ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN LUCKNOW

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 LUCKNOW

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 LUCKNOW

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 LUCKNOW

ARTIFICIAL INTELLIGENCE TRAINING REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING IN LUCKNOW

The human brain, with its astonishing complexity, has long been a marvel of nature. Yet, when compared to the capabilities of Artificial Intelligence (AI), the contrast is striking. While the human brain can hold about 2.5 petabytes of information, equivalent to thousands of lifetimes of TV shows, AI systems have the capacity to store and process data on a scale that surpasses human comprehension. Some AI models can handle over 100 petabytes of data, unlocking a realm of possibilities for data-driven intelligence that is both awe-inspiring and humbling. Its high time you pack your bags and enter this field of artificial intelligence.

DataMites offers a comprehensive Artificial Intelligence Course in Lucknow, designed to equip participants with the skills and knowledge required to excel in the field of AI. The course duration is 11 months, with a total of 780 learning hours dedicated to mastering various AI concepts and applications. The training program includes 100 hours of live online/offline sessions, providing an interactive learning experience with expert instructors. Participants will also have the opportunity to work on 10 capstone projects and one client project, enabling them to apply their learning to real-world scenarios.

DataMites also offers ON DEMAND artificial intelligence offline courses in Lucknow, catering to individuals who prefer in-person training. These courses cover a range of AI specializations, including Artificial Intelligence Engineering, Artificial Intelligence Expertise, Certified Natural Language Processing (NLP) Expertise, Artificial Intelligence Foundations, and Artificial Intelligence for Managers.

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

  • The institute boasts a team of experienced faculty members led by Ashok Veda, a renowned expert in the field. 

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

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

  • Hands-on projects utilizing real-world data provide valuable practical experience, preparing learners for real-world AI applications. DataMites also facilitates artificial intelligence courses with internship opportunities to further enhance participants' industry exposure and provides artificial intelligence training in placement assistance and job references to help jumpstart their careers in AI. 

  • Learners receive hardcopy learning materials and books to support their studies and become part of DataMites' exclusive learning community, fostering collaboration and networking opportunities. 

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

Lucknow, the capital city of Uttar Pradesh, is a vibrant and culturally rich metropolis known for its historical heritage and traditional charm. It is emerging as a hub for technology and education, attracting professionals and learners from various domains. Pursuing Artificial Intelligence Certification in Lucknow offers the advantage of being part of a growing tech ecosystem and gaining exposure to a dynamic and evolving market. The city's infrastructure, educational institutions, and business opportunities make it an ideal location to pursue AI education and build a successful career in the field.

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 Lucknow.

ABOUT ARTIFICIAL INTELLIGENCE COURSE IN LUCKNOW

AI is a field of study focused on creating intelligent machines that can mimic and replicate human cognitive abilities. These machines are designed to process information, recognize patterns, solve problems, and adapt to new situations, aiming to achieve human-level performance in specific tasks.

Pursuing a career as an AI engineer requires individuals to develop expertise in mathematics, computer science, and programming. They can achieve this through formal education or self-study, followed by specialized training in AI. Building a portfolio of AI projects and actively participating in AI communities and competitions can further enhance their credibility and job prospects.

Both AI and ML offer unique opportunities and challenges. AI provides a broader perspective, enabling the development of intelligent machines capable of simulating human intelligence. On the other hand, ML focuses on teaching machines to learn from data and make predictions. The choice between the two depends on personal interests and the specific industry or application one wishes to specialize in.

The career prospects for AI engineers are highly favorable in today's job market. As AI becomes increasingly integrated into various industries, the need for professionals who can design, develop, and deploy AI solutions is on the rise. AI engineers can find opportunities in industries such as healthcare, finance, e-commerce, and manufacturing, working on projects that push the boundaries of technology and making a significant impact on society.

Pursuing a formal education in AI or related fields is another pathway to start a career in artificial intelligence without prior experience. Enrolling in undergraduate or graduate programs specializing in AI provides a structured learning environment, access to research opportunities, and guidance from experienced faculty members.

Artificial intelligence encompasses a broader scope of developing machines that can exhibit intelligent behavior, which includes but is not limited to machine learning. Machine learning, in contrast, specifically deals with the development of algorithms and models that can learn from data and make predictions or decisions.

In the field of artificial intelligence, a robust educational background in computer science, AI, data science, or a related field is commonly sought after by employers. Having a bachelor's or master's degree in these areas can provide a strong foundation for a career in AI. Additionally, familiarity with programming languages, mathematics, and machine learning concepts is often considered beneficial for individuals aiming to excel in the field.

For individuals aspiring to acquire knowledge in Artificial Intelligence in Lucknow, there are specific prerequisites that are essential to meet.

Achieving mastery in Artificial Intelligence can be challenging because it requires a deep understanding of diverse topics, such as machine learning, deep learning, and natural language processing.

Integrating AI into various industries brings numerous advantages and benefits. AI can automate manual tasks, leading to increased efficiency and productivity. It enables advanced data analysis and predictive modeling, enhancing decision-making processes. AI-powered systems can provide personalized customer experiences, improving customer satisfaction. Industries can benefit from cost savings, improved safety measures, and the ability to uncover valuable insights from large datasets. Ultimately, AI integration drives innovation and competitive advantage across diverse sectors.

To prepare for AI job interviews and technical assessments, individuals should follow a comprehensive strategy. They can begin by reviewing fundamental AI concepts, algorithms, and technologies. It is crucial to practice coding and implementing AI models using popular programming languages like Python or R. Staying updated with the latest research papers and industry trends is important. Additionally, solving AI-related coding problems, participating in AI competitions, and gaining practical experience through internships or projects can greatly enhance their preparation. Finally, practicing mock interviews and improving communication skills will help effectively convey their thoughts and solutions during the interview process.

Participants in DataMites' AI Engineer Course in Lucknow can expect to gain comprehensive knowledge and skills in artificial intelligence. They will learn about various AI concepts, machine learning algorithms, deep learning architectures, natural language processing, computer vision, and AI model deployment techniques. Through practical hands-on projects and assignments, participants will develop proficiency in building and deploying AI models. Upon completion of the course, they will be equipped to pursue rewarding careers as AI engineers and contribute to the development of AI solutions in real-world scenarios.

The AI Expert Course offered by DataMites covers advanced topics and techniques in the field of Artificial Intelligence. Participants will delve deeper into advanced machine learning algorithms, deep learning architectures, natural language processing, computer vision, and AI model optimization. Through a combination of theoretical knowledge and practical implementation, participants will develop expertise in these areas. The course is designed to enhance their skills and prepare them to become proficient AI experts capable of tackling complex AI challenges and driving innovation in the field.

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FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN LUCKNOW

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

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

DataMites offers a range of certifications in the field of Artificial Intelligence, including AI Engineer Certification, Certified NLP Expert Certification, AI Expert Certification, AI Foundation Certification, and AI for Managers Certification. These certifications validate individuals' proficiency and expertise in AI, enhancing their professional recognition.

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

Individuals can acquire knowledge in the field of Artificial Intelligence through various means, including self-study using online resources, textbooks, research papers, and tutorials. They can also enroll in AI courses and training programs, pursue academic degrees or certifications in AI or related fields, attend workshops and seminars, and engage in practical projects to gain hands-on experience.

The objective of DataMites' AI Engineer Course in Lucknow is to provide individuals with comprehensive knowledge and skills to become proficient AI engineers. The course covers essential AI concepts, machine learning algorithms, deep learning techniques, natural language processing, computer vision, and AI model deployment. Participants gain practical experience by working on real-world projects.

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

DataMites' Placement Assistance Team provides support to students in various aspects of job placement. They assist in resume preparation, conduct mock interviews, offer guidance on interview techniques, and connect students with potential job opportunities in the field of Artificial Intelligence.

Yes, participants can avail help sessions offered by DataMites to enhance their understanding of the training topics. These sessions provide additional guidance, clarify doubts, and offer further explanations to ensure a comprehensive grasp of the course content.

The trainers providing instruction at DataMites are experienced industry professionals with expertise in the field of Artificial Intelligence. They bring practical knowledge and real-world insights to the training sessions, ensuring a high-quality learning experience for participants.

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

The Artificial Intelligence Training program in Lucknow at DataMites is priced between INR 60,795 and INR 154,000, depending on the specific course and program duration.

Yes, upon successfully completing a course with DataMites, participants can obtain a Course Completion Certificate. This certificate confirms their successful completion of the training program and can be a valuable addition to their professional credentials.

DataMites' Flexi-Pass feature in Lucknow offers participants the flexibility to attend training sessions at their convenience. It provides multiple batch options and allows individuals to choose a schedule that suits their availability and learning needs. This feature ensures a customized learning experience and accommodates individuals with varying commitments and preferences.

The specific documents required for the training session at DataMites may vary based on the course and program. Typically, participants are advised to carry a valid ID proof, such as a government-issued ID card, and any specific documents mentioned in the communication received from DataMites.

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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