ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN AFGHANISTAN

Live Virtual

Instructor Led Live Online

AFN 146,030
AFN 94,186

  • 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

AFN 87,240
AFN 56,283

  • 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

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 ONLINE CLASSES IN AFGHANISTAN

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 COURSE

Why DataMites Infographic

SYLLABUS OF ARTIFICIAL INTELLIGENCE COURSE IN AFGHANISTAN

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 AFGHANISTAN

ARTIFICIAL INTELLIGENCE COURSE REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING IN AFGHANISTAN

The Artificial Intelligence course in Afghanistan offers a comprehensive curriculum, equipping students with cutting-edge skills in machine learning, neural networks, and data analysis, fostering a promising future in AI research, development, and industry applications in the region. As per a report from Precedence Research, the worldwide market for artificial intelligence (AI) reached USD 454.12 billion in 2022 and is projected to surge to approximately USD 2,575.16 billion by 2032. This anticipated growth reflects a compound annual growth rate (CAGR) of 19% from 2023 to 2032.

Afghanistan is establishing itself as a dynamic hub for technological progress, notably in the field of AI, garnering global recognition. Enroll in Artificial Intelligence Courses in Afghanistan to acquire vital skills, positioning yourself to actively contribute to the burgeoning AI sector. With a growing demand for AI professionals, seizing this opportunity now will empower you to play a crucial role in shaping the future of AI in the vibrant city of Afghanistan.

DataMites, an internationally renowned training institute, offers a comprehensive array of specialized Artificial Intelligence courses in Afghanistan. Aspiring professionals can choose from programs such as Artificial Intelligence Engineer, Artificial Intelligence Expert, Certified NLP Expert, Artificial Intelligence Foundation, and Artificial Intelligence for Managers, tailored to diverse skill levels and career aspirations.

The Artificial Intelligence training in Afghanistan places a strong emphasis on significant career development, equipping individuals for key roles in designing, implementing, and enhancing AI systems across industries. Graduates gain proficiency in effectively utilizing AI technologies, fostering innovation, and addressing real-world challenges. The program concludes with the prestigious IABAC Certification, validating expertise in this transformative field.

DataMites employs a unique three-phase methodology for delivering its Artificial Intelligence Course in Afghanistan.

In Phase 1 - Preliminary Self-Study
Our program initiates with self-paced learning through high-quality videos, allowing participants to establish a strong foundation in the fundamentals of Artificial Intelligence.

Phase 2 - Interactive Learning Journey and 5-Month Live Training Duration
Participants can choose our online artificial intelligence training in Afghanistan, spanning 120 hours of live online instruction over 9 months. This immersive phase includes a comprehensive curriculum, a rigorous 5-month live training segment, hands-on projects, and guidance from experienced trainers.

Moving on to Phase 3 - Internship and Career Support
This stage provides practical exposure through 20 Capstone Projects and a client project, culminating in a valuable certification in artificial intelligence. Additionally, participants benefit from artificial intelligence courses with internship opportunities in Afghanistan, enriching their overall learning experience.

DataMites delivers a comprehensive and well-structured Artificial Intelligence course in Afghanistan, featuring key components:

Experienced Instructors:
Headed by Ashok Veda, the founder of AI startup Rubixe, with a proven track record of mentoring over 20,000 individuals in data science and AI.

Thorough Curriculum:
Covering essential topics, ensuring a profound understanding of Artificial Intelligence.

Recognized Certifications:
Participants can earn industry-recognized certifications from IABAC, enhancing their credibility.

Course Duration:
A 9-month program requiring a commitment of 20 hours per week, totaling over 780 learning hours.

Flexible Learning:
Students can choose between self-paced learning or engaging in online artificial intelligence training in Afghanistan, accommodating individual schedules.

Real-World Projects:
Practical experience in applying AI concepts is gained through hands-on projects using real-world data.

Internship Opportunities:
DataMites offers Artificial Intelligence training with internship opportunities in Afghanistan, empowering participants to apply their AI skills in real-world scenarios, and acquiring valuable industry experience.

Affordable Pricing and Scholarships:
The cost of the Artificial Intelligence training course in Afghanistan is reasonably priced, ranging from AF 49,509 to AF 135,057. Additionally, there are scholarship opportunities aimed at enhancing the accessibility of education.

Afghanistan, located in South Asia, is known for its rich cultural heritage and diverse landscapes, ranging from rugged mountains to arid deserts. The Afghan economy, heavily reliant on agriculture, faces challenges due to decades of conflict. Efforts for reconstruction and development are underway, with sectors like mining and services showing growth potential.

The future of AI in Afghanistan holds transformative possibilities, driving innovations across industries, enhancing automation, and revolutionizing the way humans interact with technology, promising a landscape of unprecedented efficiency and advancements. As AI continues to evolve, ethical considerations and responsible development are crucial to harness its potential for the benefit of society.

Embark on a journey towards career excellence with DataMites, where an extensive array of courses goes beyond Artificial Intelligence in Afghanistan. Our all-encompassing curriculum spans Python, Data Science, Machine Learning, Data Engineering, Tableau, Blockchain, Data Analytics, MLOps, and others. As a leading institute, we guarantee a comprehensive learning experience, fostering practical skills and providing valuable industry insights. Join DataMites to unlock a myriad of opportunities and elevate your career to unprecedented heights.

ABOUT DATAMITES ARTIFICIAL INTELLIGENCE COURSE IN AFGHANISTAN

Artificial Intelligence (AI) encapsulates the emulation of human cognitive functions by machines, particularly computer systems.

As a subset of AI, Machine Learning operates by training machines to recognize patterns within data, enabling them to make informed decisions or predictions without explicit programming.

In the business domain, AI plays crucial roles such as automation, chatbot-driven customer service, predictive analytics, and personalized marketing strategies, thereby enhancing operational efficiency and decision-making processes.

While AI encompasses a broader spectrum aimed at replicating human intelligence, Machine Learning is a specific technique within AI focused on algorithms learning from data patterns.

Key programming languages essential for AI development include Python, R, Java, and C++. Python particularly stands out for its simplicity and robust libraries tailored for AI endeavours.

While AI may streamline certain tasks, its primary function is to augment human capabilities rather than entirely replacing jobs, leading to shifts in employment roles and skill requirements.

Ethical dilemmas in AI development include algorithmic bias, privacy breaches, and potential societal implications such as job displacement and exacerbation of socioeconomic inequalities.

Risks associated with AI implementation include potential misuse such as deepfake technologies, cybersecurity vulnerabilities, and unintended consequences arising from biased or inadequately designed algorithms.

The core responsibilities of an AI engineer include developing AI models, ensuring data integrity, refining algorithms, and collaborating with multidisciplinary teams.

Top-paying roles in AI include machine learning engineering, data science, AI research, and AI architecture, with salary variations based on experience and geographic location.

Firms seeking AI talent range from tech giants like Google, Microsoft, and Amazon to startups, research institutions, and companies across diverse sectors eager to integrate AI.

In Afghanistan, individuals can acquire AI skills through online courses, university programs, or specialized training offered by tech organizations and educational institutions.

Prerequisites for AI positions in Afghanistan typically include degrees in computer science, mathematics, or related fields, along with strong programming skills and prior involvement in AI projects.

In-demand skills for AI roles in Afghanistan include proficiency in Python, familiarity with machine learning algorithms, strong data analysis capabilities, and effective problem-solving skills.

While certifications can enhance credibility and validate skills, practical experience and tangible project portfolios often carry more weight in securing AI positions in Afghanistan.

Aspiring AI engineers in Afghanistan can pursue relevant skills through education, hands-on projects, and active engagement with the local AI community.

The job market for AI professionals in Afghanistan is burgeoning, with increasing demand across sectors such as finance, healthcare, and emerging technology startups.

Transitioning into AI from another field is feasible with a dedicated focus on acquiring relevant skills and building a strong portfolio demonstrating proficiency in AI.

Entry-level AI roles suitable for beginners include positions like AI research assistants, data analysts, or junior machine learning engineers, emphasizing skill development and professional growth.

Within healthcare settings, AI is utilized for tasks such as analyzing medical images, discovering new drugs, personalizing treatment plans, and streamlining administrative processes to enhance diagnostic accuracy and patient outcomes.

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FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN AFGHANISTAN

DataMites offers a suite of AI certifications in Afghanistan, including Artificial Intelligence Engineering, AI Expertise, Certified NLP Expertise, AI Management, and AI Foundations, providing comprehensive training in various AI technologies and their real-world applications.

Eligibility for DataMites' AI training in Afghanistan is open to individuals from diverse backgrounds such as computer science, engineering, mathematics, and statistics, fostering inclusivity and welcoming anyone with an interest in AI to enrol and excel.

The duration of DataMites' Artificial Intelligence Course in Afghanistan varies based on the selected program, ranging from one to nine months, with flexible scheduling options including weekday and weekend classes to accommodate participants' availability.

Proficiency in AI in Afghanistan can be attained through enrollment with DataMites, an internationally recognized training institute specializing in data science and AI, offering a comprehensive curriculum and hands-on learning opportunities led by industry professionals.

DataMites' AI Course in Afghanistan provides a solid foundation in AI fundamentals, machine learning, and practical applications, emphasizing hands-on learning and led by industry experts to effectively apply AI principles across industries.

DataMites in Afghanistan accepts various payment methods including cash, debit/credit cards, checks, EMI, PayPal, and net banking, ensuring convenience for participants.

Yes, DataMites in Afghanistan provides hands-on experience through 10 Capstone projects and 1 Client Project as part of its AI course, allowing participants to apply their learning in practical scenarios.

Indeed, assistance sessions are available in Afghanistan to support participants in enhancing their comprehension of AI topics, providing additional guidance and clarification when needed.

DataMites adopts a case-study-driven approach to AI training in Afghanistan, with a curriculum designed by expert content teams to meet industry demands, ensuring a career-oriented educational experience.

Enrolling in DataMites' online AI training in Afghanistan provides access to expert-led instruction, flexible learning options, practical experience, industry-recognized certification, career guidance, and a supportive learning community.

The fee for AI Training in Afghanistan through DataMites varies depending on factors like the chosen course, duration, and additional features included, ranging from AF 49,509 to AF 135,057.

AI training sessions at DataMites Afghanistan are led by Ashok Veda, a distinguished Data Science coach and AI Expert, supported by elite mentors with practical experience from leading companies and esteemed institutions, ensuring top-notch guidance throughout the program.

The Flexi-Pass option for AI training in Afghanistan offers flexible learning choices, allowing students to tailor their schedules to individual preferences and commitments, enhancing the overall educational experience with access to various resources and mentorship.

Upon completion of AI training at DataMites Afghanistan, participants receive IABAC Certification, recognized within the EU framework, adhering to industry standards and globally accredited by IABAC, ensuring recognition in the field of Artificial Intelligence.

Participants attending AI training in Afghanistan are required to present a valid photo ID, such as a national ID card or driver's license, to obtain the participation certificate and schedule certification exams.

In case of missing an AI session in Afghanistan, participants can utilize recorded sessions or seek mentor guidance to catch up, ensuring continuous progress despite occasional absences.

Individuals in Afghanistan have the opportunity to attend a demo class for AI courses, enabling them to assess the program's suitability before making any payment, ensuring an informed decision.

Yes, DataMites provides AI Courses in Afghanistan with internship opportunities in select industries, offering practical experience in Analytics, Data Science, and AI roles, enhancing prospects for career advancement.

DataMites' Placement Assistance Team (PAT) organizes career mentoring sessions in Afghanistan, offering guidance on various career paths in Data Science and AI, including insights into industry challenges and strategies for career growth and development.

The AI Foundation Course caters to beginners, providing a comprehensive understanding of AI principles, practical applications, and real-world examples, accommodating individuals with varying levels of technical expertise and covering essential topics like machine learning, deep learning, and neural networks.

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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