision-making processes, leading to more informed and accurate decisions.

c. Personalization and customer experience: AI-powered recommendation systems and chatbots can provide personalized experiences, tailored product recommendations, and responsive customer service.

d. Cost savings: AI technologies can help optimize resource allocation, reduce errors, and minimize waste, resulting in cost savings for businesses.

e. Advanced data analysis: AI algorithms can uncover patterns, correlations, and trends in data, enabling businesses to gain valuable insights for strategic planning and forecasting.

Top companies actively hiring for artificial intelligence roles include tech giants like Google, Microsoft, Amazon, Facebook, IBM, Apple, and NVIDIA. Additionally, companies in various industries such as healthcare, finance, automotive, and e-commerce are also investing in AI talent.

Starting a career in artificial intelligence with no prior experience:

a. Gain foundationalknowledge: Start by learning the basics of AI, including concepts like machine learning, algorithms, and data analysis. Online courses, tutorials, and books can provide a solid foundation.

b. Learn programming: Familiarize yourself with programming languages commonly used in AI, such as Python. Practice coding and implement small AI projects to gain hands-on experience.

c. Explore AI frameworks and tools: Become familiar with popular AI frameworks and libraries like TensorFlow, Keras, and scikit-learn. These tools will aid in developing AI models and applications.

d. Build a portfolio: Create a portfolio of AI projects to showcase your skills and practical experience. This can include implementing machine learning algorithms, developing chatbots, or working on data analysis projects.

e. Join AI communities: Engage with online AI communities, forums, and social media groups to connect with professionals and stay updated on the latest trends and opportunities in the field.

f. Seek internships or entry-level positions: Look for internships or entry-level positions in AI-related roles to gain industry experience and further develop your skills.