ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN AMRITSAR

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 AMRITSAR

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 AMRITSAR

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 AMRITSAR

ARTIFICIAL INTELLIGENCE TRAINING REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING IN AMRITSAR

As advancements in machine learning, natural language processing, and computer vision propel the AI revolution forward, the global AI market is poised to reach an astounding $190.6 billion by 2025, according to research by MarketsandMarkets. Prepare to be captivated by the transformative potential of AI as it revolutionizes industries, empowers businesses with intelligent automation, and unlocks unprecedented insights from data. Join the AI revolution and embark on a journey of innovation, efficiency, and endless possibilities.

DataMites offers an immersive Artificial Intelligence Course in Amritsar, designed to provide participants with in-depth knowledge and skills in the field of AI. The course spans 11 months, encompassing 780 learning hours, including 100 hours of live online/classroom training. Participants engage in 10 capstone projects and 1 client project, allowing them to apply their theoretical knowledge to real-world scenarios. With a 365-day Flexi Pass and access to the Cloud Lab, learners have the flexibility to study at their own pace and gain practical hands-on experience.

DataMites also offers on demand artificial intelligence offline courses in Amritsar, catering to the specific requirements and preferences of learners. These courses include Artificial Intelligence Engineer, Artificial Intelligence Expert, Certified NLP Expert, Artificial Intelligence Foundation, and Artificial Intelligence for Managers, providing a diverse range of programs to cater to various career goals and aspirations.

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

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

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

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

  • An artificial intelligence internship opportunity is provided to enable learners to apply their knowledge in industry settings. Artificial intelligence courses with placement assistance and job references are available to support participants in their career advancement.

  • Additionally, hardcopy learning materials and books are provided, facilitating the learning process. 

  • By joining the DataMites Exclusive Learning Community, learners gain access to a network of like-minded individuals and industry experts, fostering collaboration and knowledge sharing.

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

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

Amritsar, located in the state of Punjab, India, is a city known for its rich cultural heritage and historical significance. It is home to the Golden Temple, one of the most revered Sikh pilgrimage sites. Amritsar has a growing IT and technology sector, with various companies and startups establishing their presence in the city. The city offers a vibrant academic environment, with renowned educational institutions and research organizations. Pursuing an Artificial intelligence certification in Amritsar provides learners with the opportunity to tap into the city's technological ecosystem and contribute to the region's growth in the field of Artificial Intelligence.

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 Amritsar.

ABOUT ARTIFICIAL INTELLIGENCE COURSE IN AMRITSAR

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

Artificial Intelligence (AI) is the result of the collective contributions of various researchers and scientists over time, including Alan Turing, John McCarthy, Marvin Minsky, and Arthur Samuel.

Implementing AI offers numerous benefits, including increased efficiency and productivity, improved accuracy and precision in tasks, enhanced decision-making capabilities, automation of repetitive tasks, better customer experiences, and the potential for innovation and new business opportunities.

The AI Engineer and AI Expert Courses offered by DataMites differ in terms of depth and specialization. The AI Engineer Course focuses on building a strong foundation in AI concepts, algorithms, and technologies, with an emphasis on practical implementation. The AI Expert Course delves deeper into advanced AI algorithms, emerging trends, and complex applications, providing specialized knowledge and skills.

Real-world examples of AI applications include virtual assistants like Siri and Alexa, autonomous vehicles, recommendation systems, fraud detection systems, chatbots, image and speech recognition systems, medical diagnosis, and predictive analytics.

Artificial Intelligence is commonly applied in domains such as healthcare (diagnosis, drug discovery), finance (fraud detection, risk assessment), transportation (autonomous vehicles, route optimization), customer service (chatbots, virtual assistants), manufacturing (automation, quality control), and many more.

After completing AI training in Amritsar, individuals can pursue career opportunities such as AI engineer, data scientist, machine learning engineer, AI research scientist, AI consultant, AI project manager, and AI ethicist in industries such as healthcare, finance, e-commerce, and technology.

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

Prominent companies that hire AI professionals include Google, Microsoft, Amazon, IBM, as well as organizations in healthcare, finance, automotive, and retail sectors.

To acquire knowledge in AI in Amritsar, having a background in computer science, mathematics, or related fields is advantageous. Familiarity with programming languages like Python and understanding of statistics and linear algebra are also useful prerequisites. However, specific AI courses may have their own unique prerequisites, so it's recommended to check with the training provider for detailed requirements.

To enter a career in AI without prior experience, individuals can start by acquiring a strong foundation in mathematics, computer science, and programming. They can take online courses or pursue a degree in AI-related fields, work on personal AI projects, participate in competitions like Kaggle, and seek internships or entry-level positions to gain practical experience.

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FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN AMRITSAR

Obtaining an Artificial Intelligence Certification in Amritsar is important as it validates individuals' knowledge and skills in AI. It enhances their professional credibility, increases job prospects, and demonstrates their commitment to continuous learning and growth in the field.

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 Amritsar varies depending on the specific course chosen. The course duration can range from one month to one year, accommodating different schedules and preferences. Weekday and weekend training sessions are available for flexibility.

Individuals can acquire knowledge in Artificial Intelligence through various methods, including self-study using online resources, textbooks, and tutorials. They can also enroll in AI courses and training programs, pursue a degree or diploma in AI or related fields, attend workshops and conferences, and engage in practical projects and competitions.

DataMites' AI Engineer Course in Amritsar aims to equip individuals with the skills and knowledge required to become proficient AI engineers. The course covers essential AI concepts, algorithms, and practical implementation techniques. It prepares participants to build AI models and deploy them in real-world scenarios.

To pursue a career as an AI engineer in Amritsar, individuals can follow these steps:

  • Develop a strong foundation in mathematics, computer science, and programming.
  • Gain knowledge of AI concepts, algorithms, and technologies.
  • Learn programming languages commonly used in AI, such as Python or R.
  • Master machine learning and deep learning techniques.
  • Build a portfolio of AI projects to showcase practical skills.
  • Stay updated with the latest advancements and research in AI.
  • Seek job opportunities in Amritsar or explore remote work options in the AI field.

DataMites accepts various payment methods for their courses in Artificial Intelligence, including online payment through credit cards, debit cards, and net banking. They may also offer options for payment through digital wallets or other online payment platforms.

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

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

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

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

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

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

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

DataMites stands out as the preferred choice for Artificial Intelligence courses in Amritsar due to the following reasons:

  • Experienced trainers who are industry professionals.
  • Comprehensive course curriculum covering various aspects of AI.
  • Hands-on learning approach with practical projects.
  • Flexibility in batch options and schedules.
  • Placement assistance to connect participants with job opportunities.
  • Positive reputation and reviews from past participants.
  • Certification options to validate knowledge and enhance professional credentials.

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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