ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN AGARTALA

Live Virtual

Instructor Led Live Online

154,000
101,745

  • 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
60,795

  • 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
116,445

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

ARE YOU LOOKING TO UPSKILL YOUR TEAM ?

Enquire Now

UPCOMING ARTIFICIAL INTELLIGENCE ONLINE CLASSES IN AGARTALA

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.

images not display images not display

WHY DATAMITES INSTITUTE FOR ARTIFICIAL INTELLIGENCE COURSE

Why DataMites Infographic

SYLLABUS OF AI COURSE IN AGARTALA

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 AGARTALA

ARTIFICIAL INTELLIGENCE TRAINING REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING IN AGARTALA

Imagine a world where machines possess the intelligence to understand, learn, and even replicate human thought processes. This is the realm of Artificial Intelligence (AI), a field that has captivated our imagination and sparked a technological revolution. While AI may seem like a futuristic concept, its origins can be traced back to ancient civilizations. Fast forward to the present, and AI has evolved into a dynamic and rapidly advancing field that holds immense promise for solving complex problems, enhancing efficiency, and transforming industries across the globe. From self-driving cars to voice-activated personal assistants, AI is not only revolutionizing the way we live but also opening doors to a future where machines and humans collaborate to shape a truly intelligent world.

DataMites offers an extensive Artificial Intelligence Course in Agartala, designed to provide participants with a comprehensive understanding of AI concepts and applications. The course spans 11 months and consists of 780 learning hours, ensuring a deep dive into the subject matter. The training includes a 100-hour live online/classroom component, allowing students to interact with experienced instructors and engage in real-time discussions. Participants also work on 10 capstone projects and a client project to apply their newfound knowledge in practical scenarios.

DataMites also offers ON DEMAND artificial intelligence offline courses in Agartala, providing flexibility for learners who prefer in-person training. These courses cover a range of AI specializations, including Artificial Intelligence Engineering, Artificial Intelligence Expertise, Certified Natural Language Processing (NLP) Expertise, Artificial Intelligence Foundations, and Artificial Intelligence for Managers.

There are numerous reasons to choose DataMites for Artificial Intelligence Training in Agartala

  • The institute boasts a team of experienced faculty members led by Ashok Veda, who bring industry expertise and practical insights to the classroom. 

  • The course curriculum is comprehensive, ensuring a well-rounded education in AI. DataMites provides global certifications from renowned organizations such as IABAC, NASSCOM FutureSkills Prime, and JainX, enhancing the value and recognition of the training. 

  • The institute offers flexible learning options artificial intelligence training online in Agartala and ON DEMAND artificail intelligence offline classes in Agartala, allowing students to access course materials and complete assignments at their own pace. 

  • Real-world projects incorporating actual data provide hands-on experience and practical skills. Artificial Intelligence Internship opportunities further enhance participants' industry exposure, while artificial intelligence courses with placement assistance and job references help kickstart their careers in AI. 

  • Learners receive hardcopy learning materials and books to aid their studies and become part of DataMites' exclusive learning community, facilitating collaboration and networking. 

  • The institute also offers affordable pricing options and scholarships, making AI education accessible to a wider audience.

Agartala, the capital city of Tripura, is a vibrant and culturally rich destination in Northeast India. It is known for its historical significance, beautiful palaces, and lush green landscapes. Agartala provides a conducive environment for learning, with a growing focus on technology and education. As the city embraces innovation and digital transformation, pursuing Artificial Intelligence Certification in Agartala offers exciting opportunities to be part of the region's technological growth and development.

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 Agartala.

ABOUT ARTIFICIAL INTELLIGENCE COURSE IN AGARTALA

Artificial Intelligence, commonly known as AI, holds great significance in the modern world. It involves the development of intelligent systems and algorithms that can mimic human intelligence and perform tasks with remarkable precision. AI has the potential to revolutionize various industries and enhance efficiency, productivity, and decision-making processes.

Artificial Intelligence is extensively employed in a wide range of sectors and domains. Some common areas where AI finds application include healthcare, finance, manufacturing, transportation, cybersecurity, customer service, marketing, and robotics. Its versatility allows it to address complex challenges and deliver innovative solutions across multiple industries.

Artificial Intelligence has become an integral part of our everyday lives. Examples include virtual voice assistants like Amazon's Alexa or Apple's Siri, personalized recommendations on streaming platforms like Netflix or Spotify, facial recognition systems in smartphones, autonomous vehicles, AI-powered chatbots used in customer support, and smart home devices that adapt to user preferences. These applications showcase the transformative impact of AI on our daily experiences.

To acquire expertise in Artificial Intelligence in Agartala, certain prerequisites can be beneficial. A foundational understanding of programming concepts, proficiency in mathematics and statistics, familiarity with data structures and algorithms, and a strong analytical mindset are essential. Additionally, curiosity, dedication to continuous learning, and problem-solving skills are advantageous for effectively delving into the field.

A career in Artificial Intelligence often demands a solid educational background. A bachelor's or master's degree in computer science, data science, or a related field is typically preferred by employers. Courses focusing on AI, machine learning, statistics, and programming languages such as Python or R are valuable for gaining the necessary knowledge and skills.

The future prospects for Artificial Intelligence in the job market are highly promising. As AI continues to advance and permeate various industries, the demand for skilled professionals is expected to soar. Job opportunities in AI-related roles, such as AI engineers, data scientists, and AI consultants, are projected to witness significant growth, offering exciting career paths and attractive remuneration.

Transitioning into a career in Artificial Intelligence from a different field is achievable with strategic steps. These may include acquiring relevant knowledge and skills through online courses, boot camps, or specialized AI programs, building a strong portfolio showcasing AI projects, participating in AI competitions and hackathons, networking with professionals in the field, and pursuing internships or freelance projects to gain practical experience.

While Artificial Intelligence is a broader field encompassing the development of intelligent machines, Machine Learning is a subset of AI. Machine Learning focuses on algorithms and models that enable systems to learn from data and improve their performance over time. In essence, Machine Learning is an essential component of Artificial Intelligence, as it enables machines to analyze and interpret data without explicit programming.

The field of Artificial Intelligence offers a diverse range of job roles. Some common job titles include AI engineer, machine learning engineer, data scientist, AI researcher, AI consultant, AI project manager, and AI ethicist. These roles involve responsibilities such as developing AI models, analyzing data, conducting research, implementing AI solutions, and addressing ethical considerations in AI development and deployment.

Undoubtedly, a career in Artificial Intelligence holds immense promise for the future. The continuous advancement of AI technologies, the increasing integration of AI in various industries, and the growing demand for AI professionals indicate a thriving job market. AI specialists have the opportunity to shape the future, solve complex problems, and contribute to groundbreaking innovations that positively impact society.

To initiate a career in Artificial Intelligence, individuals can follow a systematic approach. It includes gaining a strong foundation in computer science, mathematics, and statistics, acquiring in-depth knowledge of AI concepts and algorithms through relevant courses or online resources, developing practical skills through hands-on projects, collaborating with peers in the field, and staying updated with the latest advancements through research papers, conferences, and AI communities. Internships and entry-level positions can provide valuable industry experience and pave the way for a successful career in AI.

View more

FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN AGARTALA

Individuals can gain knowledge in Artificial Intelligence through various methods, including self-study using online resources, textbooks, research papers, and tutorials. They can also enroll in AI courses and training programs, pursue degrees or diplomas in AI or related fields, attend workshops and conferences, and engage in practical projects and competitions to gain hands-on experience.

The duration of the Artificial Intelligence course offered by DataMites in Agartala can vary depending on the specific course chosen. The course offers flexible durations ranging from one month to one year, providing options that accommodate different schedules and preferences. DataMites conducts training sessions on both weekdays and weekends, allowing participants to select a schedule that suits their availability and learning needs.

DataMites offers a range of certifications in Artificial Intelligence, including certifications for AI Engineers, Certified NLP Experts, AI Experts, AI Foundations, and AI for Managers.

DataMites is the preferred choice for online Artificial Intelligence training in Agartala for several reasons. These include having experienced trainers who are industry professionals, offering a comprehensive course curriculum that covers various AI topics, providing hands-on learning opportunities through practical projects, offering flexible batch options and schedules, providing placement assistance, and allowing participants to obtain certifications upon completing the training.

The Artificial Intelligence for Managers Course offered by DataMites in Agartala covers essential topics such as AI basics, machine learning, deep learning, natural language processing, computer vision, AI implementation challenges, ethical considerations, and AI project management. This course equips managers with the necessary knowledge to make informed decisions about AI adoption, implementation, and leveraging AI technologies for business growth.

DataMites' AI Foundation Course in Agartala provides a comprehensive introduction to AI and covers fundamental subjects, including AI basics, machine learning, and deep learning. The course content encompasses an overview of AI, supervised and unsupervised learning, neural networks, deep learning algorithms, model evaluation, and deployment techniques, establishing a strong foundation in AI concepts and techniques.

The fee for DataMites' Artificial Intelligence Training program in Agartala can vary depending on the specific course and duration chosen. Generally, the fee ranges from INR 60,795 to INR 154,000, offering flexibility for participants to choose a program that suits their requirements.

DataMites provides both online and classroom training options for Artificial Intelligence in Agartala, giving participants the flexibility to choose the mode of training that best suits their preferences and needs.

The Flexi-Pass feature offered by DataMites in Agartala allows participants to attend training sessions at their convenience. It offers flexibility in scheduling by providing multiple batch options, enabling individuals to balance their learning with other commitments and choose classes based on their availability and preference.

The AI Engineer Course offered by DataMites in Agartala aims to equip individuals with the necessary skills and knowledge 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 prepares participants to build and deploy AI models in real-world scenarios.

Yes, DataMites may offer the option to attend a demo class before enrolling in the Artificial Intelligence course in Agartala. This allows individuals to experience the training approach, course content, and teaching style before making a decision.

Yes, participants who complete a training program with DataMites in Agartala can obtain a Course Completion Certificate. This certificate acknowledges their successful completion of the program and serves as a valuable addition to their professional profile.

The average salary for an Artificial Intelligence Engineer in Agartala can vary depending on factors such as experience, skills, and the specific organization. Salaries in the field of Artificial Intelligence are generally competitive, with a range of entry-level positions to higher-paying roles based on expertise and experience. According to AmbitionBox, the average annual salary for AI Engineers in India is ?7.0 Lakhs, with a salary range typically spanning from ?3.0 Lakhs to ?20.0 Lakhs.

Yes, DataMites provides Artificial Intelligence Courses in Agartala that include placement assistance. They support participants in connecting with job opportunities in the field of Artificial Intelligence through resume building, interview preparation, and job placement guidance.

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.

View more

OTHER AI TRAINING CITIES IN INDIA

Global ARTIFICIAL INTELLIGENCE COURSES Countries

popular career ORIENTED COURSES

DATAMITES POPULAR COURSES


HELPFUL RESOURCES - DataMites Official Blog