4.9 (15,500) reviews LKR 532,760
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*Offer valid till 31st October 2026
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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
AI is the future, and DataMites helped me step into it! The online Data Science course was easy to follow, and the tutors explained everything clearly. Even after completing my course, the team supported me with placements and I successfully cracked a job! Grateful to DataMites for shaping my career.
I joined DataMites to learn Data Science and gained strong skills in ML, DL, and Statistics. Through my internship at Rubixe AI Solutions, I worked on real-world projects, which helped me land job interviews and an offer. The trainers were very supportive and explained every concept clearly. Thanks to DataMites, my data science journey was smooth and successful.
My mentor, a DataMites alumnus, suggested I join the Data Science course, and it was one of my best decisions! The offline classes were interactive and full of guidance, and I got the chance to intern at Rubixe AI Solutions. The placement team was very supportive and helped me perfect my resume. Truly thankful to DataMites for this amazing learning journey.
I joined DataMites after completing my engineering in Computer Science and became a Certified Data Scientist. With their support, I completed an internship at Rubix AI Solutions and now work as an AI intern at Inventor Hub. DataMites made learning statistics and data science easy with clear guidance and great materials. I’m truly thankful for their support.
I joined DataMites for the Certified Data Scientist online course, and it was a great experience! The real-time projects helped me apply my learning practically, and the placement support added real value to my career restart after 8 years. DataMites is the perfect platform to learn, grow, and begin your data science journey.
I joined DataMites for the Data Science course and learned Python, SQL, Machine Learning, and Deep Learning through hands-on projects. The trainers are excellent at explaining concepts clearly. The placement team is very supportive and provides great opportunities thanks to DataMites for helping me start my data science journey.
I joined DataMites after my B.Tech when I developed an interest in Data Science and Machine Learning. The training was well-structured, and I even got an internship opportunity that helped me gain real experience. The placement team was super supportive and guided me through every step. Thanks to DataMites, I’m now placed at Horizon Broadband Pvt. Ltd.
I joined DataMites after completing my BCA, and it was the best decision for my data science career! I learned Python, SQL, Machine Learning, Deep Learning, and NLP, and worked on several capstone and client projects that gave me real-world experience. The trainers and team were always friendly and supportive, patiently clearing all my doubts. Thanks to DataMites, I’m confident and job-ready in data science
I started my data science journey with DataMites, and it’s been an amazing experience! The Certified Data Scientist Program helped me learn everything from scratch through hands-on projects and an internship. The placement team was incredibly supportive, always ready to clarify my doubts. Thanks to their guidance, I successfully got placed. Highly recommend DataMites to anyone starting their data science career
Enrolling at DataMites was one of the best decisions I made for my career. Coming from a freelance writing background with no prior technical experience, I was initially unsure about stepping into the field of data analytics. But DataMites made the transition smooth and achievable. I’d highly recommend them to anyone looking for a guided, industry-ready data analytics course. A true DataMites success story!
The eligibility requirements for an artificial intelligence course in Sri Lanka depend on the course level and curriculum. Beginner programmes usually accept students who have basic computer skills and an interest in technology, while advanced courses may require mathematics, statistics, programming, or a relevant educational qualification.
An online AI course in Sri Lanka is suitable for school leavers, university students, graduates, working professionals, entrepreneurs, and anyone interested in learning artificial intelligence. Many introductory programmes welcome beginners, while specialised courses are better suited to learners with a background in computing, mathematics, or data analysis.
The duration of an online artificial intelligence course in Sri Lanka typically ranges from a few weeks to 12 months or longer, depending on its depth and format. Short introductory courses may take 2 to 8 weeks, practical intermediate courses around 3 to 6 months, and comprehensive professional certification programmes approximately 6 to 12 months.
As a general budgeting guide, short online AI courses may cost around LKR 5,000 to 30,000, while comprehensive professional programmes may range from LKR 50,000 to 250,000 or more. These are indicative estimates rather than official market averages, and actual fees vary according to course duration, curriculum, practical projects, instructor support, and certification.
Completing an AI course can help you prepare for entry-level opportunities or further study in areas such as data science, software development, automation, and intelligent applications. Potential career paths include AI developer, junior machine learning engineer, data analyst, data scientist, NLP developer, and AI solutions specialist, depending on your practical skills and qualifications.
According to Glassdoor's AI Engineer salary data for Sri Lanka, salary estimates available in 2026 indicate monthly pay ranging from approximately LKR 110,000 to LKR 260,000. Actual earnings vary depending on experience, technical skills, location, industry, and job responsibilities, and these figures should be treated as estimates rather than guaranteed salaries.
A practical online artificial intelligence course in Sri Lanka can help you develop skills in programming, data analysis, machine learning, and building AI-powered applications. Depending on the syllabus, you may also learn to:
Use Python and common AI libraries.
Prepare datasets and evaluate machine learning models.
Work with generative AI tools and language models.
Build small AI projects and understand responsible AI practices.
Yes, many artificial intelligence courses in Sri Lanka offer online classes, recorded lessons, weekend sessions, or flexible study schedules suitable for working professionals. These formats allow learners to develop AI skills alongside their existing jobs, although the weekly workload and any live attendance requirements should be checked before enrolling.
Prior programming knowledge is not always required, as beginner-level AI courses often introduce coding concepts from the basics. However, learning Python, fundamental mathematics, and basic statistics will make it easier to understand machine learning algorithms and complete practical AI projects.
Artificial intelligence (AI) is a branch of computing that enables machines to perform tasks commonly associated with human intelligence, such as recognising images, understanding language, and making predictions. AI systems process data using programmed rules or learned patterns to generate results, recommendations, or decisions, depending on how they are designed and trained.
Python is one of the most widely used languages for artificial intelligence because it has extensive libraries for machine learning, data analysis, and deep learning. Other useful languages include R for statistics, SQL for working with databases, Java for software applications, and C++ for performance-intensive systems.
A degree in computer science, software engineering, data science, mathematics, statistics, or a related field can provide a strong foundation for an AI career. However, some roles may also be accessible through professional certifications, online courses, practical projects, and a portfolio demonstrating relevant technical skills; more specialised positions may require a degree or postgraduate study.
Common AI career roles include AI engineer, machine learning engineer, data scientist, AI researcher, NLP engineer, computer vision engineer, and generative AI developer. Each role has different responsibilities, from preparing data and training models to developing intelligent software, so the most suitable option depends on your interests, education, and technical experience.
Sri Lanka’s AI industry is expected to grow as businesses explore automation, data analytics, customer service tools, and generative AI to improve productivity and decision-making. Opportunities may emerge in sectors such as banking, telecommunications, healthcare, software development, and agriculture, although growth will depend on digital infrastructure, investment, and access to skilled professionals. Learning machine learning, data analysis, Python programming, and responsible AI practices can help learners prepare for potential opportunities in this developing field.
Artificial intelligence can be a promising career choice in Sri Lanka for people who enjoy problem-solving, technology, mathematics, or software development. Opportunities may arise as businesses adopt automation and data-driven systems, but career outcomes depend on practical experience, technical ability, communication skills, and the availability of suitable jobs.
Artificial intelligence is the broad field of creating systems that perform tasks associated with human intelligence, while machine learning is a part of AI that enables systems to learn patterns from data. Deep learning is a specialised type of machine learning that uses multi-layered neural networks to handle complex tasks such as speech recognition, image analysis, and language processing.
Generative AI is helping Sri Lankan businesses create content, summarise documents, support customer service, analyse information, and automate routine tasks. Although these tools can improve productivity, they also increase the importance of checking accuracy, protecting confidential data, and developing skills in AI-assisted workflows, critical thinking, and human oversight.
To build a career in AI, focus on a combination of technical knowledge, practical experience, and problem-solving ability. The most useful skills to develop include:
Programming and data: Python, SQL, data preparation, and basic statistics.
AI fundamentals: Machine learning, deep learning, generative AI, and model evaluation.
Practical development: Building projects, using relevant AI tools, and deploying applications.
Professional skills: Analytical thinking, communication, teamwork, data privacy, and responsible AI use.
At DataMites, we welcome graduates, working professionals, and aspiring AI practitioners interested in developing artificial intelligence skills. A background in programming, mathematics, statistics, or IT can be helpful, but we recommend contacting our team to confirm the prerequisites for your chosen course.
The fee for our DataMites Artificial Intelligence course in Sri Lanka ranges from LKR 373,620 to LKR 532,760, depending on the selected course package and its inclusions. Contact our team for the latest fee details and applicable payment options.
Our DataMites Artificial Intelligence training program has a 9-month duration with 780+ learning hours, covering theoretical concepts and practical learning activities. This structured approach helps learners build their AI knowledge and develop relevant technical skills over time.
We offer course-related certification options based on the selected program and applicable completion requirements. NASSCOM FutureSkills Certification is available for eligible NRI learners, subject to the relevant eligibility criteria and certification requirements.
Yes, we offer online learning options for individuals seeking flexible AI training in Sri Lanka. Contact our team to confirm upcoming live online batches, class schedules, and the learning format available for your chosen program.
We accept online debit and credit card payments through Razorpay, and credit card EMI is available where applicable. A token advance is collected during registration, and the remaining course fee must be paid in full before course completion; our team can clarify the applicable payment terms.
You can visit the DataMites website and contact our team to discuss course details, eligibility, fees, and upcoming batches. We will guide you through the registration process and help you understand the available options before you enrol in our online AI course in Sri Lanka.
You can contact our DataMites team to check whether a demo or introductory session is available for your preferred AI course. We will confirm the available options and provide relevant details to help you make an informed decision.
If you miss a live online session, contact our team to check whether class recordings, alternative batches, or other learning support options are available. Access to recordings and make-up sessions depends on the arrangements applicable to your selected course.
Our AI curriculum is designed to cover core artificial intelligence concepts, machine learning, data preparation, model development, and practical applications. We recommend requesting the latest detailed syllabus from our team to confirm the topics, learning modules, and project work included in your selected program.
Our AI training in Sri Lanka may suit graduates, professionals, career changers, and learners interested in exploring artificial intelligence. Beginners can contact us to understand the recommended prerequisites and determine whether they should build foundational programming or mathematics skills before starting.
Our Artificial Intelligence training incorporates practical learning activities and project work to help learners apply AI concepts to real-world-style problems. Internship availability and its terms depend on the specific program, so please contact our team to confirm whether an internship component is included.
We provide online learning options to help learners study AI while managing work, education, or other commitments, subject to batch availability. Our Flexi Pass remains valid for 3 months from activation, allowing learners to attend multiple batches of the same course during that period and revisit topics whenever they need additional practice.
Refund eligibility depends on the applicable cancellation and refund policy for your enrolment. Please contact the DataMites team to confirm the relevant terms, conditions, and refund eligibility before proceeding with a cancellation.
Our AI training is designed to help learners understand relevant AI concepts and develop practical skills using technologies covered in the selected curriculum. Contact our team for the latest syllabus and confirmation of the specific programming languages, libraries, frameworks, and tools included in your course.
Our course package may include structured learning modules, practical exercises, project work, and applicable learning resources, depending on the option you select. The fee ranges from LKR 373,620 to LKR 532,760; please contact our team for a detailed breakdown of inclusions, certification options, and any additional applicable charges.
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.
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