ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN LUDHIANA

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 LUDHIANA

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 LUDHIANA

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 LUDHIANA

ARTIFICIAL INTELLIGENCE TRAINING REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING IN LUDHIANA

The global AI market is on a remarkable growth trajectory, with an estimated size of $733.7 billion by 2027. This projection signifies the tremendous potential and increasing adoption of AI technologies across industries. With a robust compound annual growth rate (CAGR) of 42.2% from 2020 to 2027, AI is poised to revolutionize business operations, transform customer experiences, and drive innovation at an unprecedented scale.  From healthcare to finance, manufacturing to retail, AI is reshaping the future by unlocking new efficiencies, enhancing productivity, and creating previously unimaginable possibilities. 

DataMites offers an extensive Artificial Intelligence Course in Ludhiana designed to equip learners with in-depth knowledge and practical skills in the field. The course spans 11 months, comprising 780 learning hours, and includes 100 hours of live online/classroom training. With a focus on hands-on learning, the program features 10 capstone projects and a client project, providing learners with valuable experience in solving real-world AI problems. Additionally, participants receive a 365-day Flexi Pass, enabling flexible learning, and access to a Cloud Lab for practical exercises. DataMites also offers offline courses on demand in Ludhiana, catering to learners who prefer in-person training.

DataMites provides various specialized courses in Artificial Intelligence, including the Artificial Intelligence Engineer, Artificial Intelligence Expert, Certified NLP Expert, Artificial Intelligence Foundation, and Artificial Intelligence for Managers. These courses cover different aspects of AI, allowing individuals to choose the program that aligns with their career goals and interests.

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

  • First, the courses are led by experienced faculty members, including industry expert Ashok Veda, ensuring high-quality instruction. The comprehensive course curriculum covers essential AI concepts, techniques, and tools, providing learners with a holistic understanding of the subject. 

  • DataMites is affiliated with renowned global certification bodies such as IABAC, NASSCOM FutureSkills Prime, and JainX, offering learners the opportunity to earn industry-recognized certifications.

  • The learning experience is flexible through online artificial intelligence training in Ludhiana and ON DEMAND artificial intelligence offline training in Ludhiana, allowing learners to study at their own pace and access the course materials and resources. Participants also work on projects with real-world data, gaining practical experience in AI implementation. 

  • DataMites provides artificial intelligence internship opportunities to enhance practical skills and offers artificial intelligence courses with placement assistance, including job references. Learners receive hardcopy learning materials and books to support their studies, and they become part of the DataMites Exclusive Learning Community, fostering networking and collaboration.

  • Lastly, DataMites offers affordable pricing options and scholarships, making AI training accessible to a wide range of individuals.

Regarding the Artificial Intelligence certification in Ludhiana, DataMites provides a reputable and globally recognized certification upon the successful completion of the AI courses. While specific details about the Ludhiana location are not available, Ludhiana is a major industrial city in Punjab, India. It is known for its manufacturing industries, including textiles, hosiery, and automotive parts. With the growing demand for AI skills in various industries, acquiring an AI certification in Ludhiana can significantly enhance career prospects and open doors to exciting job opportunities in the region's thriving industries.

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 Ludhiana.

ABOUT ARTIFICIAL INTELLIGENCE COURSE IN LUDHIANA

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

AI finds wide application across various fields such as healthcare, finance, transportation, customer service, manufacturing, and more. It has diverse applications and is transforming industries in numerous ways.

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

Prerequisites for acquiring AI knowledge in Ludhiana may include basic programming understanding, familiarity with mathematics (particularly linear algebra and statistics), and a keen interest in exploring AI technologies. However, specific prerequisites may vary based on the training program or course.

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

The future job prospects for AI are highly promising, with growing demand for AI professionals as organizations realize the potential of AI technologies. AI-related roles are expected to witness significant growth, offering ample opportunities for individuals with AI skills and expertise.

Individuals transitioning into an AI career from a different field can take several steps. These include gaining a solid understanding of AI concepts, algorithms, and technologies through online courses or self-study, learning programming languages commonly used in AI, building a portfolio of AI projects, seeking internships or freelance opportunities for practical experience, networking with AI professionals, and staying updated on industry trends through conferences and events.

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

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

Yes, a career in Artificial Intelligence holds great promise. With the increasing adoption of AI technologies by organizations, the demand for AI professionals is growing rapidly. These professionals have the chance to engage in groundbreaking projects, tackle intricate problems, and drive technological advancements. The field offers attractive salaries, continuous opportunities for learning and growth, and a diverse range of career paths.

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

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FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN LUDHIANA

Individuals can acquire knowledge in Artificial Intelligence through 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 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 Ludhiana varies depending on the chosen course. The course can last from one month to one year, with options for both weekday and weekend training sessions to accommodate different schedules.

DataMites is preferred for online Artificial Intelligence training in Ludhiana due to experienced trainers who are industry professionals, a comprehensive course curriculum covering various AI topics, hands-on learning with practical projects, flexible batch options and schedules, placement assistance, and the option to obtain certifications upon completion.

DataMites' AI Foundation Course in Ludhiana provides a comprehensive introduction to AI, covering the basics of AI, machine learning, and deep learning. The course includes supervised and unsupervised learning, neural networks, deep learning algorithms, model evaluation, and deployment techniques.

DataMites' Artificial Intelligence for Managers Course in Ludhiana covers 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 knowledge to make informed decisions about AI adoption and implementation.

Yes, DataMites offers both online and classroom training options for Artificial Intelligence in Ludhiana, allowing participants to choose the mode of training that suits their preferences.

The fee for DataMites' Artificial Intelligence Training in Ludhiana depends on the specific course and duration. DataMites' Artificial Intelligence Training program in Ludhiana offers varying fees depending on the course and duration selected. Typically, the artificial intelligence course fee in Ludhiana can range from INR 60,795 to INR 154,000, enabling individuals to select a program that fits their budget and learning goals.

The Flexi-Pass feature at DataMites in Ludhiana allows participants to attend training sessions at their convenience. It offers multiple batch options and flexible scheduling, ensuring that individuals can balance their learning with other commitments.

Certification options offered by DataMites include renowned organizations like IABAC (International Association of Business Analytics Certifications), JAINx, and NASSCOM FutureSkills Prime. These certifications hold significant recognition within the industry and can greatly boost your reputation and employability in the Artificial Intelligence field. Upon completion of the Artificial Intelligence training at DataMites, you have the opportunity to obtain these prestigious certifications, underscoring your proficiency in AI.

Yes, upon completing a training program with DataMites in Ludhiana, participants can obtain a Course Completion Certificate, which recognizes their successful completion of the program.

Yes, DataMites may offer the option to attend a demo class before enrolling in the Artificial Intelligence course in Ludhiana, allowing individuals to experience the training approach and content before making a decision.

The average salary for an Artificial Intelligence Engineer in Ludhiana varies depending on factors such as experience, skills, and the organization. Salaries in the field of Artificial Intelligence are generally competitive and can range from entry-level positions to higher-paying roles. AI Engineers in India can expect to earn an annual salary ranging from ?3.0 Lakhs to ?20.0 Lakhs, with an average of ?7.0 Lakhs, according to AmbitionBox.

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

Yes, DataMites offers Artificial Intelligence Courses in Ludhiana that include placement assistance. They provide support in resume building, interview preparation, and job placement guidance.

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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