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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 Malaysia depends on the course level and learning objectives. Beginner courses usually accept learners with basic computer skills, while advanced programmes may require a diploma or degree in computer science, information technology, mathematics, engineering, or a related field.
An online AI course in Malaysia is suitable for students, fresh graduates, working professionals, entrepreneurs, and individuals interested in technology. Beginners can start with introductory lessons, while learners with programming or data analysis experience may prefer more technical courses.
An online artificial intelligence course in Malaysia can take anywhere from a few hours to 12 months or longer, depending on its depth and format.
Short introductory courses: a few hours to 4 weeks.
Skills-based courses: 1 to 6 months.
Professional certification or comprehensive programmes: 6 to 12 months or longer.
The estimated fee for an online AI course in Malaysia ranges from RM100 to RM1,000 for short introductory courses and approximately RM2,000 to RM15,000 or more for comprehensive professional programmes. Fees vary according to course duration, curriculum, practical projects, instructor support, and certification, so these ranges are general estimates rather than official fees.
Completing an AI course can help learners prepare for technology- and data-related roles, depending on their existing qualifications, practical experience, and technical skills. Potential career paths include AI developer, machine learning engineer, data scientist, AI analyst, and AI application specialist.
The average salary of an AI Engineer in Malaysia varies depending on experience, technical skills, location, and employer. According to Indeed Malaysia, the salary data for AI Engineers provides a benchmark for earnings in this role, while Indeed’s Machine Learning Engineer salary data reports an average base salary of RM8,003 per month, equivalent to approximately RM96,036 per year. Actual earnings vary according to job title, industry, experience, and responsibilities.
An online artificial intelligence course can help you develop technical and analytical skills for working with AI tools and applications. Depending on the syllabus, you may learn:
Python programming and data analysis.
Machine learning and model evaluation.
Deep learning and neural networks.
Generative AI, prompt design, and responsible AI practices.
Yes, working professionals can enrol in an artificial intelligence course in Malaysia, including part-time and self-paced online programmes. These formats allow learners to study outside working hours while developing skills relevant to their existing roles or future career plans.
No, prior programming knowledge is not essential for beginner-level AI courses, which often introduce core concepts and practical AI tools. However, learning Python, basic statistics, and data handling will be helpful for advanced machine learning and AI development programmes.
Artificial intelligence (AI) is a branch of computing that enables machines to perform tasks such as understanding language, recognising images, making predictions, and generating content. AI systems use data, algorithms, and trained models to identify patterns and produce results, with their accuracy depending on the quality of the data, design, and evaluation.
Python is one of the most widely used programming languages in AI because it supports data analysis, machine learning, and deep learning through extensive libraries. Other useful languages include R for statistical analysis, SQL for working with databases, and C++ and Java for certain performance-intensive or large-scale applications.
A degree or diploma in computer science, data science, information technology, mathematics, or engineering can provide a strong foundation for AI-related careers. Professional certificates, practical projects, programming experience, and a portfolio demonstrating your ability to solve real problems can also strengthen your profile.
Common AI-related job roles include AI engineer, machine learning engineer, data scientist, AI researcher, and natural language processing engineer. Other options include computer vision engineer, AI application developer, and AI solutions specialist, with requirements varying according to the employer and responsibilities.
AI adoption in Malaysia is creating opportunities across sectors such as finance, manufacturing, healthcare, retail and logistics. As organisations explore automation, data-driven decision-making and generative AI, demand may grow for people who can develop, implement, evaluate and manage AI systems, although hiring needs will vary by industry.
AI can be a promising career choice in Malaysia for people who enjoy problem-solving, programming, mathematics, or working with data. Long-term prospects depend on continued industry adoption, relevant technical skills, practical experience, and the ability to keep up with changing AI technologies.
Artificial intelligence is the broader field of creating computer systems that perform tasks requiring human-like intelligence, while machine learning is a subset of AI that learns patterns from data. Deep learning is a specialised type of machine learning that uses multi-layered neural networks to handle complex tasks such as image recognition, speech processing, and language generation.
Generative AI is helping Malaysian organisations draft content, summarise documents, assist with customer enquiries, analyse information and support software development. Its use also requires attention to data privacy, accuracy, intellectual property, and human review, particularly when handling confidential or important business decisions.
A successful AI career typically combines technical knowledge with analytical thinking and practical problem-solving. Important skills include:
Python programming, statistics, and data analysis.
Machine learning fundamentals and model evaluation.
Data preparation, visualisation, and critical thinking.
Communication, responsible AI practices and continuous learning.
Our DataMites Artificial Intelligence course in Malaysia is suitable for learners who want to build skills in artificial intelligence, including students, graduates, and working professionals. A basic understanding of mathematics, statistics, or programming can be helpful, although the recommended prerequisites may vary depending on your learning background. Contact our DataMites team to confirm the eligibility requirements before enrolling.
Our DataMites AI course in Malaysia has a duration of 8 months, with 160 Live training hours covering concepts, practical exercises, and project work. This structured learning path helps learners develop their AI knowledge progressively. The exact schedule may depend on the selected batch and learning format.
The fee for our DataMites Artificial Intelligence course in Malaysia ranges from MYR 9,480 to MYR 13,520 depending on the selected course option. We recommend contacting our team for the latest fee details, payment arrangements, and any applicable terms before registration.
After completing the applicable course requirements, learners may receive the relevant course completion certification, subject to the certification terms. NASSCOM FutureSkills Certification is available for eligible NRI learners, subject to the applicable eligibility criteria. Contact our DataMites team to confirm the certification options available for your enrolment.
To enrol in our DataMites Artificial Intelligence course in Malaysia, contact our team through the official DataMites website to discuss your learning goals, course options, and eligibility. We can guide you through the registration process, available batches, fee structure, and payment requirements. Review the course details before completing your enrolment.
Yes, our AI training in Malaysia offers live online learning options for learners who prefer instructor-led sessions. These classes provide opportunities to learn concepts, follow explanations, and engage with the instructor during scheduled sessions. Contact our team to confirm the live class schedule and format available for your preferred batch.
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. For course fees of MYR 9,480 to MYR 13,520, contact our team to confirm the applicable payment terms and EMI eligibility.
If you miss a session, contact our DataMites team to check whether a recording, alternative batch, or catch-up option is available under your course plan. Our Flexi Pass option may also help eligible learners revisit sessions by attending multiple batches of the same course during its validity period. Availability and access conditions depend on the applicable learning option.
You can contact our DataMites team to ask about demo or trial class availability before enrolling in our AI course in Malaysia. We can help you confirm whether a session is available and explain how to attend it. Please check the current schedule and registration requirements with our team.
Refund eligibility depends on the applicable cancellation and refund policy for your enrolment. Before cancelling, contact our DataMites team to confirm the relevant terms, any applicable conditions, and the process for submitting a refund request. We recommend reviewing these details before making your registration payment.
Our DataMites Artificial Intelligence course in Malaysia is designed for students, graduates, working professionals, and individuals interested in developing AI skills. Learners from different educational and professional backgrounds can explore the course, although some foundational knowledge of mathematics, statistics, or programming may be useful. Contact our team to discuss your background and confirm whether the course is a suitable fit.
Our AI training in Malaysia covers key artificial intelligence concepts and relevant technical skills, with topics potentially including Python programming, statistics, machine learning, deep learning, and data preparation. The curriculum may also include practical exercises and project work to help learners apply what they study. Contact our DataMites team for the detailed syllabus and current list of tools and technologies included in your selected course.
Our DataMites Artificial Intelligence course is designed to combine theoretical learning with practical exercises and project-based learning. These activities help learners practise AI techniques and understand how they can be applied to relevant problems. Contact our team for details about the projects, datasets, and practical components included in your selected learning plan.
Internship availability and inclusion may depend on the specific course option and current programme terms. We recommend contacting our DataMites team to confirm whether an internship is included in the Artificial Intelligence course you are considering. Our team can clarify the applicable details before you enrol.
We offer online learning options for our DataMites AI training in Malaysia, with availability depending on the course plan and batch. Learners can ask our team about live online classes, other available learning arrangements, and the Flexi Pass option. The Flexi Pass allows learners to attend multiple batches of the same course during its validity period and revisit topics as needed; it remains valid for 3 months from activation.
At DataMites, we aim to help learners build AI knowledge through structured learning, practical exercises, and project-based activities. Our Artificial Intelligence course in Malaysia offers a 8-month learning journey with 440 learning hours, along with online learning options for greater flexibility. To determine whether the curriculum, certification options, and course fee of MYR 9,480 to MYR 13,520 suit your goals, contact our team for the latest programme details.
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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