ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN GHAZIABAD

Live Virtual

Instructor Led Live Online

154,000
101,745

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

Blended Learning

Self Learning + Live Mentoring

92,000
60,795

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

Classroom

In - Person Classroom Training

154,000
116,445

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

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UPCOMING ARTIFICIAL INTELLIGENCE ONLINE CLASSES IN GHAZIABAD

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 GHAZIABAD

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 GHAZIABAD

ARTIFICIAL INTELLIGENCE TRAINING REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING IN GHAZIABAD

The AI software market is on a trajectory of incredible growth, set to reach a projected size of $126 billion by 2025. With a staggering compound annual growth rate (CAGR) of 39.7% from 2020 to 2025, this industry is poised to reshape the technological landscape. AI-powered software solutions are revolutionizing businesses, enabling them to harness the power of data-driven intelligence. From virtual assistants and chatbots to predictive analytics and machine learning algorithms, the potential applications of AI software are limitless. As organizations increasingly recognize the transformative impact of AI, the market is set to thrive, creating exciting opportunities for innovation and advancement.

DataMites offers a comprehensive Artificial Intelligence Course in Ghaziabad, designed to equip participants with the necessary skills and knowledge in the field. The course spans 11 months, comprising 780 learning hours dedicated to mastering various AI concepts and techniques. Participants will benefit from 100 hours of live online/classroom training sessions, ensuring interactive and engaging learning experiences under the guidance of expert instructors. The program includes 10 capstone projects and one client project, allowing participants to apply their acquired knowledge to real-world scenarios.

In addition to online training, DataMites also provides on demand artificial intelligence offline courses in Ghaziabad. These courses cater to different 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 Ghaziabad

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

  • The course curriculum is comprehensive, covering a wide range of topics and ensuring participants gain a well-rounded understanding of AI. Upon completion of the training, participants receive globally recognized certifications from esteemed organizations such as IABAC, NASSCOM FutureSkills Prime, and JainX, enhancing their professional credentials. 

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

  • The training program incorporates projects with real-world data, enabling participants to gain hands-on experience and practical insights into AI applications. The institute also provides artificail intelligence course with internship opportunities, allowing learners to gain industry exposure and practical knowledge. DataMites offers artificail intelligence training with placement assistance and job references to support participants in launching their careers in the AI field. 

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

  • The institute offers affordable pricing for its training programs, and scholarships are available to make AI education accessible to a wider audience.

Ghaziabad, located in the northern state of Uttar Pradesh, India, is a rapidly growing city known for its industrial and educational developments. It is part of the National Capital Region (NCR) and enjoys proximity to the capital city, Delhi. Ghaziabad's strategic location, well-connected transportation, and thriving business environment make it an ideal destination for AI education and career opportunities. The city is home to numerous educational institutions, including universities and technical colleges, providing a conducive environment for learning and growth in the AI field. Ghaziabad's infrastructural development, presence of multinational companies, and emerging tech ecosystem make it a promising location for individuals seeking AI certification and job prospects.

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 ghaziabad.

ABOUT ARTIFICIAL INTELLIGENCE COURSE IN GHAZIABAD

Artificial Intelligence (AI) refers to the development and implementation of computer systems that can perform tasks requiring human intelligence. These systems are designed to learn, reason, perceive, and make decisions, ultimately simulating human-like intelligence.

To pursue a career as an AI engineer, individuals can take the following steps: build a strong foundation in mathematics, computer science, and programming, gain knowledge of AI concepts, algorithms, and technologies, learn programming languages commonly used in AI, master machine learning and deep learning techniques, develop a portfolio of AI projects to showcase practical skills, and stay updated with the latest advancements in AI.

The preference between AI and ML depends on individual interests and career goals. AI encompasses a broader scope, including various techniques and approaches to simulate human intelligence, while ML focuses specifically on training machines to learn from data. Choosing between AI and ML requires considering the level of depth and specialization one desires in their career.

AI engineers have excellent career prospects as the demand for AI expertise continues to grow. With the increasing reliance on AI technologies, organizations are actively seeking skilled professionals who can harness the power of AI to drive innovation and create value. AI engineers can explore a wide range of industries and job roles, from developing intelligent algorithms to implementing AI solutions, making it an exciting and dynamic career choice.

Entering the field of artificial intelligence without prior experience can be achieved by building a strong foundation in mathematics, computer science, and programming, gaining knowledge of AI concepts and technologies through online courses or self-study, learning programming languages commonly used in AI, undertaking AI projects to develop practical skills, seeking internships or entry-level positions for hands-on experience, and networking with AI professionals.

Artificial intelligence involves the creation of intelligent systems that can perceive and reason about their environment, learn from experience, and make decisions. Machine learning, on the other hand, is a subset of AI that focuses on developing algorithms that can automatically learn and improve from data without being explicitly programmed. Machine learning is a tool used within the broader field of artificial intelligence to achieve specific tasks and goals.

Pursuing a career in artificial intelligence often requires a solid educational background in computer science, AI, data science, or a closely related discipline. Employers generally prefer candidates who hold a bachelor's or master's degree in these fields, as it demonstrates a strong foundational knowledge necessary for AI roles. Furthermore, having expertise in programming languages, mathematics, and machine learning concepts can significantly contribute to success in the field.

To acquire knowledge in Artificial Intelligence in Ghaziabad, individuals need to possess specific qualifications or fulfill certain prerequisites.

Yes, mastering Artificial Intelligence is considered challenging due to the complex algorithms, mathematical concepts, and continuous advancements in the field.

Implementing AI brings substantial value to organizations and industries. It enables automation of repetitive tasks, leading to increased efficiency and cost savings. AI-powered systems can analyze vast amounts of data, extracting valuable insights for better decision-making. It enhances customer experiences through personalized interactions and tailored recommendations. AI also drives innovation, enabling organizations to develop cutting-edge products and services. Overall, AI implementation empowers organizations to stay competitive, improve operations, and achieve sustainable growth.

To prepare for AI job interviews and technical assessments, individuals should focus on reviewing AI concepts, practicing coding in languages like Python or R, solving AI-related problems, staying updated with the latest advancements, building a portfolio of AI projects, participating in competitions, engaging in mock interviews, and networking with AI professionals. These strategies will help individuals showcase their knowledge, skills, and practical experience in AI, increasing their chances of success in interviews and assessments.

Participants in DataMites' AI Engineer Course can expect to gain a comprehensive understanding of AI concepts, algorithms, and technologies. They will learn machine learning, deep learning, natural language processing, computer vision, and AI deployment techniques. Through hands-on projects and practical exercises, participants will acquire the skills to build and deploy AI models. By completing the course, they will be equipped to pursue AI engineering roles and contribute to cutting-edge AI projects in various industries.

The AI Expert Course offered by DataMites covers advanced topics and techniques in the field of Artificial Intelligence. Participants will delve deeper into machine learning algorithms, deep learning architectures, natural language processing, computer vision, and AI model optimization. The course is designed to provide in-depth knowledge and practical skills required to become an expert in AI. By completing the course, participants will be equipped with advanced AI capabilities and prepared for challenging AI projects and roles.

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FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN GHAZIABAD

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.

Obtaining certification in Artificial Intelligence in Ghaziabad holds significant significance as it validates individuals' knowledge and skills in the AI field. It enhances credibility, marketability, and demonstrates expertise to potential employers or clients. Certification serves as evidence of commitment to professional growth in the rapidly evolving field of AI.

DataMites stands out as the preferred choice for Artificial Intelligence courses in Ghaziabad for several reasons. These include experienced trainers who are industry professionals, a comprehensive course curriculum covering various AI topics, practical hands-on learning approach, flexible scheduling, placement assistance, and the opportunity to obtain certifications upon completion. DataMites prioritizes quality education, industry-relevant projects, and comprehensive student support.

DataMites offers a range of 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. These certifications validate individuals' proficiency and expertise in AI, enhancing their professional recognition.

The duration of DataMites' Artificial Intelligence course in Ghaziabad varies depending on the specific course selected. It provides flexibility with durations ranging from one month to one year, accommodating different schedules and learning preferences of participants.

The purpose of DataMites' AI Engineer Course in Ghaziabad is 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 gain practical experience by working on real-world projects.

To pursue a career as an AI engineer in Ghaziabad, individuals should build a strong foundation in mathematics, computer science, and programming. Enrolling in AI-related courses or training programs helps learn AI concepts, algorithms, and technologies. Gaining hands-on experience through projects, internships, participating in competitions, and staying updated with the latest advancements in the field are also beneficial.

DataMites' Placement Assistance Team provides support to students in various aspects of job placement. They assist in resume preparation, conduct mock interviews, offer 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 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 Ghaziabad offers participants the flexibility to attend training sessions at their convenience. It provides multiple batch options and allows individuals to choose a 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.

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

The Artificial Intelligence Training program in Ghaziabad at DataMites typically requires an investment ranging from INR 60,795 to INR 154,000, depending on the specific course selected.

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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