Instructor Led Live Online
Self Learning + Live Mentoring
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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.
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
MODULE 2: HDFS AND MAP REDUCE
MODULE 3: PYSPARK FOUNDATION
MODULE 4: SPARK SQL and 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
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that can perform tasks typically requiring human intelligence, such as learning, problem-solving, and decision-making.
AI finds practical applications across various domains, including healthcare diagnosis, autonomous vehicles, virtual assistants, fraud detection, recommendation systems, and predictive analytics.
Primary duties of an AI engineer involve designing and developing AI algorithms, implementing machine learning models, optimizing AI systems, troubleshooting issues, and collaborating with cross-functional teams to deploy AI solutions effectively.
To prepare for AI-related interviews, one should thoroughly study core AI concepts, practice coding exercises, review real-world AI applications, and be prepared to discuss previous AI projects or experiences.
Ethical dilemmas in AI encompass concerns regarding privacy invasion, bias in algorithms, job displacement, autonomous weapons, and the potential for AI to amplify existing social inequalities.
Categories within AI include machine learning, natural language processing, computer vision, robotics, expert systems, and autonomous agents.
Roles in AI such as machine learning engineers, data scientists, AI researchers, and AI architects typically offer high salaries due to their specialized skill sets and high demand in the market.
Major tech companies like Google, Facebook, Amazon, Microsoft, and IBM, as well as leading AI startups and research institutions, actively seek professionals skilled in AI.
Acquiring AI skills in Mogadishu involves enrolling in AI courses, attending workshops, participating in AI projects or hackathons, and leveraging online resources and communities dedicated to AI learning.
Qualifications for an AI position in Mogadishu typically include a degree in computer science, engineering, mathematics, or a related field, along with proficiency in programming languages like Python, experience with AI frameworks, and a strong understanding of AI principles.
In Mogadishu, artificial intelligence engineers can anticipate a salary range comparable to the average annual salary of $154,835 for this role in the United States, according to data from Glassdoor.
Sought-after skills for AI careers in Mogadishu include proficiency in machine learning algorithms, deep learning frameworks, data analysis, programming languages, problem-solving abilities, and strong communication skills.
AI is reshaping education through personalized learning experiences, adaptive learning platforms, intelligent tutoring systems, automated grading systems, and AI-powered educational content creation tools.
Certifications can enhance credibility and demonstrate proficiency in specific AI technologies or methodologies, making them valuable for AI careers in Mogadishu, especially for entry-level positions or career advancement opportunities.
Becoming an AI engineer in Mogadishu involves acquiring relevant education and skills, gaining practical experience through internships or projects, building a strong portfolio, networking with professionals in the field, and actively seeking job opportunities in AI-related roles.
Artificial intelligence functions by processing vast amounts of data, extracting patterns and insights, making predictions or decisions, and continuously learning and improving through algorithms and feedback loops.
Advancements in the future of AI may include breakthroughs in deep learning, reinforcement learning, natural language understanding, human-like AI, ethical AI frameworks, and AI-human collaboration.
Security concerns stemming from AI integration include vulnerabilities in AI systems, adversarial attacks on AI models, data privacy breaches, and misuse of AI-powered technologies for malicious purposes.
Prevailing developments in artificial intelligence include advancements in AI ethics and regulation, breakthroughs in AI research, democratization of AI technologies, integration of AI in various industries, and AI-driven innovation in healthcare, finance, and transportation.
Iconic instances of artificial intelligence in mainstream media include HAL 9000 from "2001: A Space Odyssey," Skynet from "Terminator," Samantha from "Her," Ava from "Ex Machina," and J.A.R.V.I.S. from the Marvel Cinematic Universe.
Eligibility for artificial intelligence courses varies depending on the specific course. Typically, individuals with backgrounds in computer science, engineering, mathematics, statistics, or related fields are eligible. However, DataMites also welcomes participants from non-technical backgrounds who are interested in transitioning to AI.
The length of the Artificial Intelligence Training in Mogadishu varies based on the specific program chosen, spanning from 1 month to 9 months. DataMites provides training sessions during weekdays and weekends to cater to different scheduling needs.
You can enhance your knowledge in Artificial Intelligence by enrolling with DataMites, a renowned global training institute specializing in data science and AI.
DataMites offers a 3-month program tailored for intermediate to expert learners in AI. The Artificial Intelligence Expert Course focuses on core AI concepts, computer vision, natural language processing, and foundational knowledge in general AI, providing a career-oriented pathway.
DataMites offers the following certifications in Artificial Intelligence in Mogadishu:
The AI Engineer Course in Mogadishu, spanning 9 months, targets intermediate to expert learners in AI. It's designed as a career-oriented program to provide a robust foundation in machine learning and AI, covering Python, statistics, visual analytics, deep learning, computer vision, and natural language processing.
The fee for Artificial Intelligence Training in Mogadishu with DataMites ranges from SOS 408,660 to SOS 1,060,354. This comprehensive program offers expert-led instruction, hands-on experience, and industry-recognized certification. With flexible learning options and practical skills development, it prepares individuals for success in AI careers.
The trainers at DataMites for artificial intelligence training in Mogadishu include Ashok Veda and lead mentors. They are esteemed professionals with expertise in data science and AI, providing top-notch mentorship to ensure quality training.
Flexi-Pass in the context of artificial intelligence courses in Mogadishu refers to a flexible learning option offered by training institutes like DataMites. It allows participants to customize their learning experience by providing access to recorded lectures, live sessions, and course materials. This enables learners to study at their own pace and convenience, fitting their training around personal or professional commitments.
Upon completion of Artificial Intelligence Course Training in Mogadishu at DataMites, participants receive IABAC Certification, which adheres to the EU-based framework. The syllabus aligns with industry standards as per the global accreditation body of IABAC.
Opt for DataMites for online AI training in Mogadishu to access expert-guided instruction, flexible learning formats, practical experience, and globally recognized IABAC certification. Benefit from a comprehensive curriculum covering machine learning, deep learning, and other essential AI concepts for real-world applicability.
Participants attending artificial intelligence sessions in Mogadishu are required to bring valid photo identification, such as a national ID card or driver's license. This documentation is essential for obtaining participation certificates and scheduling any relevant certification exams.
If you're unable to attend an artificial intelligence session in Mogadishu, you may miss out on valuable learning opportunities. It's essential to communicate your absence to the organizers promptly to explore any available alternatives or catch-up options.
Certainly, DataMites provides Artificial Intelligence Courses with internship in Mogadishu in various industries. These internships offer real-world experience in Analytics, Data Science, and AI roles, enhancing career progression significantly.
The career mentoring sessions for artificial intelligence training in Mogadishu at DataMites typically involve personalized guidance from industry experts. Sessions may include resume building, interview preparation, career goal setting, and networking strategies. These sessions aim to empower participants with the necessary skills and knowledge for successful career advancement in AI.
DataMites in Mogadishu accepts various payment methods, including cash, debit cards, checks, credit cards, EMI, PayPal, Visa, Mastercard, American Express cards, and net banking.
Career mentoring sessions for AI training in Mogadishu at DataMites usually feature tailored guidance from industry professionals. These sessions cover resume development, interview readiness, goal establishment, and networking tactics, empowering participants for AI career progression.
The training methodology for artificial intelligence courses in Mogadishu at DataMites primarily revolves around case studies. The curriculum, crafted by an expert content team, is meticulously aligned with industry standards, ensuring a job-oriented approach to learning.
Yes, DataMites in Mogadishu offers 10 Capstone projects and 1 Client Project as components of their artificial intelligence course.
Yes, you can partake in an artificial intelligence course in Mogadishu before making a payment at DataMites. This allows you to experience the course content and teaching style firsthand, helping you make an informed decision about enrolling.
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: -
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.