ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN DHANBAD

Live Virtual

Instructor Led Live Online

154,000
81,900

  • 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
86,900

  • 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 DHANBAD

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 DHANBAD

MODULE 1 : ARTIFICIAL INTELLIGENCE OVERVIEW 

• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence
• Why Artificial Intelligence Now?
• 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

MODULE3 : TENSORFLOW FOUNDATION

• 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
• 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 : PYTHON BASICS 

 • Introduction of python
 • Installation of Python and IDE
 • Python Variables
 • Python basic data types
 • Number & Booleans, strings
 • Arithmetic Operators
 • Comparison Operators
 • Assignment Operators

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
 • Basics of List
 • List: Object, methods
 • Tuple: Object, methods
 • Sets: Object, methods
 • Dictionary: Object, methods

MODULE 4 : PYTHON FUNCTIONS 

 • Functions basics
 • Function Parameter passing
 • Lambda functions
 • Map, reduce, filter functions

MODULE 1 : OVERVIEW OF STATISTICS 

 • Introduction to 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
 • Types of Sampling
 • Simple Random Sampling
 • Stratified Random Sampling
 • Cluster Random Sampling
 • Systematic Random Sampling
 • Multi stage Sampling
 • 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 & Properties
 • Z Value / Standard Value
 • Empherical Rule and Outliers
 • Central Limit Theorem
 • Normality Testing
 • Skewness & Kurtosis
 • Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
 • Covariance & Correlation

MODULE 4 : HYPOTHESIS TESTING 

 • Hypothesis Testing Introduction
 • P- Value, Critical Region
 • Types of Hypothesis Testing
 • Hypothesis Testing Errors : Type I And Type II
 • Two Sample Independent T-test
 • Two Sample Relation T-test
 • One Way Anova Test
 • Application of Hypothesis testing

MODULE 1: MACHINE LEARNING INTRODUCTION 

 • What Is ML? ML Vs AI
 • Clustering, Classification And Regression
 • Supervised Vs Unsupervised

MODULE 2: PYTHON NUMPY  PACKAGE 

• Introduction to Numpy Package
 • Array as Data Structure
 • Core Numpy functions
 • Matrix Operations, Broadcasting in Arrays

MODULE 3: PYTHON PANDAS PACKAGE

 • Introduction to Pandas package
 • Series in Pandas
 • Data Frame in Pandas
 • File Reading in Pandas
 • Data munging with Pandas

MODULE 4:  VISUALIZATION WITH PYTHON - Matplotlib 

 • Visualization Packages (Matplotlib)
 • Components Of A Plot, Sub-Plots
 • Basic Plots: Line, Bar, Pie, Scatter

MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN

 • Seaborn: Basic Plot
 • Advanced Python Data Visualizations

MODULE 6: ML ALGO: LINEAR REGRESSION

 • Introduction to Linear Regression
 • How it works: Regression and Best Fit Line
 • Modeling and Evaluation in Python

MODULE 7: ML ALGO: LOGISTIC REGRESSION 

 • Introduction to Logistic Regression
 • How it works: Classification & Sigmoid Curve
 • Modeling and Evaluation 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 9: ML ALGO: KNN

 • Introduction to KNN
 • How It Works: Nearest Neighbor Concept
 • Modeling and Evaluation in Python

MODULE 1:  FEATURE ENGINEERING 

 • Introduction to Feature Engineering
 • Feature Engineering Techniques: Encoding, Scaling, Data Transformation
 • Handling Missing values, handling outliers
 • Creation of Pipeline
 • Use case for feature engineering

MODULE 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

 • Introduction to SVM
 • How It Works: SVM Concept, Kernel Trick
 • Modeling and Evaluation of SVM in Python

MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)

 • Building Blocks Of PCA
 • How it works: Finding Principal Components
 • Modeling PCA in Python

MODULE 4: ML ALGO: DECISION TREE 

 • Introduction to Decision Tree & Random Forest
 • How it works
 • Modeling and Evaluation in Python

MODULE 5: ENSEMBLE TECHNIQUES - BAGGING

 • Introduction to Ensemble technique 
 • Bagging and How it works
 • Modeling and Evaluation in Python

MODULE 6: ML ALGO: NAÏVE BAYES

 • Introduction to Naive Bayes
 • How it works: Bayes' Theorem
 • Naive Bayes For Text Classification
 • Modeling and Evaluation in Python

MODULE 7:  GRADIENT BOOSTING, XGBOOST 

 • Introduction to Boosting and XGBoost
 • How it works?
 • Modeling and Evaluation of in Python

MODULE 1: TIME SERIES FORECASTING - ARIMA 

 • What is Time Series?
 • Trend, Seasonality, cyclical and random
 • Stationarity of Time Series
 • Autoregressive Model (AR)
 • Moving Average Model (MA)
 • ARIMA Model
 • Autocorrelation and AIC
 • Time Series Analysis in Python

MODULE 2:  SENTIMENT ANALYSIS

 • Introduction to Sentiment Analysis
 • NLTK Package
 • Case study: Sentiment Analysis on Movie Reviews

MODULE 3:  REGULAR EXPRESSIONS WITH PYTHON 

 • Regex Introduction
 • Regex codes
 • Text extraction with Python Regex

MODULE 4: ML MODEL DEPLOYMENT WITH FLASK 

 • Introduction to Flask
 • URL and App routing
 • Flask application – ML Model deployment

MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL 

 • MS Excel core Functions
 • Advanced Functions (VLOOKUP, INDIRECT..)
 • Linear Regression with EXCEL
 • Data Table
 • Goal Seek Analysis
 • Pivot Table
 • Solving Data Equation with EXCEL

MODULE 6:  AWS CLOUD FOR DATA SCIENCE

 • Introduction of cloud
 • Difference between GCC, Azure,AWS
 • AWS Service ( EC2 instance)

MODULE 7: AZURE FOR DATA SCIENCE

 • Introduction to AZURE ML studio
 • Data Pipeline
 • ML modeling with Azure

MODULE 8: INTRODUCTION TO DEEP LEARNING

 • Introduction to Artificial Neural Network, Architecture
 • Artificial Neural Network in Python
 • Introduction to Convolutional Neural Network, Architecture
 • Convolutional Neural Network in Python

MODULE 1: DATABASE INTRODUCTION

 • DATABASE Overview
 • Key concepts of database management
 • Relational Database Management System
 • CRUD operations

 MODULE 2: SQL BASICS

 • Introduction to Databases
 • Introduction to SQL
 • SQL Commands
 • MY SQL workbench installation

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
• Windows functions: Over, Partition , Rank 

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

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
 • Git Essentials: Copy & User Setup
 • Mastering Git and GitHub

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
• Editing Commits
• Commit command Amend flag
• Git reset and revert

MODULE 5: GIT WITH GITHUB AND BITBUCKET 

• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers

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

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

MODULE 1: TABLEAU FUNDAMENTALS 

 • Introduction to Business Intelligence & Introduction to Tableau
 • Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
 • Bar chart, Tree Map, Line Chart
 • Area chart, Combination Charts, Map
 • Dashboards creation, Quick Filters
 • Create Table Calculations
 • Create Calculated Fields
 • Create Custom Hierarchies

MODULE 2: POWER-BI BASICS 

 • Power BI Introduction 
 • Basics Visualizations
 • Dashboard Creation
 • Basic Data Cleaning
 • Basic DAX FUNCTION

MODULE 3 : DATA TRANSFORMATION TECHNIQUES

 • Exploring Query Editor
 • Data Cleansing and Manipulation:
 • Creating Our Initial Project File
 • Connecting to Our Data Source
 • Editing Rows
 • Changing Data Types
 • Replacing Values

MODULE 4 :  CONNECTING TO VARIOUS DATA SOURCES 

 • Connecting to a CSV File
 • Connecting to a Webpage
 • Extracting Characters
 • Splitting and Merging Columns
 • Creating Conditional Columns
 • Creating Columns from Examples
 • Create Data Model

MODULE 1: NEURAL NETWORKS 

 • Structure of neural networks
 • Neural network - core concepts(Weight initialization)
 • Neural network - core concepts(Optimizer)
 • Neural network - core concepts(Need of activation)
 • Neural network - core concepts(MSE & RMSE)
 • Feed forward algorithm
 • Backpropagation

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 - IMAGE RECOGNITION

• Convolutional neural networks (CNNs)
• CNNs with Keras-part1
• CNNs with Keras-part2
• Transfer learning in CNN
• Flowers dataset with tf2.X(part-1)
• Flowers dataset with tf2.X(part-2)
• Examining x-ray with CNN model

MODULE 4 : DEEP COMPUTER VISION - OBJECT DETECTION

 • What is Object detection
 • Methods of Object Detections
 • Metrics of Object detection
 • Bounding Box regression
 • labelimg
 • RCNN
 • Fast RCNN
 • Faster RCNN
 • SSD
 • YOLO Implementation
 • Object detection using cv2

MODULE 5: RECURRENT NEURAL NETWORK 

• RNN introduction
• Sequences with RNNs
• Long short-term memory networks(part 1)
• Long short-term memory networks(part 2)
• Bi-directional RNN and LSTM
• Examples of RNN applications

MODULE 6: NATURAL LANGUAGE PROCESSING (NLP)

• Introduction to Natural language processing
• Working with Text file
• Working with pdf file
• Introduction to regex
• Regex part 1
• Regex part 2
• Word Embedding
• RNN model creation
• Transformers and BERT
• Introduction to GPT (Generative Pre-trained Transformer)
• State of art NLP and projects

MODULE 7: PROMPT ENGINEERING

• Introduction to Prompt Engineering
• Understanding the Role of Prompts in AI Systems
• Design Principles for Effective Prompts
• Techniques for Generating and Optimizing Prompts
• Applications of Prompt Engineering in Natural Language Processing

MODULE 8: REINFORCEMENT LEARNING

• Markov decision process
• Fundamental equations in RL
• Model-based method
• Dynamic programming model free methods

MODULE 9: DEEP REINFORCEMENT LEARNING

• Architectures of deep Q learning
• Deep Q learning
• Reinforcement Learning Projects with OpenAI Gym

MODULE 10: Gen AI

• Gan introduction, Core Concepts, and Applications
• Core concepts of GAN
• GAN applications
• Building GAN model with TensorFlow 2.X
• Introduction to GPT (Generative Pre-trained Transformer)
• Building a Question answer bot with the models on Hugging Face

MODULE 11: Gen AI

• Introduction to Autoencoder
• Basic Structure and Components of Autoencoders
• Types of Autoencoders: Vanilla, Denoising, Variational, Sparse, and Convolutional Autoencoders
• Training Autoencoders: Loss Functions, Optimization Techniques
• Applications of Autoencoders: Dimensionality Reduction, Anomaly Detection, Image

OFFERED ARTIFICIAL INTELLIGENCE COURSES IN DHANBAD

ARTIFICIAL INTELLIGENCE TRAINING REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING IN DHANBAD

Artificial Intelligence, the cutting-edge field that blends human intelligence with the power of machines, is poised to reshape our world in unimaginable ways. Picture this: a market size that stretches to astonishing heights. In 2022 alone, the Artificial Intelligence market was valued at a jaw-dropping USD 55.1 billion. And hold onto your seats, because the forecast for the future is even more exhilarating. By 2030, experts project that this dynamic industry will surge to an astronomical USD 303.4 billion, experiencing an impressive compound annual growth rate of 27.60%. These numbers speak volumes about the immense potential and thrilling possibilities that lie ahead.

In the heart of Dhanbad, the pioneering DataMites institute offers a transformative Artificial Intelligence Course in Dhanbad that propels students into the realm of limitless opportunities. Designed as an immersive 9-month journey spanning over 780 learning hours, this comprehensive program equips learners with the skills and knowledge needed to excel in the world of AI. The artificial intelligence training in Dhanbad encompasses 100 hours of live online training, 10 capstone projects, and 1 client project, enabling students to apply their newfound expertise in real-world scenarios. With the added advantage of a 365-day Flexi Pass and Cloud Lab, students can dive deep into their AI exploration. DataMites also offers offline courses on demand, providing flexible options to suit every aspiring AI enthusiast in Dhanbad.

DataMites offers a range of specialized courses in Artificial Intelligence, catering to diverse career paths. From the Artificial Intelligence Engineer and Expert programs to the Certified NLP Expert, Artificial Intelligence Foundation, and even an AI course tailored specifically for managers, DataMites ensures that learners have a wide array of options to choose from based on their interests and career goals.

DataMites stands out as the preferred destination for Artificial Intelligence Training Course in Dhanbad for several compelling reasons. 

  • The institute boasts renowned faculty members like Ashok Veda, industry experts who bring a wealth of knowledge and practical experience to the table. 

  • The course curriculum is meticulously designed, covering all essential aspects of AI and keeping up with the latest industry trends. 

  • Successful completion of the program comes with globally recognized certifications from prestigious bodies such as IABAC, NASSCOM FutureSkills Prime, and JainX, boosting the credibility of learners. 

  • Flexible learning options, hands-on projects with real-world data, artificial intelligence internship opportunities, and dedicated placement assistance further enhance the learning experience. 

  • With hardcopy learning materials and exclusive access to the DataMites Learning Community, students have all the resources they need at their fingertips. 

  • Plus, the institute offers affordable pricing options and scholarships to ensure that AI education is accessible to all aspiring learners.

Nestled in the vibrant city of Dhanbad, renowned for its rich coal reserves and thriving educational ecosystem, pursuing an Artificial Intelligence certification becomes an even more enriching experience. Dhanbad provides an ideal backdrop for AI enthusiasts to delve into the world of data analytics and machine learning, with its strong technological infrastructure and an abundance of industrial opportunities. The city's vibrant culture and vibrant academic community add an extra layer of excitement to the AI journey, making it a destination that fosters growth, innovation, and endless possibilities.

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 Dhanbad.

ABOUT ARTIFICIAL INTELLIGENCE COURSE IN DHANBAD

Artificial Intelligence (AI) is the field of computer science focused on creating intelligent machines capable of performing tasks that typically require human intelligence, such as decision-making, problem-solving, language understanding, and pattern recognition.

Examples of artificial intelligence in daily life include virtual assistants like Siri, Alexa, and Google Assistant that use voice recognition and natural language processing. Additionally, personalized recommendations on streaming platforms and e-commerce websites leverage AI algorithms to analyze user preferences and suggest relevant content or products.

A career in artificial intelligence typically requires a strong educational background in computer science, mathematics, or related fields. Having a bachelor's or master's degree in computer science, AI, machine learning, or data science is often preferred. Proficiency in programming languages, algorithms, statistics, and machine learning concepts is also necessary.

Artificial intelligence offers advantages such as automating repetitive tasks, improving efficiency and accuracy in data analysis, enhancing decision-making capabilities, and driving advancements in various industries. However, disadvantages include potential job displacement, ethical concerns related to privacy and bias, and the possibility of AI systems making errors or being susceptible to malicious attacks.

Artificial intelligence is a broader concept encompassing the development of intelligent systems that simulate human-like intelligence. In contrast, machine learning is a subset of AI that focuses on algorithms and models enabling systems to learn from data and make predictions without explicit programming. In simpler terms, machine learning is a technique used to achieve AI.

Typically, pursuing a career in artificial intelligence requires a bachelor's or master's degree in computer science, AI, machine learning, or related fields. It is beneficial to have a solid foundation in programming languages like Python or Java, understanding of algorithms, statistics, and knowledge of machine learning and deep learning concepts.

Starting a career in artificial intelligence with no prior experience can be achieved through self-studying via online courses or tutorials in AI and machine learning. Building an AI project portfolio, participating in Kaggle competitions, gaining practical experience through internships or freelance work, networking with professionals, and joining AI communities provide valuable steps for career entry.

The AI Expert Course in Dhanbad is an advanced-level program that focuses on advanced AI algorithms, cutting-edge research, emerging trends, and complex applications. It may include specialized modules or tracks in areas like 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.

Prominent companies known for hiring professionals in artificial intelligence roles include Google, Microsoft, Amazon, Facebook, IBM, Apple, and Tesla. However, opportunities also exist in various industries like healthcare, finance, and manufacturing, where companies actively seek AI talent.

Transitioning into an artificial intelligence career from a different field can be accomplished 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, networking with professionals, leveraging transferable skills from the previous field, and seeking internships facilitate the transition.

To prepare for AI job interviews and technical assessments, it is important to develop a strong understanding of AI concepts, master programming languages and frameworks, practice implementing AI algorithms and models, stay updated with the latest advancements, solve AI-related coding problems, prepare for technical interview questions, and enhance communication skills.

The future prospects of AI in the job market are promising. AI is in high demand across industries, creating opportunities for AI engineers, data scientists, and AI researchers. As AI continues to advance, it is expected to transform existing job roles and create new ones, leading to significant job market expansion.

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FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN DHANBAD

Obtaining an Artificial Intelligence Certification in Dhanbad is crucial as it validates one's expertise in AI, enhancing employability and professional reputation. The certification demonstrates a commitment to staying updated in the dynamic field of AI.

DataMites is considered the top choice for AI courses in Dhanbad due to its experienced trainers, comprehensive curriculum, hands-on learning approach, flexible scheduling, placement support, and the opportunity to earn recognized certifications.

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

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

The duration of DataMites' Artificial Intelligence course in Dhanbad varies depending on the specific course chosen, with options ranging from one month to one year to accommodate different schedules and preferences.

The objective of DataMites' AI Engineer Course in Dhanbad is to equip individuals with comprehensive knowledge and skills required to become proficient AI engineers. The course covers essential AI concepts, machine learning algorithms, deep learning techniques, natural language processing, computer vision, and AI model deployment through hands-on projects.

To pursue a career as an AI engineer in Dhanbad, individuals should develop a strong foundation in mathematics, computer science, and programming. Enrolling in AI-related courses, gaining hands-on experience through projects and internships, participating in competitions, and staying updated with the latest developments in the field are recommended.

DataMites' Placement Assistance Team supports students in resume preparation, conducts mock interviews, provides interview guidance, and connects them with potential job opportunities in the field of Artificial Intelligence.

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

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

DataMites accepts various payment methods, including online options like credit/debit cards, net banking, and digital wallets, as well as bank transfers or offline payments at their training centers.

The fee for the Artificial Intelligence Training program in Dhanbad at DataMites typically falls within the range of INR 60,795 to INR 154,000.

Yes, upon successful completion of a course with DataMites, participants receive a Course Completion Certificate as evidence of their achievement and completion of the training program.

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

DataMites' Flexi-Pass feature in Dhanbad allows participants to attend training sessions based on their convenience. It offers flexibility in scheduling, with multiple batch options available, enabling individuals to select the schedule that suits their availability and learning preferences.

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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