ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN ROURKELA

Live Virtual

Instructor Led Live Online

154,000
96,900

  • 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
57,900

  • 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
110,900

  • 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 ROURKELA

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 ROURKELA

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 ROURKELA

ARTIFICIAL INTELLIGENCE TRAINING REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING IN ROURKELA

The global AI software market is on a meteoric rise, with projections by estimating its value to soar to a staggering $126 billion by 2025. This exponential growth signifies the ever-increasing investment and belief in AI technologies worldwide. Prepare to witness AI's transformative power as it reshapes industries, drives innovation, and unlocks new opportunities for businesses and individuals alike. Stay ahead of the curve, harness the potential of AI, and embark on a journey where the boundaries of what's possible are continuously pushed and redefined.

DataMites offers an extensive Artificial Intelligence Course in Rourkela, designed to equip learners with the skills and knowledge required to excel in the AI field. The course spans 11 months, consisting of 780 learning hours, including 100 hours of live online/classroom training. It provides hands-on experience through 10 capstone projects and 1 client project, allowing students to apply their knowledge in real-world scenarios. With a 365-day Flexi Pass and access to the Cloud Lab, learners have the flexibility to study at their own pace and gain practical expertise.

DataMites also provides on demand artificial intelligence offline courses in Rourkela, catering to the specific needs and preferences of learners. These courses include Artificial Intelligence Engineer, Artificial Intelligence Expert, Certified NLP Expert, Artificial Intelligence Foundation, and Artificial Intelligence for Managers, offering a diverse range of programs to suit various career aspirations.

There are reasons to choose DataMites for Artificial Intelligence Training in Rourkela

  • The course is led by experienced faculty, including renowned AI expert Ashok Veda, ensuring high-quality instruction and mentorship. 

  • The curriculum is comprehensive, covering a wide range of AI concepts and techniques. Upon completion of the course, participants receive globally recognized certifications such as IABAC, NASSCOM FutureSkills Prime, and JainX, enhancing their professional credentials and employability.

  • DataMites offers flexible learning options like online artificial intelligence training in Rourkela and ON DEMAND artificial intelligence offline courses in Rourkela, allowing students to work on projects using real-world data, gaining practical skills and experience. 

  • An artificial intelligence internship opportunity is also provided, enabling learners to apply their knowledge in industry settings. Artificial intelligence course with placement assistance and job references are available to support participants in their career endeavors. 

  • Additionally, hardcopy learning materials and books are provided, aiding in the learning process. By joining the DataMites Exclusive Learning Community, learners gain access to a network of like-minded individuals and industry experts, fostering collaboration and knowledge sharing.

  • DataMites ensures affordability through competitive pricing and scholarship opportunities, making quality AI education accessible to a wide range of learners. 

  • By choosing DataMites for AI training in Rourkela, participants gain a competitive edge in the field and can make significant contributions to the evolving world of Artificial Intelligence.

Rourkela, located in the state of Odisha, India, is a city known for its strong industrial and educational presence. It is home to numerous educational institutions, research organizations, and technological enterprises. Rourkela is an emerging technology hub, offering a conducive environment for learning and innovation. The city's industrial landscape provides ample opportunities for AI professionals, with industries such as manufacturing, steel, energy, and technology driving economic growth. By pursuing an Artificial Intelligence Certification in Rourkela, learners can tap into this vibrant ecosystem and contribute to the city's technological advancements.

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 Rourkela.

ABOUT ARTIFICIAL INTELLIGENCE COURSE IN ROURKELA

Artificial Intelligence (AI) refers to the development of intelligent machines that can perform tasks requiring human intelligence. It involves creating algorithms and systems that can learn, reason, perceive, and make decisions.

Individuals can embark on a career as an AI engineer by acquiring a strong foundation in mathematics, computer science, and programming. They can then further enhance their knowledge and skills by studying AI concepts, algorithms, and technologies. Practical experience through internships or personal projects can also greatly contribute to their career development.

The choice between AI and ML depends on the individual's inclination and career aspirations. AI encompasses various aspects of creating intelligent machines, including ML as a crucial component. If you are interested in developing advanced systems that exhibit human-like intelligence, AI provides a broader canvas. However, if your passion lies in building algorithms and models that learn from data, ML offers a more focused path. It is essential to explore both fields to make an informed decision based on your interests and long-term goals.

The career prospects for AI engineers are highly promising. With the increasing adoption of AI technologies across industries, there is a growing demand for skilled professionals who can develop intelligent systems. AI engineers have opportunities to work on cutting-edge projects, solve complex problems, and contribute to technological advancements, making it an exciting and rewarding career path.

One can kickstart a career in artificial intelligence without prior experience by participating in AI-related competitions and projects. Joining open-source projects or contributing to AI communities allows individuals to collaborate with experienced professionals, learn from their expertise, and showcase their skills to potential employers.

Artificial intelligence (AI) is a broader concept that encompasses the development of intelligent machines capable of simulating human intelligence. It involves creating algorithms and systems that can learn, reason, perceive, and make decisions. On the other hand, machine learning (ML) is a subset of AI that focuses on enabling machines to learn from data and make predictions or decisions without being explicitly programmed.

To pursue a career in artificial intelligence, it is advantageous to have a solid educational foundation in computer science, AI, data science, or a related field. Employers often value candidates with a bachelor's or master's degree in these disciplines. Additionally, possessing a strong understanding of programming languages, mathematics, and machine learning concepts can greatly enhance one's prospects in the field.

To acquire knowledge in Artificial Intelligence in Rourkela, there are specific prerequisites that individuals need to fulfill.

Mastering Artificial Intelligence poses challenges as it involves staying updated with evolving technologies, experimenting with large datasets, and solving complex real-world problems.

The implementation of AI contributes to organizational growth and development by enhancing efficiency, improving decision-making, personalizing customer experiences, increasing productivity, realizing cost savings, driving business innovation, enabling scalability and adaptability, and enhancing risk management. These benefits result in streamlined operations, improved customer satisfaction, optimized resource allocation, increased competitiveness, and the ability to capitalize on emerging opportunities, ultimately fostering growth and development for organizations.

To prepare for AI job interviews and technical assessments, one can:

  • Review fundamental AI concepts, algorithms, and technologies.
  • Practice implementing AI algorithms and models using programming languages like Python or R.
  • Solve AI-related coding problems and challenges.
  • Stay updated with the latest trends and advancements in AI through reading research papers and industry publications.
  • Participate in AI competitions or projects to gain practical experience.
  • Practice answering interview questions related to AI concepts, algorithms, and real-world applications.

Through DataMites' AI Engineer Course, participants can expect to gain a comprehensive understanding of AI concepts, algorithms, and technologies. They will learn various machine learning and deep learning techniques, along with natural language processing and computer vision. Participants will acquire practical skills in building and deploying AI models, as well as optimizing and evaluating their performance. By completing the course, they will be equipped to pursue AI engineering roles and contribute to real-world AI applications.

The AI Expert Course delves deeper into advanced AI topics and techniques. It covers advanced machine learning algorithms, deep learning architectures, natural language processing, computer vision, and AI model optimization. The course is designed for individuals who want to specialize and become experts in the field of AI.

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FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN ROURKELA

Obtaining an Artificial Intelligence Certification in Rourkela is important as it validates your knowledge and skills in the field of AI. It enhances your credibility and marketability, demonstrating your competence to potential employers or clients. Certification serves as proof of your expertise and commitment to professional development in the rapidly evolving field of Artificial Intelligence.

DataMites is preferred as a choice for Artificial Intelligence Courses in Rourkela due to several reasons, including their experienced trainers who are industry professionals, comprehensive course curriculum covering various AI topics, practical hands-on learning approach, flexibility in scheduling, placement assistance,and the option to obtain certifications upon completion of the training. They prioritize the quality of education, provide access to industry-relevant projects, and offer comprehensive support to students throughout their learning journey.

DataMites offers various certifications in the field of Artificial Intelligence, including AI Engineer Certification, Certified NLP Expert Certification, AI Expert Certification, AI Foundation Certification, and AI for Managers Certification.

The duration of DataMites' Artificial Intelligence course in Rourkela may vary depending on the specific course selected. The duration can range from one month to one year, offering flexibility to accommodate different schedules and learning preferences.

Individuals can acquire knowledge in the field of Artificial Intelligence through various means, including self-study using online resources, textbooks, research papers, and tutorials. They can also enroll in AI courses and training programs, pursue academic degrees or certifications in AI or related fields, attend workshops and seminars, and engage in practical projects to gain hands-on experience.

The AI Engineer Course offered by DataMites in Rourkela aims to provide individuals with comprehensive knowledge and skills to become proficient AI engineers. The course covers essential AI concepts, machine learning algorithms, deep learning techniques, natural language processing, computer vision, and AI model deployment. Participants will gain practical experience by working on real-world projects.

To pursue a career as an AI engineer in Rourkela, individuals should acquire a strong foundation in mathematics, computer science, and programming. They can enroll in AI-related courses or training programs to learn AI concepts, algorithms, and technologies. Gaining hands-on experience through projects and internships, participating in competitions, and continuously updating knowledge in the field will also be beneficial.

DataMites accepts various payment methods for its courses in Artificial Intelligence, including online payment options such as credit/debit cards, net banking, and digital wallets. They may also provide options for bank transfers or offline payments at their training centers.

DataMites' Placement Assistance Team provides support to students in various aspects of job placement. They assist in resume preparation, conduct mock interviews, provide guidance on interview techniques, and connect students with potential job opportunities in the field of Artificial Intelligence.

Yes, participants can avail help sessions offered by DataMites to enhance their understanding of the training topics. These sessions provide additional guidance, clarify doubts, and offer further explanations to ensure a comprehensive grasp of the course content.

The fee structure for the Artificial Intelligence Training program in Rourkela at DataMites is designed to be flexible, with prices ranging from INR 60,795 to INR 154,000, accommodating different budgets and program choices.

The trainers providing instruction at DataMites are experienced industry professionals with expertise in the field of Artificial Intelligence. They bring practical knowledge and real-world insights to the training sessions, ensuring a high-quality learning experience for participants.

Yes, upon successfully completing a course with DataMites, participants can obtain a Course Completion Certificate. This certificate confirms their successful completion of the training program and can be a valuable addition to their professional credentials.

DataMites' Flexi-Pass feature in Rourkela allows participants to attend training sessions at their convenience. It provides flexibility in scheduling by offering multiple batch options and allows participants to choose the schedule that suits their availability and learning needs. This feature ensures a customized learning experience and accommodates individuals with varying commitments and preferences.

The specific documents required for the training session at DataMites may vary based on the course and program. Typically, participants are advised to carry a valid ID proof (such as a government-issued ID card) and any specific documents mentioned in the communication received from DataMites.

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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