ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN GUWAHATI

Live Virtual

Instructor Led Live Online

154,000
96,900

  • 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
57,900

  • 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
110,900

  • 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 GUWAHATI

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 GUWAHATI

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 GUWAHATI

ARTIFICIAL INTELLIGENCE TRAINING REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING IN GUWAHATI

Have you ever wondered how computers can recognize faces, understand natural language, or beat human players in complex games? The answer lies in Artificial Intelligence (AI). It's the field that explores the creation of intelligent machines that can perform tasks that typically require human intelligence. AI algorithms analyze vast amounts of data, find patterns, and make predictions, enabling machines to solve complex problems and make informed decisions. From healthcare to finance to entertainment, AI is reshaping industries and revolutionizing the way we live and work.

Embark on an extraordinary journey into the world of Artificial Intelligence with DataMites comprehensive Artificial Intelligence Course in Guwahati. This 11-month program, comprising 780 learning hours, is designed to equip you with the knowledge and skills to thrive in the AI landscape. Engage in 100 hours of live online/classroom training sessions, guided by experienced instructors who bring real-world expertise to the classroom. Gain practical experience through 10 Capstone projects and one client project, immersing yourself in the realm of AI. With a 365-day Flexi Pass and access to Cloud Lab, you have the flexibility to learn at your own pace. Additionally, DataMites offers offline AI courses on demand in Guwahati for those who prefer a classroom environment.

DataMites provides a range of specialized Artificial Intelligence Training Courses in Guwahati to cater to different skill levels and career aspirations. Choose from programs such as Artificial Intelligence Engineer, Artificial Intelligence Expert, Certified NLP Expert, Artificial Intelligence Foundation, and Artificial Intelligence for Managers. These courses delve deep into the realms of AI, offering comprehensive insights and practical knowledge that will shape your journey in this transformative field.

Why Choose DataMites for Artificial Intelligence Training in Guwahati?

  • Renowned Faculty: Learn from industry expert Ashok Veda and a team of highly qualified instructors who bring their practical experience into the classroom.

  • Comprehensive Curriculum: DataMites Artificial Intelligence Course Training in Guwahati cover essential concepts, techniques, and emerging trends in the field, ensuring you stay ahead of the curve.

  • Global Certification: Earn internationally recognized certifications from prestigious organizations like IABAC, NASSCOM FutureSkills Prime, and JainX, enhancing your professional credentials.

  • Flexible Learning: DataMites offers flexible learning options to accommodate your schedule including online artificial intelligence training in Guwahati and ON DEMAND artificial intelligence offline training in Guwahati, allowing you to balance your training with other commitments.

  • Real-World Projects: Immerse yourself in hands-on projects using real-world data, developing the skills needed to tackle real challenges in the AI industry.

  • Internship Opportunities: Gain practical industry experience through artificial intelligence courses with internship opportunities, applying your AI skills in real-world scenarios.

  • Placement Assistance: Benefit from DataMites robust artificial intelligence courses with placement assistance program, which includes job references and support in securing AI-related positions.

  • Learning Resources: Access hardcopy learning materials and books that supplement your online resources, providing comprehensive study materials.

  • Exclusive Learning Community: Join DataMites' exclusive learning community, where you can connect and collaborate with fellow AI enthusiasts, fostering a culture of shared learning.

  • Affordable Pricing and Scholarships: DataMites' AI courses are priced competitively, and scholarships are available to deserving candidates, ensuring accessibility for all.

Guwahati, the gateway to Northeast India, is a city that seamlessly blends tradition with modernity. Known for its historical significance and breathtaking natural beauty, the city offers an ideal setting for pursuing Artificial Intelligence Certification in Guwahati. The city's vibrant atmosphere, coupled with its emerging technological scene, creates a nurturing environment for AI enthusiasts. Immerse yourself in the cultural tapestry of Guwahati while honing your skills in Artificial Intelligence and shaping a successful future in this transformative 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 Guwahati.

ABOUT ARTIFICIAL INTELLIGENCE COURSE IN GUWAHATI

Artificial Intelligence (AI) is a branch of computer science that focuses on developing intelligent machines capable of performing tasks that typically require human intelligence. These tasks can include decision-making, problem-solving, natural language understanding, and visual perception.

Instances of artificial intelligence in everyday life include virtual assistants like Siri, Alexa, and Google Assistant that use voice recognition and natural language processing to provide information and perform tasks. Another example is personalized recommendations on streaming platforms and e-commerce websites that utilize AI algorithms to analyze user preferences and suggest relevant content or products.

Advantages of artificial intelligence include automation of repetitive tasks, improved efficiency and accuracy in data analysis, enhanced decision-making capabilities, and potential advancements in various industries such as healthcare and transportation. Disadvantages can include job displacement due to automation, ethical concerns regarding privacy and bias, and the potential for AI systems to make errors or be vulnerable to malicious attacks.

The educational requirements for a career in artificial intelligence typically involve a strong background in computer science, mathematics, or related fields. A bachelor's or master's degree in areas like computer science, AI, machine learning, or data science is often preferred. Knowledge of programming languages, algorithms, statistics, and machine learning concepts is also necessary.

Some top companies known for hiring professionals in artificial intelligence roles include tech giants like Google, Microsoft, Amazon, Facebook, IBM, Apple, and Tesla. However, there are also opportunities in various industries such as healthcare, finance, and manufacturing where companies are actively seeking AI talent.

Artificial intelligence is a broader concept that focuses on developing intelligent systems capable of simulating human-like intelligence. Machine learning is a subset of AI that involves algorithms and models that allow systems to learn and make predictions from data without being explicitly programmed. In other words, machine learning is a technique used to achieve AI.

The educational qualifications necessary for a career in artificial intelligence usually include a bachelor's or master's degree in computer science, AI, machine learning, or related fields. It is beneficial to have a strong foundation in programming languages such as Python or Java, understanding of algorithms, statistics, and knowledge of machine learning and deep learning concepts.

To start a career in artificial intelligence with no prior experience, one can begin by self-studying and taking online courses or tutorials in AI and machine learning. Building a portfolio of AI projects, participating in Kaggle competitions, and gaining practical experience through internships or freelance work can also be valuable. Networking with professionals in the field and joining AI communities can provide opportunities for mentorship and guidance.

The AI Expert Course in Guwahati is an advanced-level program that delves into advanced AI algorithms, cutting-edge research, emerging trends, and complex applications. It often includes specialized modules or tracks in areas such as deep learning, computer vision, natural language processing, or reinforcement learning. The course aims to enhance participants' expertise and prepare them for challenging AI projects and roles.

Transitioning into an artificial intelligence career from a different field can be achieved by gaining relevant skills and knowledge through self-study, online courses, or specialized AI training programs. Building a strong AI portfolio, participating in AI projects or competitions, and networking with professionals in the field can help make connections and open opportunities for career transition. It is also beneficial to leverage transferrable skills from the previous field and seek internships or entry-level positions in AI to gain practical experience and bridge the gap. Continuous learning and staying updated with the latest advancements in AI are essential forsuccessful transition into an artificial intelligence career from a different field.

The AI Engineer Course in Guwahati typically focuses on providing comprehensive training in various AI areas such as machine learning, deep learning, natural language processing, computer vision, and AI deployment techniques. The ourse aims to develop practical skills through hands-on projects and case studies, enabling participants to build and deploy AI models for real-world applications.

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FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN GUWAHATI

DataMites offers the following certifications in the field of Artificial Intelligence in Guwahati:

  • AI Engineer Certification
  • Certified NLP Expert Certification
  • AI Expert Certification
  • AI Foundation Certification
  • AI for Managers Certification

The duration of the Artificial Intelligence course provided by DataMites in Guwahati may differ depending on the specific course selection. The course duration can range from one month to one year, allowing flexibility to cater to individual preferences and schedules. DataMites offers training sessions on both weekdays and weekends, ensuring that participants can choose a schedule that best suits their availability and learning needs.

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 online or classroom-based AI courses and training programs, pursue a degree or diploma program in AI or related fields, attend workshops, conferences, and industry events focused on AI, and engage in practical projects and competitions to gain hands-on experience.

The AI Engineer Course offered by DataMites in Guwahati 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 objective is to provide hands-on experience through practical projects and case studies, enabling participants to build AI models and deploy them in real-world scenarios.

The Certified NLP Expert course in Guwahati offered by DataMites focuses on Natural Language Processing (NLP), which is a subfield of AI. The course content includes fundamental concepts of NLP, text preprocessing, sentiment analysis, named entity recognition, topic modeling, language generation, and neural network-based NLP models. The course aims to train individuals in NLP techniques and applications, enabling them to solve real-world problems using NLP algorithms and models.

The AI Foundation Course in Guwahati at DataMites provides a comprehensive introduction to AI. It covers the basics of AI, machine learning, and deep learning. The course content includes an overview of AI, supervised and unsupervised learning, neural networks, deep learning algorithms, model evaluation, and deployment techniques. The AI Foundation Course aims to provide participants with a solid foundation in AI concepts and techniques, preparing them for further specialization or practical AI projects.

The topics covered in the Artificial Intelligence for Managers Course in Guwahati provided by DataMites include AI basics, 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 related to AI adoption, implementation, and leveraging AI technologies for business growth.

Eligibility criteria for enrolling in an Artificial Intelligence Certification Training in Guwahati may vary depending on the specific program and its prerequisites. Generally, individuals with a background in computer science, engineering, mathematics, or related fields are eligible to enroll.

DataMites is a preferred choice for Artificial Intelligence courses in Guwahati for several reasons. Some of the reasons include:

Expert Faculty: DataMites has a team of experienced instructors who are industry practitioners and subject matter experts in the field of Artificial Intelligence. They provide comprehensive guidance and mentorship throughout the training.

Practical Approach: The courses offered by DataMites have a strong focus on hands-on learning, allowing participants to gain practical experience by working on real-world projects and case studies. This practical approach enhances the learning experience and prepares individuals for real-world AI challenges.

Industry-Relevant Curriculum: DataMites designs its Artificial Intelligence courses in Guwahati based on industry requirements and emerging trends. The curriculum is regularly updated to incorporate the latest advancements in AI, ensuring that participants acquire skills that are in demand in the industry.

Placement Assistance: DataMites provides placement assistance to students enrolled in their Artificial Intelligence courses. The Placement Assistance Team offers support in job connections, resume creation, mock interviews, and interview question discussions to help students secure employment opportunities in the field of Artificial Intelligence.

Yes, DataMites provides Artificial Intelligence courses in Guwahati that include placement assistance. The Placement Assistance Team at DataMites offers services to support students in their job search, such as connecting them with relevant job opportunities, assisting in resume creation, conducting mock interviews, and providing guidance on interview questions. This placement assistance aims to facilitate the transition of students into successful AI careers.

DataMites offers Artificial Intelligence Courses in Guwahati, which provide placement assistance. The Placement Assistance Team at DataMites offers services such as job connections, resume creation, mock interviews, and interview question discussions to support students in securing employment opportunities in the field of Artificial Intelligence. It is recommended to visit the DataMites website or contact their support team for detailed information about the specific AI courses in Guwahati that include placement assistance and the extent of support provided.

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

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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