ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN KENYA

Live Virtual

Instructor Led Live Online

KES 220,000
KES 141,890

  • 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

KES 131,430
KES 84,777

  • 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 KENYA

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 KENYA

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 KENYA

ARTIFICIAL INTELLIGENCE COURSE REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE TRAINING IN KENYA

Amidst the dynamic landscape of Artificial Intelligence (AI), the global market is on an upward trajectory, projecting a remarkable compound annual growth rate (CAGR) of 37.3% from 2023 to 2030. GrandViewResearch foresees the market's value reaching an impressive $1,811.8 billion by 2030. In Kenya, this technological wave is gaining prominence, offering immense potential for growth and innovation within the local AI industry. As AI continues to reshape industries globally, aspiring professionals in Kenya are presented with a compelling opportunity to learn and harness the power of Artificial Intelligence for transformative advancements.

DataMites stands as a premier institute, globally recognized for its comprehensive training in AI and data science. At DataMites, we offer the Artificial Intelligence Engineer Course in Kenya, specifically tailored for intermediate and expert learners in the AI domain. This career-oriented program equips individuals with the skills to actively contribute to the development, deployment, and optimization of AI systems across diverse industries. With a focus on practical applications, our training makes participants proficient in leveraging AI technologies to drive innovation and tackle real-world challenges. Upon successful completion, learners are awarded the esteemed IABAC Certification, a testament to their expertise in Artificial Intelligence.

At DataMites, our artificial intelligence engineer training in Kenya unfolds in three meticulously designed phases to ensure a comprehensive learning experience. In Phase 1, participants embark on a pre-course self-study journey, enriched with high-quality videos featuring an easy learning approach. Transitioning to Phase 2, a five-month live training program awaits, demanding 20 hours per week commitment. This phase features a comprehensive syllabus, hands-on projects, and the guidance of expert trainers and mentors. The culmination of training occurs in Phase 3, spanning four months, focusing on project mentoring, including 10+ capstone projects, real-time internships, and engagement in a live client project. Each phase is tailored to cultivate expertise and practical skills for successful integration into the world of Artificial Intelligence.

Artificial Intelligence Courses in Kenya - Highlights

Ashok Veda and Faculty:

At DataMites, excellence is not just a goal; it's embodied in our lead instructor, Ashok Veda. With over 19 years of rich experience in Data Analytics and serving as the Founder & CEO at Rubixe™, his leadership ensures top-tier education in the realms of Data Analytics and AI.

Course Curriculum:

Embark on a transformative learning journey with our meticulously designed course curriculum. Spanning nine months, our program demands a commitment of 20 hours per week, accumulating to over 400 learning hours. Dive deep into core areas such as Python, Statistics, Machine Learning, Visual Analytics, ML, Deep Learning, Computer Vision, and Natural Language Processing.

Global Certification:

Elevate your credentials with the esteemed IABAC® Certification, globally recognized as a testament to your proficiency in Artificial Intelligence.

Flexible Learning:

Tailor your learning experience with our flexible online artificial intelligence courses in Kenya and self-study options, accommodating diverse schedules and preferences.

Projects and Internship Opportunity:

Immerse yourself in the practical applications of AI through 10+ capstone projects and a live client project. Our exclusive partnerships with leading AI companies offer artificial intelligence courses with internship in Kenya, providing real-world exposure and hands-on experience with popular tools and frameworks.

Career Guidance and Job Support:

DataMites goes beyond education; we provide end-to-end job support, personalized resume crafting, artificial intelligence interview preparation, job updates, and connections within our exclusive online learning community.

Affordable Pricing and Scholarships:

Unlock the doors to AI Training Fee in Kenya with affordable pricing, ranging from KES 114,198 to KES 296,330. DataMites is committed to making quality education accessible, offering scholarships to deserving candidates. Join us in shaping a successful AI career without financial constraints.

Kenya's Artificial Intelligence (AI) sector is undergoing a transformative phase, witnessing rapid advancements and widespread adoption. As businesses embrace AI solutions, the industry plays a pivotal role in driving innovation, efficiency, and technological progress across diverse domains.

Artificial Intelligence Engineers in Kenya enjoy lucrative compensation, with an average annual salary of 2,190,000 KES, as reported by Salary Explorer. This substantial income reflects the industry's recognition of the invaluable skills possessed by AI professionals. The robust salary structure underscores the high demand for their expertise, positioning AI Engineers among the highly paid professionals in Kenya's dynamic and evolving job market.

Our comprehensive Artifiical Intelligence Courses in Kenya, led by industry stalwart Ashok Veda, guarantees expertise in cutting-edge technologies. Beyond AI, explore diverse career avenues with our range of courses—Python, Data Science, Machine Learning, Data Engineering, Tableau, Blockchain, Data Analytics, MLOps, and more. At DataMites, we sculpt success stories, providing not just education but a pathway to unparalleled career growth. Join us, and empower your future with skills that redefine industries and position you at the forefront of innovation.

ABOUT DATAMITES ARTIFICIAL INTELLIGENCE COURSE IN KENYA

AI involves programming machines to replicate human intelligence, including learning, reasoning, and problem-solving capabilities, enabling autonomous task performance.

AI engineers are tasked with developing and implementing AI models, analyzing data for insights, and optimizing systems for better performance across various domains.

High-paying roles in AI include AI research scientists, machine learning engineers, and AI consultants, reflecting the demand for specialized expertise.

Leading tech companies like Google, Facebook, Amazon, and Microsoft, along with startups, are actively recruiting AI professionals to drive innovation in AI-driven products and services.

Individuals in Kenya can learn AI through online artificial intelligence courses, workshops, university programs, and participation in AI communities, fostering continuous skill development.

AI jobs in Kenya typically require a strong educational background in computer science or related fields, proficiency in programming languages like Python, and experience in machine learning algorithms.

Artificial Intelligence Engineers in Kenya enjoy lucrative compensation, with an average annual salary of 2,190,000 KES, as reported by Salary Explorer

In Kenya, AI professionals with expertise in machine learning, deep learning, natural language processing, and problem-solving abilities are highly sought after.

While artificial intelligence certifications can enhance credentials, practical experience and demonstrated skills are often more crucial for AI careers in Kenya.

To become an AI engineer in Kenya, individuals should pursue relevant education, gain practical experience, update skills with the latest AI advancements, and engage with the AI community.

Artificial Intelligence includes narrow AI, designed for specific tasks, and general AI, which exhibits human-like intelligence across various domains.

AI applications in daily life include virtual assistants, recommendation systems, predictive text input, and spam filters, among others.

In finance, AI is used for fraud detection, algorithmic trading, credit scoring, customer service chatbots, risk assessment, and portfolio management.

Emerging AI applications include healthcare diagnostics, autonomous vehicles, personalized medicine, smart city solutions, and robotics.

AI is applied in manufacturing for predictive maintenance, quality control, supply chain optimization, robotic automation, and autonomous systems.

AI teams typically comprise roles such as AI researchers, data scientists, machine learning engineers, software developers, project managers, and domain experts.

Preparation involves reviewing fundamental concepts, practicing coding exercises, engaging in case studies, and staying updated on AI developments.

DataMites is a reputable institution offering comprehensive artificial courses in Kenya, known for its quality curriculum and experienced instructors.

Misconceptions include fears of job displacement, concerns about uncontrollable AI systems, and misconceptions about AI possessing human-like consciousness or emotions.

Challenges include data privacy, ethical considerations, regulatory frameworks, resource allocation, and transparency in decision-making.

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FAQ’S OF ARTIFICIAL INTELLIGENCE TRAINING IN KENYA

DataMites in Kenya provides diverse AI certifications such as Artificial Intelligence Engineer, Expert, and Certified NLP Expert. Furthermore, they offer specialized courses like AI for Managers, catering to managerial roles. Their Foundation program targets beginners, laying a strong groundwork in AI principles and techniques to kickstart a rewarding journey in the field.

Tailored for executives and managers, the Artificial Intelligence for Managers Course in Kenya delivers essential AI knowledge. It enables leaders to grasp AI's employability and influence across organizational tiers, empowering them to make strategic decisions and harness AI's transformative potential for sustainable growth and competitive advantage in their respective industries.

Eligibility for DataMites' AI training in Kenya encompasses individuals with backgrounds in computer science, engineering, mathematics, or related fields. Moreover, the program welcomes candidates from non-technical backgrounds, fostering an inclusive learning environment. This inclusive policy enables anyone with a passion for AI to embark on a fulfilling educational journey with DataMites.

Master artificial intelligence in Kenya by enrolling with DataMites, a distinguished global training institute specializing in data science and artificial intelligence education.

Enrolling in DataMites' Artificial Intelligence Expert Training in Kenya offers a 3-month intensive program tailored for intermediate and advanced learners. Through in-depth modules covering core AI concepts, computer vision, and natural language processing, participants acquire expert-level skills. The curriculum also includes foundational knowledge in general AI principles, paving the way for successful AI careers.

In DataMites Kenya, artificial intelligence training courses utilize a case study-centric method. The curriculum, finely tailored by adept content teams, meets industry requirements, providing a career-focused learning journey that imparts hands-on skills and readies participants for real-world scenarios adeptly.

The purpose of DataMites' AI Engineer Course in Kenya, spanning 9 months, is to equip intermediate and expert learners with a career-centric education. It aims to provide a comprehensive understanding of machine learning and AI, encompassing key elements such as Python, statistics, machine learning, visual analytics, deep learning, computer vision, and natural language processing. Participants graduate ready to excel in AI-related roles.

Kenya's AI Foundation Course introduces beginners to the world of AI, exploring its applications and significance. Accessible to all backgrounds, it demystifies key concepts like machine learning, deep learning, and neural networks, equipping participants with essential knowledge to navigate the field of AI confidently and pursue further studies or careers.

DataMites' AI Training in Kenya offer online artificial intelligence training in Kenya, enabling remote interaction with live instructors. Additionally, self-paced learning options accommodate flexibility, allowing learners to navigate the curriculum independently and at their preferred pace.

The duration of DataMites' artificial intelligence training in Kenya varies, offering flexibility with durations ranging from 1 to 9 months. This allows participants to select a timeframe that suits their learning goals and availability. Additionally, training sessions are conveniently scheduled on weekdays and weekends, ensuring accessibility for individuals with diverse schedules and commitments.

The pricing structure for Artificial Intelligence Training in Kenya conducted by DataMites ranges from KES 114,198 to KES 296,330. The exact cost depends on factors such as the specific course selected, the duration of the training, and any additional services or resources included in the package.

DataMites Kenya boasts AI trainers like Ashok Veda and Lead Mentors, renowned for their expertise in Data Science and AI. They provide top-notch mentorship. Additionally, elite mentors and faculty members from esteemed institutes such as IIMs contribute to a comprehensive learning environment.

Flexi-Pass for AI training in Kenya offers convenience and customization, allowing learners to adapt their study schedule. With access to live sessions and recorded materials, participants can learn flexibly, tailoring their learning journey to fit their lifestyle and commitments effectively.

Indeed, attending a demo class for artificial intelligence training in Kenya before making any payment is possible. This offers an opportunity to evaluate teaching strategies, course curriculum, and instructor expertise firsthand, ensuring they are suitable for your learning preferences and objectives.

Absolutely, DataMites integrates live projects into the Artificial Intelligence Course in Kenya, comprising 10 Capstone projects and 1 Client Project. These projects provide participants with real-world application opportunities, fostering practical skills development and industry readiness effectively.

Yes, participants in artificial intelligence sessions in Kenya must bring a valid photo ID, such as a national ID card or driver's license. This is essential for receiving the participation certificate and scheduling any certification exams related to the training.

Absolutely, DataMites offers Artificial Intelligence Courses with Internship in Kenya. Participants gain real-world experience in industries related to Analytics, Data Science, and AI roles, enhancing their career prospects significantly. This hands-on experience is invaluable for their professional development and growth.

At DataMites Kenya, career mentoring sessions for AI training occur in both one-on-one and group settings. Participants benefit from personalized advice on career paths, job prospects, skill development, and industry trends, fostering their professional growth effectively.

Artificial intelligence course training in Kenya at DataMites provides diverse payment options. Participants can opt for cash, debit card, check, credit card, EMI, PayPal, Visa, Mastercard, American Express, or net banking, ensuring flexibility and ease in payment methods to suit their preferences.

Absolutely, after completing Artificial Intelligence training at DataMites in Kenya, you'll earn IABAC Certification. This certification, aligned with EU standards and industry benchmarks, certifies your competency in AI and strengthens your credentials on a global scale.

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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