ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN IMPHAL

Live Virtual

Instructor Led Live Online

154,000
99,323

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

Blended Learning

Self Learning + Live Mentoring

92,000
59,348

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

Classroom

In - Person Classroom Training

154,000
113,673

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

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UPCOMING ARTIFICIAL INTELLIGENCE ONLINE CLASSES IN IMPHAL

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 IMPHAL

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 IMPHAL

ARTIFICIAL INTELLIGENCE TRAINING REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING IN IMPHAL

The projected growth of the AI market to $190.61 billion by 2025 highlights the remarkable potential of this technology. AI's applications span various fields, such as natural language processing, machine learning, and computer vision. Its transformative capabilities have led to advancements like voice recognition systems and autonomous vehicles. This rapid expansion of AI is a testament to its ability to revolutionize industries and drive innovation, promising a future filled with exciting possibilities.

DataMites offers a comprehensive Artificial Intelligence Course in Imphal, designed to provide students with in-depth knowledge and practical skills in AI. The course duration is 11 months, consisting of 780 learning hours. It includes 100 hours of online/classroom training, allowing students to engage with instructors and fellow learners in real-time. Additionally, students will work on 10 Capstone projects and 1 Client project, providing hands-on experience in applying AI concepts to real-world scenarios. To ensure flexibility in learning, DataMites provides a 365 Days Flexi Pass, allowing students to access course materials and resources at their convenience. The course also includes access to a Cloud Lab, enabling students to practice and experiment with AI tools and technologies.

In addition to online courses, DataMites offers offline courses on demand in Imphal, catering to individuals who prefer face-to-face learning experiences. The available artificial intelligence training courses in Imphal are:

  • Artificial Intelligence Engineer

  • Artificial Intelligence Expert

  • Certified NLP Expert

  • Artificial Intelligence Foundation

  • Artificial Intelligence for Managers

Here are 10 reasons to choose DataMites for Artificial Intelligence Training in Imphal:

Ashok Veda and Faculty: DataMites boasts experienced instructors like Ashok Veda, who bring their expertise and industry knowledge to the classroom.

Comprehensive Course Curriculum: The course curriculum covers a wide range of AI topics, ensuring a thorough understanding of the subject.

Global Certification: DataMites offers globally recognized certifications such as IABAC, NASSCOM FutureSkills Prime, and JainX.

Flexible Learning: DataMites provides flexible learning options including online artificial intelligence courses in Imphal and ON DEMAND artificial intelligence offline classes in Imphal, allowing students to balance their studies with other commitments.

Projects with Real-World Data: Students get the opportunity to work on projects involving real-world data, enhancing their practical skills.

Internship Opportunity: DataMites offers artificial intelligence internship opportunities, allowing students to gain valuable industry experience.

Placement Assistance and Job References: The institute provides assistance with artificial intelligence training with placement and offers job references to help students kick-start their careers.

Hardcopy Learning Materials and Books: Students receive hardcopy learning materials and books, facilitating offline study and reference.

DataMites Exclusive Learning Community: Students become part of a dedicated learning community, fostering collaboration and knowledge sharing.

Affordable Pricing and Scholarships: DataMites offers competitive pricing for their courses and provides scholarships to eligible students.

Imphal, the capital city of Manipur in northeastern India, is known for its natural beauty and cultural heritage. The city is surrounded by picturesque landscapes and is home to historic sites such as Kangla Fort and Shree Shree Govindajee Temple. Imphal has a developing educational landscape with various institutions offering courses in technology and sciences. It provides an enriching environment for individuals interested in pursuing Artificial Intelligence Certification in Imphal, with its blend of natural charm and educational opportunities.

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 Imphal.

ABOUT ARTIFICIAL INTELLIGENCE COURSE IN IMPHAL

Artificial Intelligence refers to the development of intelligent machines that can perform tasks typically requiring human intelligence. It involves creating systems and algorithms capable of autonomous learning, reasoning, and decision-making, simulating human-like intelligence.

Examples of AI in daily life:

  • Virtual Assistants like Siri, Alexa, and Google Assistant.
  • Recommendation systems used by streaming platforms and e-commerce websites.
  • Email spam filters.
  • Autonomous vehicles and self-driving cars.
  • Facial recognition technology in smartphones.
  • Natural language processing in chatbots and customer support systems.

Advantages:

  • Automation of repetitive tasks, increasing efficiency and productivity.
  • Improved accuracy and precision in data analysis and decision-making.
  • Ability to handle large amounts of data and extract insights.
  • Enhanced capabilities in areas like healthcare, finance, and manufacturing.
  • Disadvantages:
  • Job displacement due to automation.
  • Ethical concerns regarding privacy, security, and bias in AI systems.
  • Dependency on AI systems, with potential risks if they malfunction or make errors.
  • High development and implementation costs.

Artificial Intelligence (AI) is a broader concept that encompasses the development of intelligent systems capable of tasks requiring human intelligence. Machine Learning (ML), on the other hand, is a subset of AI that focuses on enabling systems to learn and improve from data without explicit programming. ML algorithms enable systems to automatically learn patterns and make predictions based on the data they are exposed to.

A career in AI typically requires a strong educational background in computer science, mathematics, or related fields. The following qualifications can be beneficial:

  • Bachelor's or master's degree in computer science, AI, or related disciplines.
  • Knowledge of programming languages like Python, Java, or C++.
  • Understanding of algorithms, data structures, and statistics.
  • Familiarity with machine learning and deep learning concepts.
  • Continuous learning and staying updated with advancements in AI technologies.

The AI Engineer Course provides comprehensive training in Artificial Intelligence (AI). It focuses on developing practical skills and knowledge in areas such as machine learning, deep learning, natural language processing, computer vision, and AI deployment techniques. Participants learn to build AI models, analyze data, and apply AI algorithms to solve real-world problems. The course includes theoretical concepts, hands-on exercises, and practical projects for a well-rounded learning experience.

The AI Expert Course is an advanced program that allows participants to deepen their expertise in specific AI areas. It covers advanced AI algorithms, emerging trends, cutting-edge research, and complex applications. The course aims to equip participants with the knowledge and skills required to tackle complex AI challenges, develop innovative solutions, and push the boundaries of AI technology. Specialized modules or tracks focusing on topics like deep learning, computer vision, natural language processing, or reinforcement learning are often included.

To transition into an AI career from a different field, consider the following steps:

  • Assess existing skills and knowledge that align with AI, such as programming, mathematics, or data analysis.
  • Gain foundational knowledge in AI through online courses, tutorials, or books.
  • Build practical projects to showcase AI skills and create a portfolio.
  • Network with professionals in the AI field, attend meetups or conferences, and join relevant online communities.
  • Consider pursuing further education or certifications to strengthen AI expertise.
  • Look for entry-level positions, internships, or freelance opportunities in AI to gain hands-on experience.
  • Continuously update skills and stay informed about the latest developments in AI.
  • AI Engineer/Developer
  • Machine Learning Engineer
  • Data Scientist
  • AI Research Scientist
  • Natural Language Processing (NLP) Engineer
  • Computer Vision Engineer
  • Robotics Engineer
  • AI Project Manager
  • AI Consultant

Pursuing a career in artificial intelligence is indeed a promising choice. The demand for AI professionals is rapidly increasing in diverse industries such as healthcare, finance, e-commerce, and technology. With continuous advancements in AI technology and widespread adoption, there are abundant opportunities for individuals to make significant contributions and have a meaningful impact in this field.

  • Gain a strong foundation in mathematics, programming, and statistics.
  • Pursue a degree or certification in computer science, AI, or a related field.
  • Acquire knowledge and skills in machine learning, deep learning, and data analysis.
  • Build a strong portfolio by working on AI projects and participating in competitions.
  • Seek internships or entry-level positions in AI-related roles to gain practical experience.
  • Continuously learn and stay updated with advancements in AI technologies.
  • Network with professionals in the field, attend conferences, and join AI communities.
  • Consider pursuing advanced education or specialized certifications in AI to enhance expertise.

Python is considered one of the most suitable programming languages for AI development. It offers a wide range of libraries and frameworks, such as TensorFlow, PyTorch, and scikit-learn, that facilitate AI tasks like machine learning, deep learning, and natural language processing. Python's simplicity, readability, and vast community support make it popular among AI practitioners.

Comparing the advantages of AI and ML is subjective as they are closely related and often used together. However, some general points can be considered:

  • AI allows machines to exhibit human-like intelligence, enabling tasks that go beyond traditional programming.
  • ML is a subset of AI that focuses on algorithms and models that learn from data and improve their performance.
  • AI encompasses a broader scope, including problem-solving, decision-making, and reasoning.
  • ML, being a subset, specifically emphasizes learning patterns from data and making predictions.
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FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN IMPHAL

DataMites provides Artificial Intelligence certifications in Imphal, including AI Engineer Certification, Certified NLP Expert Certification, AI Expert Certification, AI Foundation Certification, and AI for Managers Certification.

The duration of the Artificial Intelligence course in Imphal provided by DataMites varies depending on the specific course chosen. The duration can range from one month to a year, with flexible training options available on weekdays and weekends.

Individuals can gain knowledge in Artificial Intelligence through self-study using online resources, enrolling in AI courses or degree programs, attending workshops or conferences, engaging in practical projects, and gaining hands-on experience.

The AI Engineer Course offered by DataMites in Imphal aims to equip individuals with the skills and knowledge required 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.

The Certified NLP Expert course offered by DataMites in Imphal focuses on Natural Language Processing (NLP) skills and applications. The course covers topics such as text preprocessing, sentiment analysis, named entity recognition, topic modeling, language generation, and neural network-based NLP models.

The AI for Managers Course provided by DataMites in Imphal covers topics such as AI fundamentals, machine learning, deep learning, natural language processing, computer vision, AI implementation challenges, ethical considerations, and AI project management. The course aims to provide managers with the necessary knowledge to make informed decisions regarding AI adoption and implementation.

The AI Foundation Course offered by DataMites in Imphal provides a comprehensive introduction to AI. The course covers the basics of AI, machine learning, and deep learning. Topics include supervised and unsupervised learning, neural networks, deep learning algorithms, model evaluation, and deployment techniques.

Generally, individuals with an interest in pursuing a career in Artificial Intelligence are eligible to enroll in an Artificial Intelligence Certification Training in Imphal. There are typically no strict prerequisites in terms of educational background or prior experience.

The average salary for an Artificial Intelligence Engineer in Imphal may vary based on factors such as experience, skills, industry, and the specific organization. However, an approximate average annual salary for an AI Engineer in India is around ?9,44,075.

To ensure a smooth process for issuing the participation certificate and booking the certification exam, participants are required to bring valid photo identification proofs, such as a National ID card or driving license, as proof of identity during the training session at DataMites in Imphal.

The cost of the Artificial Intelligence Training program at DataMites in Imphal depends on the particular course selected and the duration of the program. Typically, the fee for the Artificial Intelligence course in Imphal varies from INR 60,795 to INR 154,000. For accurate and detailed information about the fee structure, it is advisable to contact DataMites directly, as it may vary based on specific course offerings and features.

In the event that participants are unable to attend a session during the Artificial Intelligence training at DataMites in Imphal, they can coordinate with instructors to schedule a makeup class at a convenient time. For online training, recorded sessions will be provided to allow participants to catch up on missed content.

Yes, it is possible to attend a free demo class before enrolling in the Artificial Intelligence course at DataMites in Imphal. The demo class serves as an introduction to the training program, allowing potential participants to get an overview of the content, teaching methodology, and overall learning experience. Attending a demo class helps individuals make an informed decision about whether to enroll in the course.

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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