ARTIFICIAL INTELLIGENCE COURSE IN ITANAGAR

MODULE 1 : ARTIFICIAL INTELLIGENCE OVERVIEW 

• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence
• Why Artificial Intelligence Now?
• 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

MODULE3 : TENSORFLOW FOUNDATION

• 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
• 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 : PYTHON BASICS 

 • Introduction of python
 • Installation of Python and IDE
 • Python Variables
 • Python basic data types
 • Number & Booleans, strings
 • Arithmetic Operators
 • Comparison Operators
 • Assignment Operators

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
 • Basics of List
 • List: Object, methods
 • Tuple: Object, methods
 • Sets: Object, methods
 • Dictionary: Object, methods

MODULE 4 : PYTHON FUNCTIONS 

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

MODULE 1 : OVERVIEW OF STATISTICS 

 • Introduction to 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
 • Types of Sampling
 • Simple Random Sampling
 • Stratified Random Sampling
 • Cluster Random Sampling
 • Systematic Random Sampling
 • Multi stage Sampling
 • 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 & Properties
 • Z Value / Standard Value
 • Empherical Rule and Outliers
 • Central Limit Theorem
 • Normality Testing
 • Skewness & Kurtosis
 • Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
 • Covariance & Correlation

MODULE 4 : HYPOTHESIS TESTING 

 • Hypothesis Testing Introduction
 • P- Value, Critical Region
 • Types of Hypothesis Testing
 • Hypothesis Testing Errors : Type I And Type II
 • Two Sample Independent T-test
 • Two Sample Relation T-test
 • One Way Anova Test
 • Application of Hypothesis testing

MODULE 1: MACHINE LEARNING INTRODUCTION 

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

MODULE 2: PYTHON NUMPY  PACKAGE 

• Introduction to Numpy Package
 • Array as Data Structure
 • Core Numpy functions
 • Matrix Operations, Broadcasting in Arrays

MODULE 3: PYTHON PANDAS PACKAGE

 • Introduction to Pandas package
 • Series in Pandas
 • Data Frame in Pandas
 • File Reading in Pandas
 • Data munging with Pandas

MODULE 4:  VISUALIZATION WITH PYTHON - Matplotlib 

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

MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN

 • Seaborn: Basic Plot
 • Advanced Python Data Visualizations

MODULE 6: ML ALGO: LINEAR REGRESSION

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

MODULE 7: ML ALGO: LOGISTIC REGRESSION 

 • Introduction to Logistic Regression
 • How it works: Classification & Sigmoid Curve
 • Modeling and Evaluation 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 9: ML ALGO: KNN

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

MODULE 1:  FEATURE ENGINEERING 

 • Introduction to Feature Engineering
 • Feature Engineering Techniques: Encoding, Scaling, Data Transformation
 • Handling Missing values, handling outliers
 • Creation of Pipeline
 • Use case for feature engineering

MODULE 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

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

MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)

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

MODULE 4: ML ALGO: DECISION TREE 

 • Introduction to Decision Tree & Random Forest
 • How it works
 • Modeling and Evaluation in Python

MODULE 5: ENSEMBLE TECHNIQUES - BAGGING

 • Introduction to Ensemble technique 
 • Bagging and How it works
 • Modeling and Evaluation in Python

MODULE 6: ML ALGO: NAÏVE BAYES

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

MODULE 7:  GRADIENT BOOSTING, XGBOOST 

 • Introduction to Boosting and XGBoost
 • How it works?
 • Modeling and Evaluation of in Python

MODULE 1: TIME SERIES FORECASTING - ARIMA 

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

MODULE 2:  SENTIMENT ANALYSIS

 • Introduction to Sentiment Analysis
 • NLTK Package
 • Case study: Sentiment Analysis on Movie Reviews

MODULE 3:  REGULAR EXPRESSIONS WITH PYTHON 

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

MODULE 4: ML MODEL DEPLOYMENT WITH FLASK 

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

MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL 

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

MODULE 6:  AWS CLOUD FOR DATA SCIENCE

 • Introduction of cloud
 • Difference between GCC, Azure,AWS
 • AWS Service ( EC2 instance)

MODULE 7: AZURE FOR DATA SCIENCE

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

MODULE 8: INTRODUCTION TO DEEP LEARNING

 • Introduction to Artificial Neural Network, Architecture
 • Artificial Neural Network in Python
 • Introduction to Convolutional Neural Network, Architecture
 • Convolutional Neural Network in Python

MODULE 1: DATABASE INTRODUCTION

 • DATABASE Overview
 • Key concepts of database management
 • Relational Database Management System
 • CRUD operations

 MODULE 2: SQL BASICS

 • Introduction to Databases
 • Introduction to SQL
 • SQL Commands
 • MY SQL workbench installation

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
• Windows functions: Over, Partition , Rank 

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

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
 • Git Essentials: Copy & User Setup
 • Mastering Git and GitHub

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

MODULE 5: GIT WITH GITHUB AND BITBUCKET 

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

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
  

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
  

MODULE 1: TABLEAU FUNDAMENTALS 

 • Introduction to Business Intelligence & Introduction to Tableau
 • Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
 • Bar chart, Tree Map, Line Chart
 • Area chart, Combination Charts, Map
 • Dashboards creation, Quick Filters
 • Create Table Calculations
 • Create Calculated Fields
 • Create Custom Hierarchies

MODULE 2: POWER-BI BASICS 

 • Power BI Introduction 
 • Basics Visualizations
 • Dashboard Creation
 • Basic Data Cleaning
 • Basic DAX FUNCTION

MODULE 3 : DATA TRANSFORMATION TECHNIQUES

 • Exploring Query Editor
 • Data Cleansing and Manipulation:
 • Creating Our Initial Project File
 • Connecting to Our Data Source
 • Editing Rows
 • Changing Data Types
 • Replacing Values

MODULE 4 :  CONNECTING TO VARIOUS DATA SOURCES 

 • Connecting to a CSV File
 • Connecting to a Webpage
 • Extracting Characters
 • Splitting and Merging Columns
 • Creating Conditional Columns
 • Creating Columns from Examples
 • Create Data Model

MODULE 1: NEURAL NETWORKS 

 • Structure of neural networks
 • Neural network - core concepts(Weight initialization)
 • Neural network - core concepts(Optimizer)
 • Neural network - core concepts(Need of activation)
 • Neural network - core concepts(MSE & RMSE)
 • Feed forward algorithm
 • Backpropagation

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 - IMAGE RECOGNITION

• Convolutional neural networks (CNNs)
• CNNs with Keras-part1
• CNNs with Keras-part2
• Transfer learning in CNN
• Flowers dataset with tf2.X(part-1)
• Flowers dataset with tf2.X(part-2)
• Examining x-ray with CNN model

MODULE 4 : DEEP COMPUTER VISION - OBJECT DETECTION

 • What is Object detection
 • Methods of Object Detections
 • Metrics of Object detection
 • Bounding Box regression
 • labelimg
 • RCNN
 • Fast RCNN
 • Faster RCNN
 • SSD
 • YOLO Implementation
 • Object detection using cv2

MODULE 5: RECURRENT NEURAL NETWORK 

• RNN introduction
• Sequences with RNNs
• Long short-term memory networks(part 1)
• Long short-term memory networks(part 2)
• Bi-directional RNN and LSTM
• Examples of RNN applications

MODULE 6: NATURAL LANGUAGE PROCESSING (NLP)

• Introduction to Natural language processing
• Working with Text file
• Working with pdf file
• Introduction to regex
• Regex part 1
• Regex part 2
• Word Embedding
• RNN model creation
• Transformers and BERT
• Introduction to GPT (Generative Pre-trained Transformer)
• State of art NLP and projects

MODULE 7: PROMPT ENGINEERING

• Introduction to Prompt Engineering
• Understanding the Role of Prompts in AI Systems
• Design Principles for Effective Prompts
• Techniques for Generating and Optimizing Prompts
• Applications of Prompt Engineering in Natural Language Processing

MODULE 8: REINFORCEMENT LEARNING

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

MODULE 9: DEEP REINFORCEMENT LEARNING

• Architectures of deep Q learning
• Deep Q learning
• Reinforcement Learning Projects with OpenAI Gym

MODULE 10: Gen AI

• Gan introduction, Core Concepts, and Applications
• Core concepts of GAN
• GAN applications
• Building GAN model with TensorFlow 2.X
• Introduction to GPT (Generative Pre-trained Transformer)
• Building a Question answer bot with the models on Hugging Face

MODULE 11: Gen AI

• Introduction to Autoencoder
• Basic Structure and Components of Autoencoders
• Types of Autoencoders: Vanilla, Denoising, Variational, Sparse, and Convolutional Autoencoders
• Training Autoencoders: Loss Functions, Optimization Techniques
• Applications of Autoencoders: Dimensionality Reduction, Anomaly Detection, Image

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.