AI ENGINEER CERTIFICATION AUTHORITIES

CERTIFIED AI ENGINEER COURSE FEATURES

ARTIFICIAL INTELLIGENCE LEAD MENTORS

CERTIFIED AI ENGINEER COURSE FEE

Live Virtual

Instructor Led Live Online

2,890
1,910

  • IABAC® & NASSCOM® Certification
  • 9-Month | 780 Learning Hours
  • 120-Hour Live Online Training
  • 20 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® & NASSCOM® Certification
  • 1 Year Access To Elearning
  • 10 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Learner assistance and support

Corporate Training

Customize Your Training


  • 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 ENGINEER ONLINE CLASSES

BEST ARTIFICIAL INTELLIGENCE ENGINEER 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 ENGINEER COURSE

Why DataMites Infographic

SYLLABUS OF AI ENGINEER COURSE

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

CERTIFIED AI ENGINEER COURSE REVIEWS

ABOUT AI ENGINEER TRAINING COURSE

Artificial Intelligence has been successful in bringing revolution across industries. It has become ubiquitous and is greatly contributing to change. The idea of incorporating human intelligence into machines has ushered in curiosity for further experimentation. Today AI is recognised as a branch of computer science and is being widely used in areas like Business, Medicine, Education, Customer Service etc. Apart from technology, AI is now identified as a way out, for dealing with complexities.

The Artificial Intelligence course helps you to gain a sound knowledge of Machine Learning, Deep Learning, Programming Languages like ‘Python’ and ‘R’.

ABOUT DATAMITES AI ENGINEER COURSE

The Artificial Engineer course offers the knowledge and skills that are required to become a successful AI Engineer. Precisely, the course talks about the ways to find a solution to complex problems, by employing deep learning, machine learning, computer vision, and NLP methods.

The Artificial Intelligence Engineer course helps to get a better understanding of Statistics for Data Science, Machine Learning, Deep Learning, Programming Such as Python and Tensor Flow.

Some of the prerequisites for pursuing the Artificial Engineer course are:-

  • Knowledge of basic statistical tools and techniques.
  • Basics of programming languages such as Python
  • Problem-solving skills.
  • Curious mind
  • Graduates/Freshers who wish to make a career in Artificial Intelligence.
  • Professionals in the field of analytics, who wish to make a career switch to AI.
  • Senior Analysts and Team Leaders.

Artificial Intelligence has gained importance across sectors and is instrumental in bringing change. Medical, Banking and Finance, Manufacturing, Retail, Hospitality, every sector of the economy uses AI. This can be attributed to the problem-solving capability in AI. Pursuing the Artificial Intelligence for Engineers course

  • Helps to find solutions to complex real-world problems by using Machine Learning and Deep Learning techniques.
  • Take important decisions in critical areas.
  • Maximise revenue and Goodwill of the company.

DataMites  is an authorized institute by the International Association of Business Analytics Certifications (IABAC™) providing global data science certification courses

  • An authorized institute by the International Association of Business Analytics Certifications (IABAC™) providing global data science certification courses
  • Founded by Data Science/Analytics Experts, who have deep roots in providing management consulting to major companies in India, Europe and USA.
  • Delivered 20+ sessions for Senior Executives of tier-1 companies in India including, CTS, TVS Group, Rane Group etc.,
  • In collaboration with CII - Confederation of Indian Industry, conducted workshops for Senior executives in India.
  • DataMites has in-house faculties who are from elite universities, IIM’s and PH.Ds in Data Science. 

Artificial intelligence (AI) refers to the use of computers and technology to mimic human intelligence and perform tasks that typically require human cognitive abilities, such as problem-solving and decision-making.

The term "artificial intelligence" was first introduced by John McCarthy, a renowned computer science professor at Stanford University.

  • Robotics: AI-powered robots that can perform tasks and interact with their environment.
  • Healthcare: AI systems used for disease diagnosis, personalized treatment, and medical image analysis.
  • Data Security: AI algorithms that detect and prevent cyber threats and protect sensitive information.
  • Gaming: AI opponents and game characters that exhibit intelligent behavior and adapt to player actions.
  • Finance: AI algorithms used for fraud detection, risk assessment, and automated trading.
  • Digital Media, Social Media: AI-powered recommendation systems and content analysis.
  • Travel: AI chatbots for customer support and AI-powered travel planning.
  • Automotive Industry: AI-enabled self-driving cars and advanced driver assistance systems.
  • Customer Service: AI chatbots and virtual assistants for customer interaction.
  • Facial Recognition: AI algorithms for biometric identification and authentication.

Learning Artificial Intelligence offers numerous benefits as it is a rapidly growing field with vast potential. AI technologies like intelligent voice assistants, self-driving cars, and robotic process automation have gained significant prominence. The demand for AI professionals is high, and it opens doors to lucrative career opportunities.

Upon completing Artificial Intelligence Training, individuals can explore various job roles, including:

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

To excel in learning Artificial Intelligence, it is beneficial to have skills in computer programming, statistics, data modeling, data validation, and design. Non-technical skills such as critical thinking, curiosity, and a strong interest in math and science are also important.

A wide range of AI software development tools and platforms are accessible in the market, offering various capabilities for creating and implementing AI applications. Notable examples include Microsoft Azure AI Platform, Google Cloud AI Platform, IBM Watson, Infosys Nia, Dialog Flow, and BigML. These technologies furnish developers with frameworks and services that facilitate the development, deployment, and management of AI-powered solutions. By leveraging these advanced tools, developers can harness the potential of artificial intelligence to create innovative applications across diverse domains.

An AI Engineer is a professional who specializes in designing, developing, and implementing artificial intelligence systems and technologies to solve complex problems and create intelligent solutions.

The job responsibilities of an AI Engineer include designing and implementing AI models using deep learning neural networks and machine learning algorithms, collaborating with various stakeholders, conducting statistical analysis, automating processes, and translating machine learning models into usable APIs.

The key skills for AI Engineers include proficiency in mathematics, programming, analytics, business intelligence, communication, collaboration, and critical thinking.

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.

The Artificial Intelligence Engineer Certification is important because it validates and demonstrates your expertise and skills in the field of artificial intelligence. It serves as a recognized credential that employers can trust, indicating that you have the necessary knowledge and proficiency to work on AI projects and contribute effectively to the industry. Having an AI Engineer Certification can enhance your career prospects, open up job opportunities, and increase your earning potential. It also showcases your commitment to continuous learning and professional development in the dynamic field of artificial intelligence.

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FAQ’S OF CERTIFIED AI ENGINEER TRAINING COURSE

The trainers for the Artificial Intelligence Engineer course have experienced Data Scientists, AI and Machine Learning experts who possess good knowledge of the subject matter.

No. Coding is not required to learn Artificial Intelligence Engineer courses.

You can enrol for the Artificial Intelligence Engineer Course by visiting our website, and doing the payment through Debit/Credit card, Visa. The receipt for the payment done will be sent to your registered E-mail id.

Yes. You will be given a certificate after the completion of the Artificial Intelligence Engineer course.

You will receive certification from IABAC® - International Association of Business Analytics Certification.

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

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 constraints, 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 an individual basis in assisting for the right Data Science job.

You get a 100% refund training fee if 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 the latest information on Data Science as well as job updates.

To learn Artificial Intelligence effectively, it is advisable to enroll in DataMites' Artificial Intelligence Course, which provides comprehensive training to prepare you for future employment in the field.

DataMites offers the following Artificial Intelligence Certifications:

  • Artificial Intelligence Engineer
  • Artificial Intelligence Expert
  • Certified NLP Expert
  • Artificial Intelligence for Managers
  • Artificial Intelligence Foundation

Prerequisites for an Artificial Intelligence Engineering course include basic knowledge of statistical tools and methodologies, programming skills in languages like Python, and a curious mindset.

The Artificial Intelligence Engineer course has a duration of 11 months, with training sessions available on weekdays and weekends.

The Artificial Intelligence Engineer program is suitable for graduates, newcomers interested in pursuing a career in AI, professionals in analytics looking to transition into AI roles, and team leaders or senior analysts seeking to enhance their skills.

By enrolling in online Artificial Intelligence Engineer training from DataMites, you can benefit from their accreditation by the International Association of Business Analytics Certification (IABAC), access to real-world projects and case studies, an IABAC certification upon completion, and the opportunity for an internship with Rubix, a global technology company.

The fee for the online Artificial Intelligence Engineer Training is as follows:

  • USA: $2,890 (discounted price: $1,910)
  • India: INR 154,000 (discounted price: INR 101,745)
  • UK: GBP 2,430 (discounted price: GBP 1,521)

DataMites provides classroom training in Bangalore. However, arrangements for classroom training in other locations can be made on-demand, depending on the availability of other candidates from the same location.

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

The Flexi-Pass offered by DataMites for the Artificial Intelligence Engineer training allows participants to attend sessions related to any queries or revision they may require for a duration of 3 months.

Upon completion of the training, participants will receive an IABAC certification, which is globally recognized for relevant skills.

Yes, after completing the course, DataMites will issue a Course Completion Certificate.

In case you miss a session, you can contact your instructors to schedule a makeup class. For online training, all sessions are recorded and uploaded for participants to catch up at their own pace.

Yes, DataMites provides a free demo class to give participants a brief idea of the training methodology and what to expect from the course.

Yes, DataMites has a dedicated Placement Assistance Team (PAT) that provides placement facilities to participants upon completion of the course.

The DataMites Placement Assistance Team (PAT) offers services such as job connections, resume creation, mock interviews with industry professionals, and interview question discussions to help participants kickstart their careers in Artificial Intelligence.

The career mentoring sessions conducted by the Placement Assistance Team (PAT) aim to guide participants in understanding the various career options available in Artificial Intelligence, the challenges they may face as newcomers, and strategies to overcome them. Industry experts provide insights and help participants explore their career paths.

Yes, participants are encouraged to make the most of their training sessions and can request additional support sessions for further clarification.

DataMites accepts payments through various methods, including cash, net banking, checks, debit cards, credit cards, 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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