ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN AGRA

Live Virtual

Instructor Led Live Online

154,000
99,323

  • 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
59,348

  • 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
113,673

  • 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 AGRA

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 AGRA

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 AGRA

ARTIFICIAL INTELLIGENCE TRAINING REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING IN AGRA

In the ever-evolving landscape of Artificial Intelligence (AI), the services market stands as a dynamic and thriving sector. With an estimated market size of $57.64 billion by 2026, and a remarkable compound annual growth rate (CAGR) of 38.0% from 2021 to 2026, the AI services industry is poised for exponential growth. Organizations across various sectors are harnessing the power of AI services to drive innovation, improve efficiency, and unlock new opportunities. From customized AI solutions and consulting services to AI-driven data analytics and machine learning implementations, the demand for AI services continues to surge, paving the way for a future where intelligent technologies shape our world in unprecedented ways.

DataMites offers an extensive Artificial Intelligence Course in Agra, providing participants with comprehensive training in the field. The course duration is 9 months, encompassing 780 learning hours dedicated to mastering the various aspects of AI. The training program includes 100 hours of live online/offline sessions, ensuring interactive and engaging learning experiences with expert instructors. Participants will have the opportunity to work on 10 capstone projects and one client project, allowing them to apply their knowledge to practical scenarios.

In addition to online training, DataMites also provides ON DEMAND artificial intelligence offline courses in Agra. These courses cover a range of specializations within the field of Artificial Intelligence, including Artificial Intelligence Engineering, Artificial Intelligence Expertise, Certified Natural Language Processing (NLP) Expertise, Artificial Intelligence Foundations, and Artificial Intelligence for Managers.

There are several compelling reasons to choose DataMites for Artificial Intelligence Training in Agra

  • The institute boasts experienced faculty members, led by Ashok Veda, a prominent figure in the AI industry. 

  • The course curriculum is comprehensive, covering all essential aspects of AI and ensuring a well-rounded education. Upon completion of the training, participants receive globally recognized certifications from prestigious organizations such as IABAC, NASSCOM FutureSkills Prime, and JainX, bolstering their professional credentials. 

  • DataMites offers flexible learning options including artificial intelligence training online in Agra and ON DEMAND artificail intelligence offline classes in Agra, allowing participants to access course materials and complete assignments at their convenience. 

  • The training program includes projects that involve real-world data, enabling participants to gain practical experience and insights into AI applications. The institute also facilitates artificial intelligence course with internship opportunities, providing learners with valuable industry exposure. DataMites offers artificial intelligence training with placement assistance and job references to support participants in launching their AI careers. 

  • Learners receive hardcopy learning materials and books, enhancing their learning experience. By joining DataMites, participants become part of an exclusive learning community, fostering collaboration and networking opportunities. 

  • The institute's training programs are priced affordably, and scholarships are available to make AI education accessible to a wider audience.

Agra, a city in the northern state of Uttar Pradesh, India, is renowned worldwide for its architectural masterpiece, the Taj Mahal. The city attracts tourists from all over the globe due to its rich historical and cultural heritage. Agra is also emerging as a center for education and technological advancements. Pursuing an Artificial Intelligence Certification in Agra offers the advantage of being in a city with a vibrant blend of tradition and modernity. The city's infrastructure, educational institutions, and growing tech ecosystem provide an ideal environment for individuals seeking AI education and career opportunities.

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 Agra.

ABOUT ARTIFICIAL INTELLIGENCE COURSE IN AGRA

Artificial Intelligence (AI) refers to the development of intelligent machines that can perform tasks requiring human intelligence. It involves creating algorithms and systems capable of learning, reasoning, perceiving, and making decisions.

Everyday examples of Artificial Intelligence include virtual assistants like Siri and Alexa, personalized recommendations on streaming platforms, fraud detection systems in banks, voice recognition systems, and self-driving cars.

Artificial Intelligence is widely applied across various industries such as healthcare, finance, transportation, customer service, manufacturing, and more. It has broad applications and is transforming multiple sectors.

Pursuing a career in Artificial Intelligence typically requires a strong educational background in computer science, AI, data science, or related fields. Employers often prefer candidates with a bachelor's or master's degree in these disciplines. Additionally, having knowledge of programming languages, mathematics, and machine learning concepts is beneficial.

Prerequisites for acquiring knowledge in Artificial Intelligence in Agra may include a basic understanding of programming concepts, familiarity with mathematics (particularly linear algebra and statistics), and a curious mindset to explore AI technologies. However, specific prerequisites may vary depending on the training program or course.

The future prospects for AI in the job market are highly promising. The demand for AI professionals is rapidly growing as organizations recognize the potential of AI technologies. AI-related job roles are expected to experience significant growth, providing numerous opportunities for individuals with AI skills and expertise.

To transition into an Artificial Intelligence career from a different field, individuals can take several steps. These include gaining a solid understanding of AI concepts, algorithms, and technologies through online courses, self-study, or academic programs. Learning programming languages commonly used in AI, building a portfolio of AI projects, seeking internships or freelance opportunities to gain practical experience, networking with AI professionals, and staying updated on industry trends are important for a successful transition.

Artificial Intelligence (AI) is a broader concept that encompasses the development of intelligent machines capable of simulating human intelligence. It includes various techniques and approaches, including Machine Learning (ML). Machine Learning is a subset of AI that focuses on enabling machines to learn from data and make predictions or decisions without explicit programming.

Job roles within the AI field include AI Engineer/Developer, Data Scientist, Machine Learning Engineer, AI Research Scientist, AI Consultant, AI Project Manager, and AI Ethicist. These roles involve responsibilities such as designing and implementing AI solutions, analyzing data, conducting research, managing AI projects, and considering ethical implications.

To start a career in AI, individuals can begin by acquiring a strong foundation in mathematics, computer science, and programming. They should gain knowledge and understanding of AI concepts, algorithms, and technologies through online courses, academic programs, or self-study. Learning programming languages commonly used in AI, mastering machine learning and deep learning techniques, building a portfolio of AI projects, staying updated with the latest advancements, and seeking internships or entry-level positions to gain practical experience are crucial steps.

Yes, a career in Artificial Intelligence is considered promising. The demand for AI professionals is rapidly increasing as organizations adopt AI technologies. AI professionals have the opportunity to work on cutting-edge projects, solve complex problems, and contribute to technological advancements. The field offers competitive salaries, continuous learning prospects, and a wide range of career paths.

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FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN AGRA

DataMites stands out as the preferred choice for Online Artificial Intelligence Training in Agra due to several reasons. These include highly experienced trainers who are industry professionals, a comprehensive course curriculum covering various AI topics, hands-on learning with practical projects, flexible batch options and schedules, placement assistance, and the opportunity to obtain certifications upon course completion.

DataMites provides 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 Agra may vary depending on the specific course selected. The course duration can range from one month to one year, providing flexibility to accommodate different schedules and preferences. DataMites offers training sessions on both weekdays and weekends, allowing participants to choose a schedule that suits their availability and learning needs.

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

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

DataMites' AI Foundation Course in Agra covers a comprehensive introduction to AI, encompassing subjects such as the basics of AI, machine learning, and deep learning. The course content includes an overview of AI, supervised and unsupervised learning, neural networks, deep learning algorithms, model evaluation, and deployment techniques. It lays a strong foundation in AI concepts and techniques.

The fee for DataMites' Artificial Intelligence Training program in Agra can vary depending on the specific course and duration. The cost of artificial intelligence courses in Agra generally varies between INR 60,795 and INR 154,000, allowing individuals to choose a program that aligns with their financial resources and educational objectives.

Yes, DataMites offers both online and classroom training options for Artificial Intelligence in Agra. Participants have the flexibility to choose the mode of training that best suits their preferences and requirements.

The Flexi-Pass feature provided by DataMites in Agra allows participants to attend training sessions at their convenience. It offers multiple batch options and flexible scheduling, enabling individuals to balance their learning with other commitments. This feature ensures that participants can attend classes based on their availability and preferences.

DataMites offers prestigious certifications from esteemed organizations such as IABAC (International Association of Business Analytics Certifications), JAINx, and NASSCOM FutureSkills Prime. These certifications are highly recognized in the industry and can substantially enhance your professional standing and career prospects in the field of Artificial Intelligence. Upon successfully completing the Artificial Intelligence training at DataMites, you have the chance to acquire these esteemed certifications, validating your expertise in AI.

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

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

Yes, DataMites offers placement assistance with their Artificial Intelligence Courses in Agra. They strive to support participants in connecting with job opportunities in the field of Artificial Intelligence by providing resume building, interview preparation, and job placement guidance.

The objective of DataMites' AI Engineer Course in Agra is to equip individuals with the skills and knowledge required to become proficient AI engineers. The course covers various aspects of AI, including machine learning, deep learning, natural language processing, computer vision, and AI deployment techniques. It aims to prepare participants to build AI models and deploy them effectively in real-world scenarios.

Certainly, upon completing a training program with DataMites in Agra, participants can obtain a Course Completion Certificate. This certificate acknowledges their successful completion of the program and can be a valuable addition to their professional profile.

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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