ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN EGYPT

Live Virtual

Instructor Led Live Online

EGP 107,440
EGP 69,289

  • 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

EGP 64,190
EGP 41,409

  • 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 EGYPT

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 EGYPT

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 EGYPT

ARTIFICIAL INTELLIGENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN EGYPT

Artificial Intelligence (AI) is a field experiencing significant global expansion. As of 2022, the global AI market achieved a substantial value of USD 136.55 billion, with an anticipated compound annual growth rate (CAGR) of 37.3% from 2023 to 2030, according to Grand View Research. Within Egypt, the AI sector is gaining momentum, providing a fertile ground for individuals seeking to leverage the potential of this advanced technology. Our meticulously designed artificial intelligence courses in Egypt offer a comprehensive understanding of the industry, enabling participants to navigate and contribute effectively to Egypt's dynamic AI landscape.

DataMites stands as a distinguished institute, recognized globally for its expertise in AI and data science training. As a leading global training institute for Artificial Intelligence, we offer the comprehensive Artificial Intelligence Engineer Course in Egypt. Tailored for intermediate and expert learners in the AI field, this career-oriented program equips individuals to actively contribute to the development, deployment, and optimization of AI systems across diverse industries. Graduates emerge proficient in leveraging AI technologies to drive innovation and address real-world challenges. The program also includes IABAC Certification, enhancing the professional credentials of participants.

At DataMites, our comprehensive Artificial Intelligence Engineer Training in Egypt unfolds in three distinct phases to ensure a thorough and effective learning journey, perfectly tailored for the burgeoning AI landscape in Egypt.

Phase 1: Pre Course Self-Study

Before commencing the formal training, participants engage in self-study through high-quality videos employing an easily digestible learning approach.

Phase 2: 5-Month Duration Live Training

This intensive phase spans 5 months, with participants dedicating 20 hours a week to live training. The curriculum encompasses a comprehensive syllabus, featuring hands-on projects guided by expert trainers and mentors.

Phase 3: 4-Month Duration Project Mentoring

The final phase involves 4 months of project mentoring, offering participants an opportunity to work on 10+ capstone projects, engage in real-time internships, and collaborate on a live client project, ensuring a well-rounded and practical learning experience.

At DataMites, we take pride in presenting a truly captivating learning experience tailored for the Egyptian audience.

Artificial Intelligence Courses in Egypt - Highlights

Ashok Veda and Faculty:

Led by Ashok Veda, a seasoned professional with over 19 years of experience in Data Analytics and AI, DataMites offers top-tier education. Ashok Veda, also the Founder & CEO at Rubixe™, exemplifies expertise in the realm of Data Analytics and AI.

Course Curriculum:

Our meticulously designed artificial intelligence course curriculum aims to establish a robust foundation in core areas of machine learning and AI, encompassing python, statistics, machine learning, visual analytics, deep learning, computer vision, and natural language processing.

Course Duration:

Embark on a 9-month program, dedicating 20 hours per week, accumulating over 400 learning hours. The commitment to quality education is reflected in our structured learning approach.

Global Certification:

Attain the prestigious IABAC® Certification, validating your expertise and enhancing professional credentials.

Flexible Learning:

Benefit from flexible learning options with our online artificial intelligence courses in Egypt and self-study modules.

Projects and Internship Opportunity:

Immerse yourself in theoretical concepts and practical applications through hands-on experience with popular tools and frameworks. DataMites' exclusive partnerships with leading AI companies provide artificial intelligence courses with internship in Egypt for learners, offering real-world exposure.

Career Guidance and Job References:

Navigate your career path seamlessly with end-to-end job support, personalized resume and artificial intelligence interview preparation, and continuous updates on job opportunities. Our exclusive online learning community, featuring thousands of active learners, mentors, and alumni, serves as a valuable resource for clarifying doubts and mentorship.

Affordable Pricing and Scholarships:

Experience quality education at affordable pricing, with artificial intelligence course fees in Egypt ranging from EGP 22,088 to EGP 57,316. Explore scholarship options to make your educational journey even more accessible.

The Artificial Intelligence Market in Egypt is experiencing unprecedented growth, with numerous industries embracing AI technologies for enhanced efficiency and innovation. From healthcare to finance, businesses across sectors are integrating AI solutions to stay competitive in the dynamic market landscape.

AI Engineers in Egypt are highly valued, commanding an average annual salary of EGP 284,423, according to the Economic Research Institute. This lucrative compensation reflects the industry's recognition of the critical role AI engineers play in driving technological advancements and solving complex challenges. The demand for skilled professionals underscores the importance placed on their expertise, positioning AI engineers as highly paid contributors in Egypt's evolving technological landscape.

In Egypt, DataMites emerges as the paramount choice for aspirants seeking to excel in Artificial Intelligence. Beyond our acclaimed Artificial Intelligence Training in Egypt, we offer a spectrum of courses, including Python, Data Science, Machine Learning, Data Engineering, Tableau, Blockchain, Data Analytics, MLOps, and more. These courses, meticulously designed and led by industry experts, pave the way for comprehensive skill development. Choose DataMites as your partner in career success, propelling you into a realm of opportunities and professional growth. Elevate your skills, redefine your career, and chart a successful trajectory with DataMites.

ABOUT DATAMITES ARTIFICIAL INTELLIGENCE COURSE IN EGYPT

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to mimic human actions and cognitive functions such as learning, problem-solving, and decision-making.

AI applications in business include customer service chatbots, predictive analytics for marketing, supply chain optimization, fraud detection, and personalized recommendation systems, enhancing efficiency and decision-making.

AI encompasses various technologies that enable machines to simulate human intelligence, while Machine Learning is a subset of AI focused on enabling machines to learn from data without explicit programming.

Machine Learning is a subset of AI that involves teaching computers to learn from data patterns and make decisions without being explicitly programmed. Algorithms analyze large datasets to identify patterns and make predictions or decisions based on that analysis.

While AI may automate certain tasks, it's unlikely to replace humans entirely. Instead, it's expected to augment human capabilities, create new job roles, and increase productivity in various industries.

Yes, ethical concerns in AI development include issues of bias in algorithms, privacy concerns with data collection, job displacement, and the potential for AI to be used in harmful ways such as surveillance or autonomous weapons.

Risks of AI include job displacement, loss of privacy, algorithmic bias leading to unfair decisions, cybersecurity threats, and the potential for AI systems to malfunction or be manipulated.

Common programming languages for AI development include Python, R, Java, and C++. Python is particularly popular due to its simplicity, versatility, and extensive libraries for AI and Machine Learning.

High-paying jobs in AI include roles such as AI research scientists, machine learning engineers, data scientists, and AI solution architects, often in industries like technology, finance, healthcare, and research.

Tech giants like Google, Amazon, Microsoft, and Facebook, as well as companies in industries like finance, healthcare, automotive, and retail, are actively hiring AI professionals for various roles.

Individuals in Egypt can learn AI through online artificial intelligence courses, bootcamps, university programs, or self-study using resources like books, tutorials, and coding platforms. Joining AI communities and attending workshops can also be beneficial.

Key responsibilities of an AI engineer include designing and developing AI models, analyzing data, optimizing algorithms, implementing AI solutions, and ensuring the ethical and responsible use of AI technologies.

In-demand skills for AI careers in Egypt include proficiency in programming languages (especially Python), expertise in machine learning algorithms and frameworks, data analysis skills, and knowledge of AI applications in specific industries.

Certifications in AI can demonstrate proficiency and credibility to potential employers, although they are not always mandatory. Relevant certifications include those from reputable organizations like Google, Microsoft, and IBM, as well as specialized AI training providers.

To become an AI Engineer in Egypt, individuals should acquire relevant education, gain practical experience through projects and internships, develop strong programming and analytical skills, stay updated on AI advancements, and build a portfolio showcasing their AI expertise.

The job market for AI professionals in Egypt is growing, with increasing demand for skilled individuals in industries such as technology, finance, healthcare, and government. Job opportunities exist for various roles, from entry-level positions to senior leadership roles.

In Egypt, AI Engineers are esteemed, with an average annual salary of EGP 284,423, as reported by the Economic Research Institute, reflecting the high value placed on their expertise in the field.

Yes, individuals from diverse backgrounds can transition to AI careers by acquiring relevant skills through training, self-study, or bootcamps, leveraging transferable skills from their previous roles, and building a strong portfolio to showcase their AI expertise.

Yes, entry-level AI jobs for beginners include roles such as AI/ML interns, data analysts, junior data scientists, and AI software engineers, which may require foundational knowledge in AI and programming languages.

AI is used in healthcare for tasks such as medical image analysis, disease diagnosis and prediction, drug discovery, personalized treatment planning, patient monitoring, and administrative tasks, improving efficiency, accuracy, and patient outcomes.

Qualifications for AI jobs in Egypt typically include a degree in computer science, data science, or a related field, along with proficiency in programming languages like Python, experience with AI frameworks and tools, and strong problem-solving skills.

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FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN EGYPT

DataMites offers several AI certifications in Egypt, including Artificial Intelligence Engineer, Artificial Intelligence Expert, Certified NLP Expert, Artificial Intelligence for Managers, and Artificial Intelligence Foundation courses, providing comprehensive training and certification in various aspects of AI technologies and applications.

The length of the Artificial Intelligence Course in Egypt varies based on the specific program chosen, ranging from 1 month to 9 months. Flexible training schedules are available on both weekdays and weekends to accommodate diverse participant availability.

Consider joining DataMites, a renowned global training institute specializing in data science and artificial intelligence, providing comprehensive learning opportunities for aspiring AI enthusiasts.

Opting for DataMites Artificial Intelligence Expert Training in Egypt offers a condensed 3-month program ideal for intermediate and expert learners. This career-centric curriculum delves into core AI concepts, computer vision, natural language processing, and foundational knowledge in general AI, ensuring advanced expertise.

Eligibility for DataMites Artificial Intelligence Courses in Egypt varies. While individuals with backgrounds in computer science, engineering, mathematics, or statistics are typically eligible, those from non-technical fields have also successfully transitioned. DataMites welcomes anyone interested in AI, providing opportunities for diverse backgrounds to learn and excel in artificial intelligence training in Egypt.

Benefit from expert-led instruction, flexible learning choices, hands-on experience, and industry-recognized IABAC certification. With a comprehensive curriculum covering machine learning and deep learning, acquire practical skills for real-world application. Enjoy a supportive learning environment and career guidance.

The fee for Artificial Intelligence Training in Egypt at DataMites ranges from EGP 22,088 to EGP 57,316. Prices may vary based on the specific course, duration, and any additional features or materials included in the training program.

At DataMites Egypt, artificial intelligence training is led by Ashok Veda, a renowned Data Science coach and AI Expert, alongside elite mentors and faculty members. They bring real-time experience from esteemed companies and institutions like IIMs, ensuring top-quality mentorship.

Flexi-Pass in AI training in Egypt offers flexible learning options, allowing students to customize their schedules. It grants access to diverse learning resources and mentorship, accommodating varying learning paces and individual commitments for an enriched educational experience.

AI Engineer Course in Egypt is an intensive 9-month program catering to intermediate and expert learners, providing career-oriented training in machine learning and AI essentials. Covering Python, statistics, visual analytics, deep learning, computer vision, and natural language processing, it fosters a robust foundation in AI domains.

Yes, apart from the IABAC Certification, DataMites in Egypt offers a course completion certificate to acknowledge your successful culmination of the Artificial Intelligence course.

Participants must bring a valid photo ID, such as a national ID card or driver's license, to AI training sessions in Egypt. This is essential for receiving the participation certificate and scheduling certification exams.

If unable to attend an AI session in Egypt, you can catch up through recorded sessions or seek guidance from mentors. Flexibility ensures you maintain progress despite occasional absences.

Certainly, you can attend a demo class for artificial intelligence courses in Egypt before making any payment. It allows you to assess the program suitability firsthand.

Yes, DataMites provides Artificial Intelligence Courses in Egypt with Internships in selected industries. These internships offer hands-on experience in Analytics, Data Science, and AI roles, enhancing career progression.

Upon completing AI training at DataMites Egypt, you receive IABAC Certification, recognized within the EU framework. The syllabus aligns with industry standards, accredited globally by IABAC, ensuring you gain credentials acknowledged in the field of Artificial Intelligence.

Artificial intelligence training at DataMites in Egypt employs a case study-based approach. The curriculum, crafted by an expert content team, is tailored to industry demands, ensuring a job-oriented learning experience for participants.

Various payment methods are available at DataMites in Egypt for artificial intelligence course training, including cash, debit/credit cards (Visa, Mastercard, American Express), checks, EMI, PayPal, and net banking.

Yes, DataMites in Egypt offers 10 Capstone projects and 1 Client Project as part of the artificial intelligence course, providing hands-on experience for practical learning.

Yes, you can attend help sessions in Egypt to gain a better grasp of artificial intelligence topics. These sessions provide additional support and clarification for improved comprehension.

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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