4.9 (1,250) reviews MYR 13,520
MYR 9,341*
*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!
Eligibility for an artificial intelligence course in Kuala Lumpur depends on the course level and curriculum. Beginner courses generally require basic computer skills, while advanced programmes may require a degree or diploma in computer science, mathematics, engineering, or a related field.
An online AI course in Kuala Lumpur is suitable for students, fresh graduates, working professionals, entrepreneurs, and career changers interested in artificial intelligence. Beginners can start with introductory courses, while those with programming or data analysis experience can pursue more technical training.
The duration of an online artificial intelligence course in Kuala Lumpur varies according to its depth and learning format.
Short introductory courses: 1 to 4 weeks
Practical skills courses: 1 to 6 months
Professional certification programs: 3 to 12 months
The average fee for an online artificial intelligence course in Kuala Lumpur in 2026 varies by course level and duration. Short introductory courses may cost around RM200 to RM1,500, while practical AI and machine learning programs typically range from RM2,000 to RM5,000, and advanced professional or postgraduate programs can cost RM9,000 to RM25,000 or more. These are indicative market ranges, not official averages, and fees depend on course content, practical projects, duration, and certification.
Completing an AI course can help learners prepare for entry-level or specialised roles, depending on their qualifications, technical skills, and project experience. Potential career paths include AI developer, machine learning engineer, data analyst, data scientist, AI application specialist, and automation developer.
The average base salary of an AI Engineer in Kuala Lumpur is approximately RM76,890 per year, according to Indeed Malaysia salary data, based on six reported salaries. Actual earnings vary depending on experience, technical skills, industry, employer, and job responsibilities.
An online artificial intelligence course can help you develop practical skills in programming, data analysis, machine learning, and AI application development. Depending on the syllabus, you may also learn model evaluation, prompt engineering, automation, data visualization, and responsible AI practices.
Yes, working professionals can enrol in online or part-time AI courses that fit around their existing schedules. Flexible lessons, recorded sessions, and practical assignments can help learners build relevant skills while continuing their employment.
No, prior programming knowledge is not essential for beginner-level AI courses that focus on concepts, tools, and practical applications. However, learning Python, basic statistics, and fundamental mathematics will be helpful when progressing to machine learning and AI development.
Artificial intelligence (AI) is technology that enables computers to perform tasks such as understanding language, recognising images, making predictions, and generating content. It works by processing data and using rules or trained models to identify patterns, produce outputs, and support decisions.
Python is widely used in AI because it offers accessible syntax and extensive libraries for machine learning, data analysis, and deep learning. Other useful languages include R for statistical computing, SQL for working with databases, and Java or C++ for certain production systems and performance-intensive applications.
A degree or diploma in computer science, data science, software engineering, mathematics, or a related field can provide a strong foundation for an AI career. Professional certifications, practical projects, programming skills, and experience with real datasets can also strengthen a candidate's profile, although requirements vary by role.
Common AI-related job titles include AI Engineer, AI Developer, Machine Learning Engineer, Data Scientist, Data Analyst, and Natural Language Processing Engineer. Other opportunities include computer vision specialist, AI automation developer, and machine learning operations engineer, depending on employer needs and technical expertise.
Businesses in Kuala Lumpur are adopting AI across banking, finance, retail, healthcare, logistics, manufacturing, and customer service to automate routine tasks, analyse data, improve decision-making, and enhance customer experiences. Malaysia's National AI Action Plan 2026–2030 also emphasises responsible AI adoption, talent development, and innovation to support the country's growing AI ecosystem.
AI can be a promising career option in Kuala Lumpur as organisations explore automation, data-driven decision-making, and generative AI applications. However, employment prospects depend on market demand, relevant qualifications, practical experience, and the ability to keep technical skills up to date.
Artificial intelligence is the broader field of creating computer systems that perform tasks associated with human intelligence. Machine learning is a branch of AI in which systems learn patterns from data, while deep learning is a type of machine learning that uses multiple layers of artificial neural networks to handle complex tasks such as speech recognition and image analysis.
Generative AI helps employees draft documents, summarise information, create marketing content, write code, and assist with customer enquiries. Businesses in Kuala Lumpur can use these tools to support productivity, but they still need human review, data protection measures, and safeguards against inaccurate or biased outputs.
A successful AI career combines technical knowledge with problem-solving, communication, and responsible decision-making. Important skills include Python programming, mathematics and statistics, data preparation, machine learning, model evaluation, and familiarity with AI tools, alongside critical thinking, teamwork, and an understanding of data privacy and ethics.
Our DataMites AI Engineer course in Kuala Lumpur is suitable for graduates, working professionals, and individuals interested in developing AI skills. A background in programming, mathematics, statistics, or computer science can be helpful, but we recommend contacting our team to confirm the eligibility requirements for your profile.
The fee for our DataMites Artificial Intelligence course in Kuala Lumpur ranges from MYR 9,480 to MYR 13,520, depending on the selected course option. For a detailed fee breakdown and confirmation of the applicable package, please contact our DataMites team.
Our DataMites AI training program in Kuala Lumpur has a duration of 9 months, with 780+ learning hours. This extended learning period allows learners to build their AI knowledge, develop practical skills, and work on relevant projects.
After completing the applicable course requirements, learners may receive a DataMites course completion certificate, subject to the relevant certification terms. NASSCOM FutureSkills Certification is available for eligible NRI learners. Please contact our team to confirm your eligibility and the certification options applicable to your enrollment.
To enrol in our DataMites Artificial Intelligence course in Kuala Lumpur, contact our team to discuss your learning goals, available batches, course options, and fees. Our team can guide you through the registration process and explain the payment requirements before you confirm your enrolment.
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; please contact our team to confirm the applicable payment terms.
Yes, our AI training in Kuala Lumpur offers a live online learning format, allowing learners to participate in instructor-led sessions remotely. Contact our team for details about batch schedules, session timings, and the online learning arrangements available for your chosen course.
Refund eligibility depends on the applicable cancellation and refund policy for your enrolment. Please contact our DataMites team to confirm the relevant terms, eligibility conditions, and cancellation procedure before making a decision.
You can contact our team to enquire about demo or trial class availability for the DataMites Artificial Intelligence course in Kuala Lumpur. We can confirm the available options and help you understand the course structure and learning format before you enrol.
If you miss a live online class, contact our team to check the available options for catching up on the missed topics. Recording availability, session access, and any applicable revision arrangements should be confirmed with DataMites.
Our DataMites Artificial Intelligence training in Kuala Lumpur is designed for graduates, aspiring AI professionals, and working individuals who want to build or strengthen their AI skills. The most suitable entry route depends on your educational background and existing knowledge, so we recommend checking the specific eligibility requirements with our team.
Our AI course is designed to combine theoretical concepts with practical learning and project-based activities that help learners apply their knowledge. Contact our team for confirmation of the specific projects, hands-on exercises, and practical components included in your selected course option.
Our DataMites Artificial Intelligence course covers key AI concepts and relevant technical skills, with topics potentially including Python programming, machine learning, deep learning, data preparation, and AI applications. For the complete curriculum and the technologies covered in each module, please request the latest syllabus from our team.
Yes, our DataMites AI training in Kuala Lumpur may be suitable for beginners who want to develop their understanding of artificial intelligence from the ground up. Basic programming and mathematical knowledge can be helpful, and we recommend discussing your current skill level with our team to choose the right learning path.
Yes, we offer online learning options, and learners can enquire about the Flexi Pass for additional flexibility. The Flexi Pass allows you to attend multiple batches of the same course during its validity period and revisit topics as needed, and it remains valid for 3 months from activation; contact our team to confirm the applicable terms.
The fee for our DataMites AI course in Kuala Lumpur ranges from MYR 9,480 to MYR 13,520, depending on the selected course option. Course inclusions may cover structured learning, training sessions, and practical activities, but the exact package contents, learning resources, and certification provisions should be confirmed with our DataMites team before enrolment.
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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