ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE COURSE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN KHARADI, PUNE

Live Virtual

Instructor Led Live Online

76,000
49,098

  • 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

46,000
29,623

  • 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

76,000
56,273

  • 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 KHARADI

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 FOR ARTIFICIAL INTELLIGENCE TRAINING

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SYLLABUS OF ARTIFICIAL INTELLIGENCE CERTIFICATION COURSE

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 KHARADI

ARTIFICIAL INTELLIGENCE TRAINING COURSE REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE COURSE IN KHARADI

The Artificial Intelligence course in Kharadi offers comprehensive training in cutting-edge AI technologies, preparing students for diverse career opportunities in fields such as machine learning, data science, and AI development. The global artificial intelligence market achieved a valuation of USD 136.55 billion in 2022 and is expected to undergo a compound annual growth rate (CAGR) of 37.3% from 2023 to 2030, as indicated by findings from a report by Grand View Research. Additionally, the salary of an artificial intelligence engineer in Pune ranges from INR 1,000,000 per year according to a Glassdoor report

DataMites presents a varied selection of specialized Artificial Intelligence courses in Kharadi, offering aspiring professionals opportunities such as Artificial Intelligence Engineer, Artificial Intelligence Expert, Certified NLP Expert, Artificial Intelligence Foundation, and Artificial Intelligence for Managers. These programs, tailored to varying skill levels and career goals, empower individuals to explore specific AI domains under their interests.

DataMites presents its Artificial Intelligence Course in Kharadi with distinctive features:

Experienced Instructors: Headed by Ashok Veda, the founder of the AI startup Rubixe, our faculty, boasting a track record of mentoring over 20,000 individuals in data science and AI, ensures expert guidance.

Comprehensive Curriculum: Our AI courses cover essential topics, delivering a thorough understanding of the subject matter.

Recognized Certifications: Acquire industry-recognized certifications from IABAC and NASSCOM FutureSkills, bolstering your professional credibility.

Flexible Learning Options: Choose from live online classes, self-paced learning, or offline Artificial Intelligence training in Kharadi to suit your schedule.

Real-World Projects: Apply theoretical knowledge to practical scenarios through hands-on projects utilizing real-world data.

Internship Opportunities: Gain valuable industry experience by applying your skills in real-world situations through our AI internships.

Placement Support: Receive dedicated guidance, support, and job references from our team to jumpstart your AI career.

Learning Materials: Access hardcopy learning materials and books for continuous reference throughout your AI learning journey.

Affordable Pricing and Scholarships: Access quality AI education at reasonable prices, with scholarships available for eligible candidates.

Kharadi, located in Pune, India, is a rapidly growing IT and business hub, known for its modern infrastructure, residential developments, and burgeoning commercial activities. The demand for artificial intelligence in Kharadi is on the rise, driven by the burgeoning IT sector and a growing emphasis on innovative technologies, creating opportunities for AI applications across various industries and businesses in the region. As organizations increasingly recognize the transformative potential of AI, the demand for skilled AI professionals continues to grow in Kharadi. Initiate your journey into an AI career by enrolling in DataMites AI training in Kharadi, a crucial stride toward attaining success in this dynamic field.

ABOUT DATAMITES ARTIFICIAL INTELLIGENCE COURSE IN KHARADI

Artificial Intelligence (AI) involves the creation of computer systems capable of performing tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, and perception. The field focuses on developing algorithms and models that enable machines to simulate intelligent behaviour.

John McCarthy, an American computer scientist, is widely considered the father of AI. He played a crucial role in establishing the field, coining the term "Artificial Intelligence." Other key contributors include Alan Turing, Marvin Minsky, Allen Newell, and Herbert A.

An AI engineering course covers fundamental principles, tools, and methods for designing AI systems. It delves into topics such as machine learning, deep learning, data preprocessing, model assessment, and deployment. Students also acquire proficiency in Python programming and hands-on skills for implementing AI algorithms and constructing models.

Leading companies actively hiring for AI positions include industry giants like Google, Microsoft, Amazon, Facebook, IBM, Apple, and NVIDIA. Moreover, organizations in diverse sectors such as healthcare, finance, automotive, and e-commerce actively recruit individuals with AI expertise.

Contrary to common misconceptions, learning AI is not inherently challenging. However, a solid foundation in programming, mathematics, and statistics is crucial for understanding key concepts. This foundation enables individuals to analyze data, formulate effective algorithms, and implement AI models successfully.

The need for AI professionals is substantial, as evidenced by the U.S. Bureau of Labor Statistics projecting a 15% growth in the computer and information technology sector, which encompasses AI roles, from 2021 to 2031.

Essential skills for AI professionals include proficiency in programming, familiarity with libraries and frameworks, a strong foundation in mathematics and statistics, expertise in machine learning, deep learning, natural language processing, computer vision, data science, data analysis, and the development of soft skills.

Given ongoing technological advancements, AI is expected to play an increasingly integral role in various industries, revolutionizing sectors such as healthcare, banking, and transportation. The widespread adoption of AI-driven automation is anticipated to reshape the labour market, necessitating the acquisition of new roles and additional skill sets.

The prevalent classification system identifies four primary types of AI: reactive, limited memory, theory of mind, and self-aware. Each category signifies different levels of AI capabilities.

ChatGPT is indeed an AI chatbot that employs natural language processing to generate conversational responses resembling human interaction. This sophisticated language model can effectively address inquiries and generate diverse written content, including articles, social media posts, essays, code, and emails.

Contemporary AI applications showcase machines that can gain knowledge through experience, adapt to new inputs, and execute tasks mirroring human capabilities. Examples encompass computers engaging in chess matches to autonomous vehicles leveraging technologies such as deep learning and natural language processing.

AI finds application across diverse industries, including healthcare for tasks like drug discovery, disease diagnosis, and personalized patient care. In the financial sector, AI contributes to functions such as fraud detection, risk management, and providing investment guidance.

Google integrates AI into various applications, such as Google Maps, where it analyzes real-time traffic data to assist users in navigating routes efficiently. Moreover, AI plays a role in automatically updating information like business hours and speed limits, ensuring users have current and relevant details about their surroundings.

The salary of an artificial intelligence engineer in Pune ranges from INR 1,000,000 per year according to a Glassdoor report

To pursue a career as an AI engineer, a foundational requirement is a bachelor's degree in a relevant field such as information technology, computer science, statistics, or data science. Further specialization in AI can be achieved through postgraduate studies.

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FAQ'S OF ARTIFICIAL INTELLIGENCE TRAINING IN KHARADI

DataMites provides a range of Artificial Intelligence certifications, including those for an Artificial Intelligence Engineer, Artificial Intelligence Expert, Certified NLP Expert, Artificial Intelligence for Managers, and Artificial Intelligence Foundation.

The Artificial Intelligence course in Kharadi at DataMites has a duration of approximately 11 months, consisting of 780 learning hours and 100 hours of live online training.

To acquire proficiency in Artificial Intelligence, individuals can enrol in the Artificial Intelligence Course in Kharadi offered by DataMites. This comprehensive course provides the necessary knowledge and skills for future career opportunities in the field of AI.

The AI Engineer course is designed to equip participants with the skills needed to develop intelligent algorithms using techniques like deep learning, machine learning, computer vision, and natural language processing. The course aims to prepare individuals for successful careers as AI Engineers in the realm of artificial intelligence.

The Certified NLP Expert course delves into the development and application of natural language processing skills in practical situations. It explores various methods and approaches to effectively harness the potential of Natural Language Processing.

DataMites in Kharadi distinguishes itself with its globally recognized AI courses, featuring experienced trainers and hands-on projects for a comprehensive and industry-relevant education in artificial intelligence. The institute's dedication to quality education and flexible learning options positions it as the top selection for AI enthusiasts in Kharadi.

The Artificial Intelligence for Managers Course is tailored for executives and managers aiming to harness AI knowledge within their organizations. It provides insights into the practical application and potential impact of AI across different organizational levels.

The AI Foundation Course serves as an introductory program, offering a thorough understanding of AI concepts, including machine learning, deep learning, and neural networks. It caters to individuals with or without technical backgrounds.

DataMites has earned global recognition for its AI training and holds accreditation from the International Association of Business Analytics Certification (IABAC). The institute offers a comprehensive three-step learning approach, incorporating self-study materials, live online training, and real-world projects, with opportunities for certification and internships upon completion.

The fee for Artificial Intelligence Training in Kharadi at DataMites varies from INR 59,348 to INR 154,000, depending on the specific course and mode of training.

Certainly, DataMites provides offline Artificial Intelligence training in Kharadi, and arrangements can be made for sessions in other locations based on demand and candidate availability.

Trainers at DataMites are certified professionals with extensive industry experience and a high level of expertise in the field of Artificial Intelligence.

The Flexi-Pass option at DataMites allows individuals the flexibility to attend sessions for queries or revisions for up to 3 months after completing the training.

Yes, upon successful completion of the Artificial Intelligence training in Kharadi, DataMites awards an IABAC certification, which holds global recognition.

DataMites provides a Course Completion Certificate to individuals who successfully finish the AI training course.

Certainly, DataMites offers a complimentary demo class to provide individuals with a preview of the training content before any financial commitment is made.

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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