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Live Virtual

Instructor Led Live Online

2,890
1,910

  • 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

1,730
1,143

  • 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

Corporate Training

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  • Instructor-Led & Self-Paced training
  • Customized Learning Options
  • Industry Expert Trainers
  • Case Study Approach
  • Enterprise Grade Learning
  • 24*7 Cloud Lab

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UPCOMING AI COURSE ONLINE CLASSES

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 AI COURSES

Why DataMites Infographic

SYLLABUS OF ARTIFICIAL INTELLIGENCE COURSES

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

ARTIFICIAL INTELLIGENCE CAREER SUCCESS STORIES

ARTIFICIAL INTELLIGENCE COURSE REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING COURSE

Artificial Intelligence (AI) entered the mainstream and revolutionized the way we do business. AI is increasingly being adopted in automating many jobs with much higher productivity, lower cost, and scalable solutions. It is reported by PWC in a publication that about 50% of human jobs will be taken away by the AI in the next 5 years. There is already a big demand for AI specialists and this demand will be exponentially growing in the coming years.

DataMites Artificial Intelligence course is designed to lay a strong foundation of AI basics and build on skills aligned with current industry requirements, to get you ready for AI career. This course covers popular Deep Learning algorithms: Convolutional Networks, BatchNorm, RNNS, etc., with the case studies from autonomous driving, healthcare, Natural language processing, etc.,

Artificial Intelligence (AI) has become a rapidly expanding field with numerous job opportunities. Artificial Intelligence involves the development of machines that can mimic human-like behavior, learn from data, and make decisions. It has made significant advancements in various industries such as healthcare, retail, hospitality, manufacturing, and finance.

Businesses worldwide are embracing Artificial Intelligence to enhance their operations and improve customer experiences. Artificial intelligence and machine learning have become essential for businesses to stay competitive and deliver personalized services to customers. The field is expected to have over 11.6 million job openings by 2026, according to the US Bureau of Labor Statistics.

DataMites Institute offers top-notch artificial intelligence courses designed to equip students and professionals with the necessary skills and knowledge to thrive in the Artificial Intelligence industry. With a team of expert faculty members and a comprehensive curriculum, DataMites ensures a transformative learning experience.

Reasons to Choose DataMites for Artificial Intelligence Courses:

Expert Faculty: At DataMites, Ashok Veda is a lead mentor and the founder of an AI startup company; Rubixe, who has successfully coached over 20,000 aspirants in the domain of data science and artificial intelligence. The faculty comprises seasoned experts with extensive industry experience and contributions to research and development.

Comprehensive Curriculum: The course curriculum covers all essential topics in artificial intelligence, ensuring a thorough understanding of the subject. Course covers machine learning, python programming, computer vision, natural language processing (nlp), GAN and other artificial intelligence key areas.

Global Certification: DataMites offers industry-recognized certifications from IABAC, Jain University, and NASSCOM FutureSkills, enhancing your credibility and employability.

Flexible Learning Options: Choose from live online classes, self-paced learning, or classroom training based on your schedule and preferences.

Real-World Projects: Hands-on projects using real-world data provide practical experience and strengthen problem-solving skills.

Internship Opportunities: DataMites offers Artificial Intelligence Internships, allowing you to apply your skills in real-world scenarios and gain valuable industry experience.

Placement Assistance: A dedicated placement assistance team provides guidance, support, and job references to help you launch your Artificial Intelligence Career.

Learning Materials: Access hardcopy learning materials and books that serve as valuable references throughout your Artificial Intelligence journey.

Exclusive Learning Community: Join a vibrant learning community to connect with fellow learners, industry experts, and mentors for collaboration and knowledge sharing.

Affordable Pricing and Scholarships: DataMites provides training programs at affordable prices without compromising on quality. We believe in making Artificial Intelligence education accessible to all, which is why we offer scholarships to eligible candidates.

3 Phase Learning

Phase 1 focuses on preparing candidates for the course through self-study videos and high-quality study materials. Phase 2 consists of intensive live training, hands-on projects, and the opportunity to earn the globally recognized IABAC Artificial Intelligence Certification. Phase 3 includes projects, internships, and a job-ready program.

DataMites offers several AI courses:

Artificial Intelligence Engineer: The Artificial Intelligence Engineer course is a comprehensive program that encompasses a range of subjects, such as artificial intelligence fundamentals, machine learning, deep neural networks, computer vision, natural language processing, reinforcement learning, and the deployment of deep learning models. The program has a duration of eleven months.

Expert in Artificial Intelligence (AI): This three-month course covers artificial intelligence foundations, machine learning, deep neural networks, computer vision, natural language processing, reinforcement learning, and generative adversarial networks.

Artificial Intelligence for Managers: This one month course is designed for senior executives and managers who want to apply artificial intelligence knowledge at the organizational level. It helps participants understand AI's capabilities and leverage its recommendations to improve decision-making.

Certified Natural Language Processing Expert: Natural language processing (NLP) is a crucial aspect of AI. This three-month course focuses on developing NLP skills and applying them in real-world scenarios.

Artificial Intelligence Foundation: This two month course is suitable for individuals with no prior experience in artificial intelligence or programming. It provides a practical understanding of artificial intelligence concepts, applications, and use cases across industries. 

DataMites offers flexible learning options, including classroom courses, online courses, and blended learning sessions. The global artificial intelligence market is projected to reach USD 360.36 billion by 2028, creating numerous job opportunities.

If you're interested in pursuing a career in AI, DataMites' artificial intelligence training can be a valuable step towards success.

Artificial Intelligence Classroom Training Centres: Bangalore, Hyderabad, Mumbai, Chennai, Delhi, Pune and Ahmedabad.

 

ABOUT DATAMITES ARTIFICIAL INTELLIGENCE COURSES

Artificial Intelligence (AI) entered the mainstream and revolutionized the way we do business. AI is increasingly being adopted in automating many jobs with much higher productivity, lower cost, and scalable solutions. It is reported by PWC in a publication that about 50% of human jobs will be taken away by the AI in the next 5 years. There is already a big demand for AI specialists and this demand will be exponentially growing in the coming years.

DataMites Artificial Intelligence course is designed to lay a strong foundation of AI basics and build on skills aligned to current industry requirements, to get you ready for an AI career. This course covers popular Deep Learning algorithms: Convolutional Networks, BatchNorm, RNNS, etc., with the case studies from autonomous driving, healthcare, Natural language processing, etc.,

  • Introduce Deep Learning AI algorithms
  • Improving Deep Learning models with Optimization and Hyperparameter tuning
  • Convolutional Neural Networks
  • Structuring Deep Learning Projects
  • Hands-on Case Study
  • Machine Learning essential knowledge is mandatory
  • Basic Python programming skills is required.

Artificial Intelligence is revolutionizing the way business. Learning Deep Learning will make you a scare, highly in demand resource and has the potential to supercharge your career growth. In simple words, this course will help you build a career in the most coveted domain, AI.

This course is an advanced course, professionals with essential knowledge in Machine Learning and aspiring to be an AI specialist can opt.

 

  • Professionals aspiring to become AI specialist
  • Machine learning professionals wanted to move up this the ladder with AI skills

This course covers most of the popular AI, deep learning algorithms along with the application case study. This course will help you build an AI career in an effective manner.

DataMites™ is the global institute for Data Science accredited by International Association of Business Analytics Certifications (IABAC). DataMites provides flexible learning options from Classroom training, Live Online to high quality recorded sessions

The 6 Key reasons to choose Data Mites™

IABAC™ Accredited

  • Globally reputed certification
  • Syllabus Aligned with IABAC global market standards

Elite Faculty & Mentors

  • Best in industry faculty from IIMs
  • Course structured by Professors in Data Science from top universities
  • Ensures high quality learning experience

Learning Approach

  • Learning through case study approach
  • Theory → Hands On → Case Study → Project → Model Deployment

10+ Industry Projects

  • 10+ Industry related projects
  • Enabling candidates to gain real time skills, also boosting confidence for real challenges

PAT (Placement Assistance Team)

  • Dedicated PAT (Placement assistanceTeam)
  • Resume assist service
  • Mapping candidates to verified jobs by PAT team
  • Supporting in Interview preparation

24x7 Cloud Lab for ONE year

  • High capacity data science cloud lab
  • All Machine Learning python and R scripts on cloud lab for quick reference
  • Enable participants to practice Data Science even with their mobile phones through cloud lab

Artificial Intelligence (AI) refers to the utilization of computers and technology to simulate human intelligence, including problem-solving and decision-making abilities.

John McCarthy, a professor emeritus of computer science at Stanford University, introduced the term "artificial intelligence" for the first time.

  • Robotics
  • Healthcare
  • Data Security
  • Travel
  • Gaming
  • Finance
  • Digital Media and Social Media
  • Automotive Industry
  • Customer Service
  • Facial Recognition
  • Cost-effective solutions for complex tasks
  • Enhanced efficiency in the workplace
  • Reduced errors in results
  • Time-saving capabilities
  • 24/7 availability
  • Augmentation of human skills
  • Wide market potential
  • Improved decision-making processes

NLP stands for Natural Language Processing, which is a branch of artificial intelligence that focuses on computers' ability to understand and interpret human language.

AI is extensively used in various industries for personalized recommendations, product optimization, inventory planning, logistics, and more.

Learning Artificial Intelligence opens up numerous career opportunities in industries leveraging AI and machine learning technologies. It also provides a competitive edge and higher earning potential.

Anyone interested in learning Artificial Intelligence, from beginners to professionals, can enroll. There are options for engineers, marketing professionals, software and IT professionals, and regular courses for individuals with basic high school education.

  • Big Data Engineer
  • AI Data Analyst
  • Business Intelligence Developer
  • Data Scientist
  • Machine Learning Engineer
  • Research Scientist
  • Product Manager
  • AI Engineer
  • Robotic Scientist
  • Data Analyst

Prerequisites for learning Artificial Intelligence include computer programming skills, knowledge of statistics and probability, data modeling, data validation, and design. Non-technical skills such as critical thinking, curiosity, and passion for math and science are also beneficial.

Artificial Intelligence can be challenging, but with dedication and interest, it is achievable. The level of difficulty depends on the individual, and the field of AI holds great potential for the future.

Some of the popular AI software development tools include Microsoft Azure AI Platform, Google Cloud AI Platform, IBM Watson, Infosys Nia, Dialog Flow, and BigML.

Artificial Intelligence finds applications in various fields, including travel, healthcare, sales, credit and insurance, marketing, social media, automation, and many more.

Artificial Intelligence Certification is essential as it demonstrates expertise and knowledge in AI, making individuals valuable assets for organizations seeking AI professionals in today's technology-driven world.

According to Glassdoor.com:

  • The Artificial Intelligence Engineer Salary in United States is USD $1,05,634 per year.

  • The national average salary for an Artificial Intelligence Engineer in UK is £52,712 per annum.

  • The national average salary for an Artificial Intelligence Engineer in India is INR ₹9,44,075 per year.

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FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING COURSE

DataMites™ provide flexible learning options from traditional classroom training, latest virtual live classroom to distance course. Based on your location preference, you may have one or more learning options

This course is perfectly aligned to the current industry requirements and gives exposure to all latest techniques and tools. The course curriculum is designed by specialists in this field and monitored improved by industry practitioners on continual basis.

All certificates can be validated with your unique certification number at IABAC.org portal. You also get candidate login at exam.iabac.org , where can find your test results and other relevant validation details.

The results of the Exam are immediate, if you take online test at exam.iabac.org portal. The certificate issuance, as per IABAC™ terms, takes about 7-10 bussiness days for e-certificate.

No, the exam fees are already included in the course fee and you will not be charged extra.

Course fee needs to be paid in one payment as it is required to block your seat for the entire course as well as book the certification exams with IABAC™. In case, if you have any specific constrains, your relation manager at DataMites™ shall assist you with part payment agreements

DataMites™ has a dedicated Placement Assistance Team(PAT), who work with candidates on individual basis in assisting for right Data Science job.

You get 100% refund training fee if you the training is not to your satisfaction but the exam fee will not be refunded as we pay to accreditation bodies. If the refund is due to your availability concerns, you may need to talk to the relationship manager and will be sorted out on case to case basis

DataMites™ provides loads of study materials, cheat sheets, data sets, videos so that you can learn and practice extensively. Along with study materials, you will get materials on job interviews, new letters with latest information on Data Science as well as job updates.

DataMites provides certifications in various Artificial Intelligence courses, including Artificial Intelligence Engineer, Artificial Intelligence Expert, Certified NLP Expert, Artificial Intelligence for Managers, and Artificial Intelligence Foundation.

The duration of the Artificial Intelligence course varies depending on the specific course you choose, ranging from 1 month to a year. Training sessions are available on weekdays and weekends to accommodate different schedules.

To learn Artificial Intelligence, you can enroll in the Artificial Intelligence Course offered by DataMites. The course provides comprehensive instruction and prepares you for future career opportunities in the field.

The objective of the AI Engineer course is to provide individuals with the necessary knowledge and abilities to develop intelligent algorithms capable of analyzing data, making predictions, and addressing intricate problems. This is accomplished through the utilization of various techniques such as deep learning, machine learning, computer vision, and natural language processing. The course aims to equip participants with the expertise needed to succeed as AI Engineers in the field of artificial intelligence.

The Certified NLP Expert course focuses on developing and applying natural language processing skills in real-world scenarios. It explores various methods and approaches to harness the potential of Natural Language Processing.

The Artificial Intelligence for Managers Course is designed to enable executives and managers to leverage AI knowledge in their organizations. It helps them understand the employability and potential impact of AI at different levels within a company.

The AI Foundation Course is a beginner's course that provides a comprehensive understanding of AI, its applications, and real-world examples. It caters to individuals with or without technical backgrounds, covering concepts like machine learning, deep learning, and neural networks.

DataMites is a globally recognized institute for Artificial Intelligence accredited by the International Association of Business Analytics Certification (IABAC). With a large number of enrolled students, DataMites offers a three-step learning approach, including self-study materials, live online training, and real-world projects. Upon completion, you receive an IABAC certification and internship opportunities with AI company Rubixe.

The Artificial Intelligence Course Fee varies depending on the course and mode of training. In the USA, the range is from 272 USD to 1910 USD, in India it's from 14,595 INR to 101,745 INR, and in UK, it's from GBP 450 to GBP 1521.

Yes, DataMites provides classroom training, primarily in Bangalore. However, they can organize training in other locations based on demand and the availability of candidates.

The trainers at DataMites are certified and highly qualified professionals with extensive industry experience and expertise in the subject matter.

Flexi-Pass at DataMites allows you to attend sessions related to any queries or revisions for a period of 3 months after the training.

Yes, upon completion, DataMites will provide you with an IABAC certification that holds global recognition of your acquired skills.

Yes, DataMites will issue a Course Completion Certificate after you have successfully completed the course.

You need to carry valid photo ID proofs such as a National ID card or driving license for issuing the participation certificate and booking the certification exam.

If you miss a session, you can coordinate with your instructors to schedule a makeup class at your convenience. In the case of online training, recorded sessions will be available for you to catch up on any missed content.

Yes, DataMites offers a free demo class to provide an overview of the training and its content.

Yes, DataMites has a dedicated Placement Assistance Team (PAT) that offers placement facilities to candidates upon completion of the course. The PAT assists with job connections, resume creation, mock interviews, and interview question discussions.

The Placement Assistance Team at DataMites assists candidates in various aspects of starting their careers in Artificial Intelligence. This includes making job connections, creating effective resumes, conducting mock interviews with industry professionals, and discussing interview-related questions.

The career mentoring sessions at DataMites are designed to guide applicants in understanding the opportunities and challenges in the field of Artificial Intelligence. Industry experts provide insights into different career paths and help applicants gain a comprehensive understanding of their options and ways to overcome obstacles.

DataMites follows a case study approach to learning, which involves theory, hands-on practice, case studies, project implementation, and model deployment.

Yes, you can request support sessions to clarify any doubts or gain a better understanding of the topics covered in the training.

DataMites accepts various payment methods, including cash, net banking, check, debit card, credit card, PayPal, Visa, Mastercard, and American Express.

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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