ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN KOTA

Live Virtual

Instructor Led Live Online

154,000
81,900

  • 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
86,900

  • 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 KOTA

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 KOTA

MODULE 1 : ARTIFICIAL INTELLIGENCE OVERVIEW 

• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence
• Why Artificial Intelligence Now?
• Areas Of Artificial Intelligence
• AI Vs Data Science Vs Machine Learning

MODULE 2 :  DEEP LEARNING INTRODUCTION

• Deep Neural Network
• Machine Learning vs Deep Learning
• Feature Learning in Deep Networks
• Applications of Deep Learning Networks

MODULE3 : TENSORFLOW FOUNDATION

• TensorFlow Structure and Modules
• Hands-On:ML modeling with TensorFlow

MODULE 4 : COMPUTER VISION INTRODUCTION

• Image Basics
• Convolution Neural Network (CNN)
• Image Classification with CNN
• Hands-On: Cat vs Dogs Classification with CNN Network

MODULE 5 : NATURAL LANGUAGE PROCESSING (NLP)

• NLP Introduction
• Bag of Words Models
• Word Embedding
• Hands-On:BERT Algorithm

MODULE 6 : AI ETHICAL ISSUES AND CONCERNS

• Issues And Concerns Around Ai
• Ai And Ethical Concerns
• Ai And Bias
• Ai:Ethics, Bias, And Trust

MODULE 1 : PYTHON BASICS 

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

MODULE 2 : PYTHON CONTROL STATEMENTS 

 • IF Conditional statement
 • IF-ELSE
 • NESTED IF
 • Python Loops basics
 • WHILE Statement
 • FOR statements
 • BREAK and CONTINUE statements

MODULE 3 : PYTHON DATA STRUCTURES 

 • Basic data structure in python
 • Basics of List
 • List: Object, methods
 • Tuple: Object, methods
 • Sets: Object, methods
 • Dictionary: Object, methods

MODULE 4 : PYTHON FUNCTIONS 

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

MODULE 1 : OVERVIEW OF STATISTICS 

 • Introduction to Statistics
 • Descriptive And Inferential Statistics
 • Basic Terms Of Statistics
 • Types Of Data

MODULE 2 : HARNESSING DATA 

 • Random Sampling
 • Sampling With Replacement And Without Replacement
 • Cochran's Minimum Sample Size
 • Types of Sampling
 • Simple Random Sampling
 • Stratified Random Sampling
 • Cluster Random Sampling
 • Systematic Random Sampling
 • Multi stage Sampling
 • Sampling Error
 • Methods Of Collecting Data

MODULE 3 : EXPLORATORY DATA ANALYSIS 

 • Exploratory Data Analysis Introduction
 • Measures Of Central Tendencies: Mean,Median And Mode
 • Measures Of Central Tendencies: Range, Variance And Standard Deviation
 • Data Distribution Plot: Histogram
 • Normal Distribution & Properties
 • Z Value / Standard Value
 • Empherical Rule and Outliers
 • Central Limit Theorem
 • Normality Testing
 • Skewness & Kurtosis
 • Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
 • Covariance & Correlation

MODULE 4 : HYPOTHESIS TESTING 

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

MODULE 1: MACHINE LEARNING INTRODUCTION 

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

MODULE 2: PYTHON NUMPY  PACKAGE 

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

MODULE 3: PYTHON PANDAS PACKAGE

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

MODULE 4:  VISUALIZATION WITH PYTHON - Matplotlib 

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

MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN

 • Seaborn: Basic Plot
 • Advanced Python Data Visualizations

MODULE 6: ML ALGO: LINEAR REGRESSION

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

MODULE 7: ML ALGO: LOGISTIC REGRESSION 

 • Introduction to Logistic Regression
 • How it works: Classification & Sigmoid Curve
 • Modeling and Evaluation in Python

MODULE 8: ML ALGO: K MEANS CLUSTERING

 • Understanding Clustering (Unsupervised)
 • K Means Algorithm
 • How it works : K Means theory
 • Modeling in Python

MODULE 9: ML ALGO: KNN

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

MODULE 1:  FEATURE ENGINEERING 

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

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

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

MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)

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

MODULE 4: ML ALGO: DECISION TREE 

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

MODULE 5: ENSEMBLE TECHNIQUES - BAGGING

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

MODULE 6: ML ALGO: NAÏVE BAYES

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

MODULE 7:  GRADIENT BOOSTING, XGBOOST 

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

MODULE 1: TIME SERIES FORECASTING - ARIMA 

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

MODULE 2:  SENTIMENT ANALYSIS

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

MODULE 3:  REGULAR EXPRESSIONS WITH PYTHON 

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

MODULE 4: ML MODEL DEPLOYMENT WITH FLASK 

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

MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL 

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

MODULE 6:  AWS CLOUD FOR DATA SCIENCE

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

MODULE 7: AZURE FOR DATA SCIENCE

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

MODULE 8: INTRODUCTION TO DEEP LEARNING

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

MODULE 1: DATABASE INTRODUCTION

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

 MODULE 2: SQL BASICS

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

MODULE 3: DATA TYPES AND CONSTRAINTS

 • Numeric, Character, date time data type
 • Primary key, Foreign key, Not null
 • Unique, Check, default, Auto increment

MODULE 4: DATABASES AND TABLES (MySQL)

 • Create database
 • Delete database
 • Show and use databases
 • Create table, Rename table
 • Delete table, Delete table records
 • Create new table from existing data types
 • Insert into, Update records
 • Alter table

MODULE 5: SQL JOINS

• Inner join
• Outer join
• Left join
• Right join
• Cross join
• Self join
• Windows functions: Over, Partition , Rank 

MODULE 6: SQL COMMANDS AND CLAUSES

 • Select, Select distinct
 • Aliases, Where clause
 • Relational operators, Logical
 • Between, Order by, In
 • Like, Limit, null/not null, group by
 • Having, Sub queries

 MODULE 7: DOCUMENT DB/NO-SQL DB

 • Introduction of Document DB
 • Document DB vs SQL DB
 • Popular Document DBs
 • MongoDB basics
 • Data format and Key methods

MODULE 1: GIT  INTRODUCTION 

 • Purpose of Version Control
 • Popular Version control tools
 • Git Distribution Version Control
 • Terminologies
 • Git Workflow
 • Git Architecture

MODULE 2: GIT REPOSITORY and GitHub 

 • Git Repo Introduction
 • Create New Repo with Init command
 • Git Essentials: Copy & User Setup
 • Mastering Git and GitHub

MODULE 3: COMMITS, PULL, FETCH AND PUSH 

• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo

MODULE 4: TAGGING, BRANCHING AND MERGING 

• Organize code with branches
• Checkout branch
• Merge branches
• Editing Commits
• Commit command Amend flag
• Git reset and revert

MODULE 5: GIT WITH GITHUB AND BITBUCKET 

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

MODULE 1: BIG DATA INTRODUCTION 

  • Big Data Overview
  • Five Vs of Big Data
  • What is Big Data and Hadoop
  • Introduction to Hadoop
  • Components of Hadoop Ecosystem
  • Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE 

  • HDFS – Big Data Storage
  • Distributed Processing with Map Reduce
  • Mapping and reducing  stages concepts
  • Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort

MODULE 3: PYSPARK FOUNDATION 

  • PySpark Introduction
  • Spark Configuration
  • Resilient distributed datasets (RDD)
  • Working with RDDs in PySpark
  • Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE 

  • Introducing Spark SQL
  • Spark SQL vs Hadoop Hive

MODULE 1: TABLEAU FUNDAMENTALS 

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

MODULE 2: POWER-BI BASICS 

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

MODULE 3 : DATA TRANSFORMATION TECHNIQUES

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

MODULE 4 :  CONNECTING TO VARIOUS DATA SOURCES 

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

MODULE 1: NEURAL NETWORKS 

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

MODULE 2: IMPLEMENTING DEEP NEURAL NETWORKS 

 • Introduction to neural networks with tf2.X
 • Simple deep learning model in Keras (tf2.X)
 • Building neural network model in TF2.0 for MNIST dataset

MODULE 3: DEEP COMPUTER VISION - IMAGE RECOGNITION

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

MODULE 4 : DEEP COMPUTER VISION - OBJECT DETECTION

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

MODULE 5: RECURRENT NEURAL NETWORK 

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

MODULE 6: NATURAL LANGUAGE PROCESSING (NLP)

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

MODULE 7: PROMPT ENGINEERING

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

MODULE 8: REINFORCEMENT LEARNING

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

MODULE 9: DEEP REINFORCEMENT LEARNING

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

MODULE 10: Gen AI

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

MODULE 11: Gen AI

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

OFFERED ARTIFICIAL INTELLIGENCE COURSES IN KOTA

ARTIFICIAL INTELLIGENCE TRAINING REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING IN KOTA

DataMites is at the forefront of Artificial Intelligence Training, offering an advanced curriculum designed to bridge theoretical knowledge with real-world application. With over 100,000 learners trained globally and accreditations from more than 20 esteemed global institutions, DataMites has established itself as a trusted name in delivering cutting-edge AI and Machine Learning training.

DataMites Artificial Intelligence course in Kota curriculum covers everything from data manipulation and visualization to advanced machine learning techniques, providing a comprehensive learning experience. The institute's seasoned instructors, drawing on extensive industry experience, skillfully navigate learners through the complexities of the subject matter.

DataMites Artificial Intelligence Engineer Course, accredited by IABAC and NASSCOM FutureSkills, is designed to meet global industry standards. This 9-month, immersive training program is offered at an offline center in Kota, blending in-person instruction with practical learning. The course provides live projects, internships, and training tailored to meet the needs of both professionals and students. Combined with dedicated placement support, it equips participants with the skills and confidence to excel in AI-driven industries.

Kota's Emerging IT Landscape

Kota, traditionally recognized as an educational hub, is now making strides toward developing its Information Technology (IT) sector. Although it is still in its nascent stages, initiatives aimed at fostering digital infrastructure and smart city projects are laying the groundwork for a technology-driven future.

Jaipur, the capital of Rajasthan, has rapidly evolved into one of India’s leading IT hubs, supported by state-sponsored initiatives such as the Rajasthan Startup Policy and the establishment of IT parks like Mahindra World City. According to reports, Jaipur’s IT sector is growing at an impressive rate of 60% annually, with a significant influx of software development, IT services, and digital marketing firms.

Ahmedabad, one of Gujarat’s economic powerhouses, has emerged as a thriving IT and software development hub. With the presence of IT parks such as the Gujarat International Finance Tec-City (GIFT City), the city has attracted investments from leading technology firms.

Why Kota is an Emerging Hub for Artificial Intelligence Training

Kota, widely recognized as a hub for education and skill development, is now carving a niche in advanced technology training, including artificial intelligence. Here's what makes Artificial Intelligence Course in Kota a unique destination:

  1. Growing Tech Ecosystem: Kota is gradually integrating technology into its industries, creating a fertile ground for applying AI concepts in fields like education, manufacturing, and public services.
  2. Cost-Effective Learning: Kota offers a more affordable learning environment compared to metropolitan cities, making it an attractive option for acquiring high-quality AI education without excessive costs.
  3. Local Opportunities: The city's industries are increasingly adopting AI to enhance operations, creating demand for skilled professionals trained in cutting-edge technologies.
  4. Strategic Location: Positioned as a gateway to Rajasthan's growing technological initiatives, Kota offers learners access to opportunities across the region while contributing to its development as an emerging tech hub.

Career Pathways in Kota’s Artificial Intelligence Ecosystem

As industries in Kota embrace AI-driven innovations, new career opportunities are emerging across sectors. Graduates of Artificial Intelligence Training in Kota can pursue roles such as:

  1. AI/ML Engineer: Building and deploying machine learning models tailored to industry needs.
  2. Data Scientist: Extracting actionable insights from complex datasets to inform decision-making.
  3. AI Researcher: Innovating and advancing AI algorithms to solve real-world challenges.
  4. Business Intelligence Analyst: Translating data into strategies that drive business growth.
  5. Robotics Engineer: Developing intelligent automation systems for diverse applications.

Success in these roles requires mastery of programming languages like Python, expertise in AI frameworks like TensorFlow and Keras, and familiarity with big data platforms and cloud technologies. Soft skills, including problem-solving and analytical thinking, are equally critical for communicating AI-driven solutions effectively.

Why DataMites for Artificial Intelligence Training in Kota?

  1. Global Recognition: Our Artificial Intelligence courses in Kota are backed by credentials accredited by prestigious organizations like IABAC and NASSCOM FutureSkills.
  2. Expert Faculty: Gain knowledge from leading industry professionals, including esteemed AI expert Ashok Veda, who bring practical insights and real-world experience to enhance your learning experience.
  3. Flexible Learning Options: DataMites provides both online and on demand offline Artificial Intelligence courses in Kota, with a conveniently located offline center for easy accessibility.
  4. Practical Project and Internships: Our Artificial Intelligence Courses in Kota with internships, seamlessly combine academic learning with practical training.
  5. Placement Assistance: DataMites offers Artificial Intelligence courses in Kota with placement assistance, ensuring a seamless transition from education to employment.

Innovative 3-Phase Learning Methodology at DataMites

DataMites follows a well-defined 3-Phase Learning Methodology, ensuring an interactive and hands-on educational experience for students.

Phase 1: Pre-Course Self-Study

Students kickstart their learning journey with high-quality video tutorials and comprehensive study materials, establishing a strong grasp of artificial intelligence fundamentals.

Phase 2: Immersive Training

This phase consists of 20 hours of training per week, distributed over a three-month period. Learners have the option to choose between live online sessions or offline artificial intelligence courses in Kota. The curriculum integrates hands-on projects, expert mentorship, and industry-relevant content to provide a thorough and rewarding learning experience.

Phase 3: Internship & Placement Assistance

Students complete 20 capstone projects along with a client project, leading to a prestigious internship certification. The DataMites Placement Assistance Team (PAT) provides personalized career support, helping students land positions with top companies.

Comprehensive Artificial Intelligence Curriculum

Our Artificial Intelligence Engineer Courses in Kota integrate the AI Expert and Certified Data Scientist (CDS) programs, offering a thorough and comprehensive education in artificial intelligence and data science. The Artificial Intelligence course curriculum encompasses a wide array of topics, including:

  1. Python Foundation
  2. Data Science Foundations
  3. Machine Learning Expert
  4. Advanced Data Science
  5. Version Control with Git
  6. Big Data Foundation
  7. Certified BI Analyst
  8. Database: SQL and MongoDB
  9. Artificial Intelligence Foundation

This comprehensive approach empowers students with the essential knowledge and skills needed to excel in the dynamic and rapidly evolving field of artificial intelligence.

Additional AI Certifications from DataMites

  1. AI for Managers: A specialized course aimed at business leaders, emphasizing the integration of AI into strategic decision-making and improving operational efficiency.
  2. Certified NLP Expert: A program focused on Natural Language Processing, perfect for those keen on exploring AI's role in understanding and interpreting human language.
  3. Artificial Intelligence Expert: A course designed for beginners and intermediate data science professionals, offering a strong, career-focused foundation in AI.
  4. Artificial Intelligence Foundation: An introductory program that offers a comprehensive understanding of AI's fundamental principles and core concepts.

DataMites Artificial Intelligence Course Tools in Kota

In our Artificial Intelligence Training in Kota, we provide comprehensive coverage of a wide array of AI tools, ensuring you gain the essential skills and expertise. These tools encompass:

  1. Anaconda
  2. Python
  3. Apache Pyspark
  4. Git
  5. Hadoop
  6. MySQL
  7. MongoDB
  8. Amazon SageMaker
  9. Google Bert
  10. Google Colab
  11. Advanced Excel
  12. Scikit Learn
  13. Azure Machine Learning
  14. Flask
  15. Apache Kafka
  16. Power BI
  17. GitHub
  18. Numpy
  19. TensorFlow
  20. Pandas
  21. Tableau
  22. Atlassian BitBucket
  23. Natural Language Toolkit
  24. PyCharm

Kota’s Tech Future and the Role of Artificial Intelligence

The AI hardware market is poised for substantial growth, with an anticipated size of $105.26 billion by 2026. This projection reflects the increasing demand for specialized hardware solutions that can support the computational requirements of artificial intelligence.

Known for its educational institutions and coaching centers, Kota has gained recognition as an academic hub. It is renowned for its coaching centers that prepare students for competitive exams. Earning an Artificial Intelligence certification in Kota can be highly beneficial, as AI skills are increasingly sought after across various industries. By obtaining a certification from DataMites professionals can enhance their career prospects and tap into the growing opportunities in the field of Artificial Intelligence in Kota.

In addition to artificial intelligence courses, DataMites offers training in machine learning, deep learning, Python, IoT, data engineering, MLOps, Tableau, data mining, Python for data science, data analytics, and data science.

ABOUT ARTIFICIAL INTELLIGENCE COURSE IN KOTA

The term "Artificial Intelligence (AI)" refers to the development of intelligent machines that can perform tasks requiring human intelligence, such as learning, reasoning, perceiving, and decision-making.

The AI Engineer Course focuses on building a solid foundation in AI concepts, algorithms, and technologies, with an emphasis on practical implementation. The AI Expert Course delves deeper into advanced AI algorithms, emerging trends, and complex applications, providing specialized knowledge and skills.

The field of AI has evolved through the contributions of many researchers and scientists over time, including Alan Turing, John McCarthy, Marvin Minsky, and Arthur Samuel.

Implementing AI offers advantages such as increased efficiency, improved accuracy, enhanced decision-making, automation of tasks, better customer experiences, and potential business innovation.

Examples of AI applications include virtual assistants, autonomous vehicles, recommendation systems, fraud detection, chatbots, image and speech recognition, medical diagnosis, and predictive analytics.

After completing AI training in Kota, career opportunities may include AI engineer, data scientist, machine learning engineer, AI research scientist, AI consultant, AI project manager, and AI ethicist roles in various industries.

Commonly used technologies in AI include machine learning algorithms, deep learning frameworks like TensorFlow and PyTorch, natural language processing tools, computer vision libraries, and AI development platforms.

AI is applied in areas such as healthcare, finance, transportation, customer service, manufacturing, and more, for tasks like diagnosis, fraud detection, autonomous vehicles, chatbots, and automation.

Prominent companies hiring for AI roles include Google, Microsoft, Amazon, IBM, and companies in healthcare, finance, automotive, and retail sectors.

Having a background in computer science, mathematics, or related fields is beneficial. Familiarity with programming languages like Python and knowledge of statistics and linear algebra are also useful prerequisites.

To start a career in AI without prior experience, one can build a strong foundation in mathematics, computer science, and programming, take AI-related courses, work on personal projects, participate in competitions, and seek internships or entry-level positions.

View more

FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN KOTA

DataMites is recognized as the preferred choice for Artificial Intelligence courses in Kota due to several reasons. These include having experienced trainers who are industry professionals, offering a comprehensive course curriculum covering various aspects of AI, emphasizing hands-on learning with practical projects, providing flexibility in batch options and schedules, offering placement assistance to connect participants with job opportunities, having a positive reputation and reviews from past participants, and providing certification options to validate knowledge and enhance professional credentials.

DataMites offers a range of certifications in 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 Kota varies depending on the specific course chosen, ranging from one month to one year. The training sessions are available on weekdays and weekends to accommodate different schedules.

Individuals can acquire knowledge in Artificial Intelligence through various methods, such as self-study using online resources, textbooks, and tutorials, enrolling in AI courses and training programs, pursuing a degree or diploma in AI or related fields, attending workshops and conferences, and engaging in practical projects and competitions.

The AI Engineer Course offered by DataMites in Kota aims to equip individuals with the necessary skills and knowledge to become proficient AI engineers. The course covers essential AI concepts, algorithms, and practical implementation techniques, enabling participants to build and deploy AI models in real-world scenarios.

Obtaining an Artificial Intelligence Certification in Kota is crucial as it validates individuals' AI knowledge and skills, enhancing their professional credibility and job prospects. It also demonstrates their commitment to continuous learning and growth in the field of AI.

To pursue a career as an AI engineer in Kota, individuals can take the following steps: develop a strong foundation in mathematics, computer science, and programming; acquire knowledge of AI concepts, algorithms, and technologies; learn programming languages commonly used in AI, such as Python or R; master machine learning and deep learning techniques; build a portfolio of AI projects to showcase practical skills; stay updated with the latest advancements and research in AI; and seek job opportunities in Kota or explore remote work options in the AI field.

DataMites accepts various payment methods for their courses in Artificial Intelligence, including online payment through credit cards, debit cards, net banking, and digital wallets.

DataMites' Placement Assistance Team provides support to students in connecting with job opportunities in the AI field. They offer assistance with resume building, interview preparation, and job placement guidance to help students leverage their AI skills and secure suitable positions.

Yes, participants can access help sessions provided by DataMites to enhance their understanding of the training topics. These sessions aim to provide additional clarification, guidance, and support to ensure comprehensive comprehension of the concepts covered.

Yes, upon successful completion of an Artificial Intelligence course from DataMites, participants receive a Course Completion Certificate. This certificate serves as evidence of completion and adds value to their professional credentials.

DataMites engages experienced trainers who are industry professionals and subject matter experts in the field of Artificial Intelligence. These trainers possess practical knowledge and expertise to deliver high-quality instruction.

DataMites' Flexi-Pass feature in Kota offers participants the flexibility to attend training sessions according to their convenience. It provides multiple batch options, enabling individuals to choose schedules that align with their availability and preferences.

Specific document requirements for the training session at DataMites may vary based on the program and location. It is advisable to contact DataMites directly for detailed information regarding any specific documents needed for the training session in Kota.

The policy for missed sessions during the Artificial Intelligence training at DataMites in Kota may vary depending on the specific course and batch. Participants are recommended to refer to DataMites' guidelines or contact their support team for information regarding the missed session policy.

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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