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Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN ITANAGAR

Live Virtual

Instructor Led Live Online

154,000
92,055

  • IABAC® & DMC Certification
  • 9-Month | 780 Learning Hours
  • 100-Hour Live Online Training
  • 10 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

92,000
55,005

  • Self Learning + Live Mentoring
  • IABAC® & DMC Certification
  • 1 Year Access To Elearning
  • 10 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Learner assistance and support

Classroom

In - Person Classroom Training

154,000
105,355

  • IABAC® & DMC Certification
  • 9-Month | 780 Learning Hours
  • 100-Hour Classroom Sessions
  • 10 Capstone & 1 Client Project
  • Cloud Lab Access
  • Internship + Job Assistance

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UPCOMING ARTIFICIAL INTELLIGENCE ONLINE CLASSES IN ITANAGAR

BEST ARTIFICIAL INTELLIGENCE CERTIFICATIONS

The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

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WHY DATAMITES INSTITUTE FOR ARTIFICIAL INTELLIGENCE COURSE

Why DataMites Infographic

SYLLABUS OF AI 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

OFFERED ARTIFICIAL INTELLIGENCE COURSES IN ITANAGAR

ARTIFICIAL INTELLIGENCE TRAINING REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING IN ITANAGAR

Artificial Intelligence (AI) is not just a buzzword; it's a game-changer that is reshaping industries and transforming the way we live. Did you know that the global AI market is projected to reach a staggering $190 billion by 2025? That's because AI has the potential to revolutionize sectors like healthcare, finance, manufacturing, and more. From personalized medical diagnoses to smart virtual assistants, AI is driving innovation and creating unprecedented opportunities for businesses and individuals alike.

In Itanagar, aspiring individuals can explore the world of Artificial Intelligence through DataMites comprehensive Artificial Intelligence Courses in Itanagar. The course spans over 11 months, comprising 780 learning hours. It offers a blend of theoretical knowledge and practical hands-on experience to equip students with a strong foundation in AI. The training includes 100 hours of live online/classroom sessions, allowing learners to interact with experienced instructors. Additionally, participants engage in 10 Capstone projects and one client project to apply their skills in real-world scenarios. The Artificial Intelligence Training in Itanagar also provides a 365-day Flexi Pass and Cloud Lab access, enabling flexible learning at one's own pace. For those preferring offline learning, DataMites offers on-demand AI courses offline in Itanagar as well.

DataMites offers a range of specialized courses in Artificial Intelligence in Itanagar. Aspiring professionals can choose from programs like Artificial Intelligence Engineer, Artificial Intelligence Expert, Certified NLP Expert, Artificial Intelligence Foundation, and Artificial Intelligence for Managers. These courses cater to different skill levels and career aspirations, allowing individuals to delve into specific AI domains based on their interests.

Here are the reasons to choose DataMites for Artificial Intelligence training in Itanagar

  • Firstly, the courses are conducted by industry expert Ashok Veda and a team of highly qualified faculty members. 

  • Secondly, DataMites offers a comprehensive course curriculum that covers all essential concepts and techniques in AI. 

  • Thirdly, the training program provides global certifications recognized by esteemed organizations like IABAC, NASSCOM FutureSkills Prime, and JainX

  • Fourthly, DataMites offers flexible learning options including online artificial intelligence courses in Itanagar and artificial intelligence classroom training in Itanagar, allowing students to balance their training with other commitments. 

  • Fifthly, the course emphasizes practical learning by involving projects with real-world data, enabling students to gain hands-on experience.

  • Sixthly, the program provides artificial intelligence internship opportunities to apply AI skills in practical settings. 

  • Seventhly, DataMites offers artificial intelligence courses with placement assistance and job references to support students in their career progression. 

  • Eighthly, learners receive hardcopy learning materials and books to supplement their online resources. 

  • Ninthly, DataMites nurtures an exclusive learning community where students can connect, collaborate, and learn from their peers. 

  • Lastly, the courses are affordable, and DataMites also offers scholarships to deserving candidates.

Itanagar, the capital city of Arunachal Pradesh, is a vibrant and culturally rich location. Situated in the foothills of the eastern Himalayas, Itanagar offers a picturesque setting for learning and exploring the field of Artificial Intelligence. The city's serene environment, coupled with its warm and welcoming people, provides an ideal atmosphere for personal and professional growth. Additionally, Itanagar's strategic location in the northeastern region of India opens up opportunities to engage with diverse industries and organizations that are increasingly adopting AI technology. Students pursuing an Artificial Intelligence certification in Itanagar can not only benefit from the course but also immerse themselves in the unique charm of the city and its surroundings.

Along with artificial intelligence courses, DataMites also provides machine learning, deep learning, python training, IoT, data engineer, mlops, tableau, data mining, python for data science, data analytics and data science courses in Itanagar.

ABOUT ARTIFICIAL INTELLIGENCE COURSE IN ITANAGAR

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.

Preparation for AI job interviews and technical assessments:

a. Review foundational AI concepts: Refresh your knowledge of key AI concepts, algorithms, and techniques.

b. Practice coding: Solve coding problems and challenges related to AI algorithms and data manipulation. Websites like LeetCode and HackerRank offer coding practice opportunities.

c. Study real-world use cases: Familiarize yourself with practical AI applications and case studies in different industries. Understand how AI is being implemented and the challenges faced.

d. Be prepared for technical questions: Expect questions related to algorithms, data structures, machine learning models, and programming languages used in AI.

e. Showcase your projects: Be ready to discuss and demonstrate your AI projects during the interview process.

f. Stay updated on the latest trends: Stay informed about recent advancements and trends in AI through research papers, conferences, and industry publications.

AI is expected to have a significant impact on the job market, creating new roles and transforming existing ones. The demand for AI professionals, including AI engineers, data scientists, and AI researchers, is expected to continue growing. Industries such as healthcare, finance, cybersecurity, and autonomous systems are likely to see increased AI adoption, leading to more job opportunities in these sectors.

a. Assess your transferable skills: Identify skills from your current field that can be applied to AI, such as data analysis, problem-solving, programming, or domain knowledge.

b. Fill knowledge gaps: Take relevant courses or pursue certifications in AI to acquire the necessary technical knowledge and skills.

c. Build a network: Connect with professionals in the AI field through networking events, conferences, and online communities. Seek mentorship or guidance from experienced AI practitioners.

d. Leverage existing experience: Highlight how your previous field intersects with AI and how your skills can contribute to solving AI-related challenges.

e. Gain practical experience: Work on AI projects, collaborate on open-source projects, or participate in Kaggle competitions to gain hands-on experience and build your portfolio.

f. Stay updated: Continuously learn and stay updated on the latest developments in AI to remain competitive in the field.

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FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN ITANAGAR

The average salary for an Artificial Intelligence Engineer in Itanagar can vary based on factors such as the individual's experience, skills, industry, and the specific organization they work for. It is difficult to provide an exact figure without specific data for Itanagar. The salary range for AI Engineers in India typically varies from ?3.0 Lakhs to ?20.0 Lakhs, with an average annual salary of ?7.0 Lakhs, according to AmbitionBox.

Learning Artificial Intelligence in Itanagar, like any other location, is significant due to the growing importance and widespread applications of AI across various industries. By acquiring knowledge in AI, individuals in Itanagar can stay relevant and contribute to the technological advancements happening globally. AI has the potential to transform businesses, improve efficiency, and solve complex problems, and learning AI can open up new career opportunities in fields such as data science, machine learning, robotics, and automation.

DataMites offers various AI courses:

AI Engineer: 11-month program covering AI fundamentals, machine learning, deep learning, computer vision, NLP, and model deployment.

AI Expert: 3-month course covering AI foundations, ML, deep learning, computer vision, NLP, reinforcement learning, and GANs.

AI for Managers: 1-month course for managers to understand AI capabilities and apply them in decision-making.

Certified NLP Expert: 3-month course focusing on NLP skills and real-world applications.

AI Foundation: 2-month course introducing AI concepts, applications, and use cases for beginners.

The duration of the Artificial Intelligence course in Itanagar offered by DataMites may vary depending on the specific course chosen, ranging from one month to one year. The training sessions are designed to be flexible and accommodate different schedules, with options available on weekdays and weekends.

Acquiring knowledge in the field of Artificial Intelligence (AI) can be done through various avenues:

a) Self-Study: This involves exploring online resources such as textbooks, research papers, tutorials, and online courses. There are many platforms like Coursera, edX, and Udacity that offer AI courses.

b) Online Courses: Enrolling in online AI courses from reputable institutions or e-learning platforms can provide structured learning materials, assignments, and access to instructors or forums for clarification.

c) Formal Education: Pursuing a degree or diploma program in AI or a related field from a recognized university can provide in-depth knowledge and practical experience.

d) Workshops and Conferences: Attending workshops, conferences, and industry events can provide insights into the latest developments, research, and trends in AI.

e) Hands-on Experience: Engaging in practical projects, participating in Kaggle competitions, or working on real-world AI applications can help gain practical knowledge.

The AI Engineer Course in Itanagar has the purpose of equipping students with the skills and knowledge required to become proficient AI engineers. The course covers various aspects of AI, including machine learning, deep learning, natural language processing, computer vision, and AI deployment techniques. It aims to provide hands-on experience through practical projects and case studies, enabling students to build AI models and deploy them in real-world scenarios.

The Certified NLP Expert course offered by DataMites in Itanagar focuses on Natural Language Processing (NLP), which is a subfield of AI that deals with the interaction between computers and human language. The course content includes fundamental concepts of NLP, text preprocessing, sentiment analysis, named entity recognition, topic modeling, language generation, and neural network-based NLP models. The course aims to train individuals in NLP techniques and applications, enabling them to solve real-world problems using NLP algorithms and models.

The Artificial Intelligence for Managers Course in Itanagar provided by DataMites is designed for professionals in managerial roles who want to understand the fundamentals of AI and its impact on business strategies. The course covers topics such as AI basics, machine learning, deep learning, natural language processing, computer vision, AI implementation challenges, ethical considerations, and AI project management. It aims to provide managers with the necessary knowledge to make informed decisions related to AI adoption, implementation, and leveraging AI technologies for business growth.

The AI Foundation Course in Itanagar at DataMites is a comprehensive introductory course that covers the basics of AI, machine learning, and deep learning. The course content includes an overview of AI, supervised and unsupervised learning, neural networks, deep learning algorithms, model evaluation, and deployment techniques. It aims to provide participants with a solid foundation in AI concepts and techniques, preparing them for further specialization or practical AI projects.

DataMites is considered a preferred choice for online AI training in Itanagar due to the following factors:

a) Comprehensive Curriculum: DataMites offers courses that cover a wide range of AI concepts, techniques, and applications, ensuring a well-rounded learning experience.

b) Hands-on Approach: The training programs emphasize practical projects and case studies, allowing participants to gain hands-on experience in AI implementation.

c) Experienced Instructors: DataMites has a team of experienced instructors who have expertise in AI and related fields. They provide guidance and support throughout the training.

d) Flexibility: DataMites offers online training, allowing participants to learn at their own pace and from anywhere with an internet connection.

e) Placement Support: DataMites provides placement assistance and support to help participants kickstart or advance their careers in AI.

The fee for the Artificial Intelligence Training program in Itanagar at DataMites may vary depending on the specific course and the duration of the program. However generally, the artificial intelligence course fee in Itanagar can vary from INR  60,795 to INR 154,000.

Yes, DataMites offers classroom training for Artificial Intelligence. Apart from online training, they also provide instructor-led classroom training at various locations, including Itanagar. This option allows participants to have face-to-face interactions with instructors and fellow learners.

DataMites offers various certifications, including those from IABAC (International Association of Business Analytics Certifications), JAINx, and NASSCOM FutureSkills Prime. These certifications are recognized and respected in the industry and can enhance your credibility and marketability in the field of Artificial Intelligence. By completing the Artificial Intelligence training at DataMites, you may have the opportunity to earn certifications from these reputable organizations, further validating your skills and knowledge in the AI domain.

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.

No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.

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