ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN FARIDABAD

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 FARIDABAD

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 FARIDABAD

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 FARIDABAD

ARTIFICIAL INTELLIGENCE TRAINING REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING IN FARIDABAD

Artificial Intelligence (AI) is the driving force behind the digital revolution, paving the way for groundbreaking innovations. With the global AI market expected to reach a staggering $733.7 billion by 2027, industries across the world are embracing AI to enhance efficiency, improve decision-making, and unlock new possibilities. From self-driving cars and virtual assistants to healthcare diagnostics and personalized recommendations, AI is transforming the way we live, work, and interact with technology. The potential of AI is limitless, and the future holds exciting prospects for those who master this cutting-edge field.

DataMites offers a comprehensive Artificial Intelligence Course in Faridabad, designed to provide students with in-depth knowledge and practical skills in the field of AI. The course duration is 11 months, consisting of 780 learning hours, ensuring a thorough understanding of AI concepts and applications. The program includes 100 hours of live online training, allowing students to actively engage with expert instructors and participate in interactive sessions. As part of the course, students will work on 10 capstone projects and 1 client project, gaining hands-on experience in applying AI techniques to real-world scenarios. Additionally, learners will have access to a 365-day Flexi Pass and Cloud Lab, enabling them to practice their skills and access course materials at their convenience. DataMites also offers offline AI courses on demand in Faridabad, catering to the diverse learning preferences of individuals.

DataMites provides a range of specialized Artificial Intelligence Training in Faridabad, catering to different career goals and skill levels. The courses available include Artificial Intelligence Engineer, Artificial Intelligence Expert, Certified NLP Expert, Artificial Intelligence Foundation, and Artificial Intelligence for Managers. Each course is designed to equip students with the necessary knowledge and skills to excel in the field of AI, covering topics such as AI algorithms, machine learning, natural language processing, and more.

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

Experienced Faculty: DataMites boasts highly experienced faculty members, including renowned expert Ashok Veda, who bring extensive industry knowledge and expertise to the classroom.

Comprehensive Course Curriculum: The institute offers a comprehensive course curriculum that covers a wide range of AI topics, ensuring a holistic learning experience.

Global Certifications: DataMites provides global certifications from reputable organizations such as IABAC, NASSCOM FutureSkills Prime, and JainX. These certifications enhance the recognition and value of the training.

Flexible Learning Options: DataMites offers flexible learning options, allowing students to choose the mode of learning that suits them best. Whether it's online artificial intelligence training in Faridabad or ON DEMAND artificial intelligence offline classes in Faridabad, learners have the flexibility to tailor their learning experience.

Real-World Projects: The training includes real-world projects that involve working with relevant data, providing students with hands-on experience in applying AI techniques to practical scenarios.

Internship Opportunities: DataMites offers artificial intelligence internship opportunities, allowing students to gain practical experience in the field of AI and further enhance their skills.

Placement Assistance: The institute provides artificial intelligence course with placement assistance and job references, connecting students with potential employers and helping them kickstart their AI careers.

Learning Materials and Books: Students have access to hardcopy learning materials and books, ensuring they have comprehensive resources to support their learning journey.

Exclusive Learning Community: DataMites offers an exclusive learning community where students can interact with peers, share knowledge, and collaborate on projects, fostering a supportive learning environment.

Affordable Pricing and Scholarships: DataMites provides affordable pricing options for AI training, making it accessible to a wider range of learners. Additionally, the institute offers scholarships to deserving candidates, further facilitating their learning journey.

Faridabad, located in the state of Haryana, is a growing city that forms part of the National Capital Region (NCR) in India. The city offers a favorable environment for AI professionals, with various opportunities in industries such as IT, manufacturing, healthcare, and finance. Faridabad's strategic location and well-connected infrastructure make it an ideal destination for individuals seeking quality AI education and career prospects. The city's dynamic business ecosystem and the presence of renowned companies provide a platform for AI enthusiasts to thrive and contribute to innovation and technological advancements.

Along with artificial intelligence courses, DataMites also provides machine learning, deep learning, python training, IoT, data engineer, mlops, tableau, data mining, python for data science, data analytics and data science courses in Faridabad.

ABOUT ARTIFICIAL INTELLIGENCE COURSE IN FARIDABAD

The term "Artificial Intelligence (AI)" refers to the development of intelligent machines capable of performing tasks that typically require human intelligence. This involves creating algorithms and systems that can learn, reason, perceive, and make decisions.

Steps to pursue a career as an AI engineer:

  • Develop a strong foundation in mathematics, computer science, and programming.
  • Acquire knowledge and understanding 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 showcasing practical AI skills through projects.
  • Stay updated with the latest advancements and research in AI.

Several pioneers and contributors in the field of AI include Alan Turing, John McCarthy, Marvin Minsky, and Arthur Samuel. However, AI has evolved through the collective efforts of numerous researchers and scientists over time.

A career as an AI engineer is considered promising and rewarding. The demand for AI professionals is rapidly growing as organizations across industries adopt AI technologies. AI engineers have opportunities to work on cutting-edge projects, solve complex problems, and contribute to technological advancements. The field offers competitive salaries and continuous learning prospects.

Typically, a career in artificial intelligence requires:

  • A bachelor's or master's degree in computer science, AI, data science, or a related field.
  • Proficiency in programming languages like Python or Java.
  • Understanding of mathematics, including linear algebra, calculus, and statistics.
  • Familiarity with machine learning algorithms, neural networks, and deep learning frameworks.

The AI Engineer Course provides a comprehensive understanding of AI concepts, algorithms, and technologies. It covers topics such as machine learning, deep learning, natural language processing, computer vision, and AI deployment techniques. The curriculum includes hands-on projects and practical exercises to develop skills in building and deploying AI models.

To delve into Artificial Intelligence in Faridabad, it is helpful to have a basic understanding of programming concepts, familiarity with mathematics, and a curiosity to learn and explore AI technologies.

Transitioning into an artificial intelligence career from a different field can be achieved through several steps:

  • Gain foundational knowledge: Start by studying AI concepts, algorithms, and techniques through online courses, books, or tutorials.
  • Acquire programming skills: Learn programming languages commonly used in AI, such as Python or R, to implement AI models and algorithms.
  • Build a portfolio: Undertake AI projects and create a portfolio to demonstrate practical skills and showcase your abilities to potential employers.
  • Gain practical experience: Seek internships or freelance opportunities to gain hands-on experience and learn from industry professionals.
  • Network and join AI communities: Engage with AI professionals, attend conferences, and participate in AI-related events to expand your network and stay updated on industry trends.

Mastering Artificial Intelligence can be challenging due to the vastness and complexity of the field. It requires a solid foundation in mathematics, programming skills, and continuous learning to keep up with advancements. However, with dedication, practice, and ongoing learning, one can acquire proficiency in AI.

The AI For Managers Course covers topics such as AI basics, machine learning, deep learning, natural language processing, computer vision, AI implementation challenges, ethical considerations, and AI project management. It equips managers with the necessary knowledge to make informed decisions regarding AI adoption, implementation, and leveraging AI technologies for business growth.

Python is widely considered the best language for AI due to its simplicity, extensive libraries, and active community support. Python offers powerful libraries like TensorFlow, PyTorch, and scikit-learn, which facilitate the implementation of AI algorithms and frameworks. Additionally, Python's readability and versatility make it suitable for various AI tasks, including machine learning, natural language processing, and computer vision.

The AI For Managers Course is suitablefor professionals in managerial or leadership roles who are interested in understanding and utilizing AI technologies in their organizations. Executives, project managers, business analysts, and decision-makers can benefit from this course.

There are various job roles and career paths within the field of AI, including:

  • AI Engineer/Developer: Responsible for designing, developing, and implementing AI solutions and algorithms.
  • Data Scientist: Analyzes complex data sets to extract insights and develop predictive models using AI techniques.
  • Machine Learning Engineer: Develops and deploys machine learning models and algorithms for specific applications.
  • AI Research Scientist: Conducts research to advance AI technologies and develop innovative algorithms.
  • AI Consultant: Provides expert advice and guidance on implementing AI strategies and solutions for businesses.
  • AI Project Manager: Oversees AI projects, manages teams, and ensures successful implementation and delivery of AI initiatives.
  • AI Ethicist: Focuses on the ethical considerations and implications of AI technologies and develops guidelines for responsible AI usage.
View more

FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN FARIDABAD

DataMites is a preferred choice for Artificial Intelligence courses in Faridabad due to several reasons:

  • Experienced Faculty: DataMites has industry practitioners and subject matter experts as instructors, providing valuable insights and practical knowledge.
  • Practical Approach: The courses focus on hands-on learning, with projects and case studies that help participants apply their knowledge in real-world scenarios.
  • Industry-Relevant Curriculum: The curriculum is designed based on industry requirements, ensuring participants learn the latest advancements and techniques in AI.
  • Placement Assistance: DataMites offers placement assistance, helping students connect with job opportunities in the AI field.

Eligibility criteria for an Artificial Intelligence Certification Training in Faridabad may vary depending on the specific program. Generally, individuals with backgrounds in computer science, engineering, mathematics, or related fields are eligible to enroll.

Learning Artificial Intelligence in Faridabad is significant due to the increasing demand for AI professionals across industries. It equips individuals with valuable skills sought after in the job market, opens up career opportunities, and allows them to contribute to technological advancements using AI.

Obtaining an Artificial Intelligence Certification in Faridabad is important as it validates one's knowledge and skills in AI. It adds credibility to their profile, enhances job prospects, and demonstrates a commitment to continuous learning and professional development in the field.

DataMites offers various 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 the Artificial Intelligence course in Faridabad provided by DataMites may vary depending on the specific course chosen. The course duration can range from one month to one year, offering flexibility to accommodate different schedules and preferences. DataMites provides 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 the field of Artificial Intelligence through 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 AI Engineer Course in Faridabad offered by DataMites aims to equip individuals with the skills and knowledge required to become proficient AI engineers. It covers various aspects of AI, including machine learning, deep learning, natural language processing, computer vision, and AI deployment techniques. The course prepares participants to build and deploy AI models in real-world scenarios.

The Certified NLP Expert course in Faridabad offered by DataMites focuses on Natural Language Processing (NLP), a subfield of AI. The course covers fundamental NLP concepts, text preprocessing, sentiment analysis, named entity recognition, topic modeling, language generation, and neural network-based NLP models. It trains individuals in NLP techniques and their practical applications.

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

The AI Foundation Course in Faridabad at DataMites provides a comprehensive introduction to AI. It covers 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. The course establishes a strong foundation in AI concepts and techniques.

The Flexi-Pass feature available at DataMites allows participants to attend training sessions at their convenience. It offers flexibility in scheduling by providing multiple batch options. This feature ensures that individuals can balance their learning with other commitments and attend classes based on their availability and preference.

The fee for the Artificial Intelligence Training program in Faridabad at DataMites may vary depending on factors such as the specific course selected and the program duration. Generally, the fee for the Artificial Intelligence course in Faridabad ranges from INR 60,795 to INR 154,000.

DataMites offers a range of certifications in the field of Artificial Intelligence, including prestigious certifications from renowned organizations such as IABAC (International Association of Business Analytics Certifications), JAINx, and NASSCOM FutureSkills Prime. These certifications hold significant value in the industry and are highly respected. Completing the Artificial Intelligence training at DataMites provides participants with the opportunity to earn these esteemed certifications, enhancing their professional credibility and marketability in the AI field. These certifications serve as a validation of their skills and knowledge in Artificial Intelligence, distinguishing them in the competitive job market.

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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