Artificial Intelligence Certification Course in Singapore





Instructor-led Advanced AI Course live online Training Schedule
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Why enroll for Artificial Intelligence Certification Course in Singapore?



Artificial Intelligence Course in Singapore Benefits




Why Artificial Intelligence Certification Course from edureka in Singapore
Live Interactive Learning
- World-Class Instructors
- Expert-Led Mentoring Sessions
- Instant doubt clearing
Hands-On Project Based Learning
- Industry-Relevant Projects
- Course Demo Dataset & Files
- Quizzes & Assignments
Industry Recognised Certification
- Edureka Training Certificate
- Graded Performance Certificate
- Certificate of Completion
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About your Artificial Intelligence Certification Course
Artificial Intelligence Skills Covered
Image Classification Image Processing Text Processing Collaborative Filtering Text Classification Computer Vision
Artificial Intelligence Tools Covered
Artificial Intelligence Certification Course Curriculum in Singapore
Curriculum Designed by Experts
Introduction to Text Mining and NLP
Topics
- Overview of Text Mining
- Need of Text Mining
- Natural Language Processing (NLP) in Text Mining
- Applications of Text Mining
- OS Module
- Reading, Writing to text and word files
- Setting the NLTK Environment
- Accessing the NLTK Corpora
![Hands On Experience skill]()
Hands-on/Demo
- Install NLTK Packages using NLTK Downloader
- Accessing your operating system using the OS Module in Python
- How to read json format, understand key-value pairs, and for that matter, understand uses of pkl files
![skill you will learn skill]()
Skills
- Reading & Writing .txt Files from/to your Local
- Reading & Writing .docx Files from/to your Local
- Working with the NLTK Corpora
Extracting, Cleaning and Preprocessing Text
Topics
- Tokenization
- Frequency Distribution
- Different Types of Tokenizers
- Bigrams, Trigrams & Ngrams
- Stemming
- Lemmatization
- Stopwords
- POS Tagging
- Named Entity Recognition
![Hands On Experience skill]()
Hands-on/Demo
- Regex, Word, Blankline, Sentence Tokenizers
- Bigrams, Trigrams & Ngrams
- Stopword Removal
- UTF encoding, dealing with URLs, hashtags
- POS Tagging
- Named Entity Recognition (NER)
![skill you will learn skill]()
Skills
- Tokenization
- Stopword Removal
- UTF encoding
- POS Tagging
- Named Entity Recognition (NER)
Analyzing Sentence Structure
Topics
- Syntax Trees
- Chunking
- Chinking
- Context Free Grammars (CFG)
- Automating Text Paraphrasing
![Hands On Experience skill]()
Hands-on/Demo
- Parsing Syntax Trees
- Chunking
- Chinking
- Automate Text Paraphrasing using CFG’s
![skill you will learn skill]()
Skills
- Chunking
- Chinking
- Automate Text Paraphrasing
Text Classification-I
Topics
- Machine Learning: Brush Up
- Bag of Words
- Count Vectorizer
- Term Frequency (TF)
- Inverse Document Frequency (IDF)
![Hands On Experience skill]()
Hands-on/Demo
- Demonstrate Bag of Words Approach
- Working with CountVectorizer()
- Using TF & IDF
![skill you will learn skill]()
Skills
- Bag of Words
- CountVectorizer()
- TF-IDF
Introduction to Deep Learning
Topics
- What is Deep Learning?
- Curse of Dimensionality
- Machine Learning vs. Deep Learning
- Use cases of Deep Learning
- Human Brain vs. Neural Network
- What is Perceptron?
- Learning Rate
- Epoch
- Batch Size
- Activation Function
- Single Layer Perceptron
![Hands On Experience skill]()
Hands-on/Demo
- Single Layer Perceptron
![skill you will learn skill]()
Skills
- Curse of Dimensionality
- Single Layer Perceptron
Getting Started with TensorFlow 2.0
Topics
- Introduction to TensorFlow 2.x
- Installing TensorFlow 2.x
- Defining Sequence model layers
- Activation Function
- Layer Types
- Model Compilation
- Model Optimizer
- Model Loss Function
- Model Training
- Digit Classification using Simple Neural Network in TensorFlow 2.x
- Improving the model
- Adding Hidden Layer
- Adding Dropout
- Using Adam Optimizer
![Hands On Experience skill]()
Hands-on/Demo
- Classifying handwritten digits using TensorFlow 2.0
![skill you will learn skill]()
Skills
- Installing and Working with TensorFlow 2.0
Convolution Neural Network
Topics
- Image Classification Example
- What is Convolution
- Convolutional Layer Network
- Convolutional Layer
- Filtering
- ReLU Layer
- Pooling
- Data Flattening
- Fully Connected Layer
- Predicting a cat or a dog
- Saving and Loading a Model
- Face Detection using OpenCV
![Hands On Experience skill]()
Hands-on/Demo
- Saving and Loading a Model
- Face Detection using OpenCV
![skill you will learn skill]()
Skills
- Image Classification using CNN
- Face Detection using OpenCV
Regional CNN
Topics
- Regional-CNN
- Selective Search Algorithm
- Bounding Box Regression
- SVM in RCNN
- Pre-trained Model
- Model Accuracy
- Model Inference Time
- Model Size Comparison
- Transfer Learning
- Object Detection – Evaluation
- mAP
- IoU
- RCNN – Speed Bottleneck
- Fast R-CNN
- RoI Pooling
- Fast R-CNN – Speed Bottleneck
- Faster R-CNN
- Feature Pyramid Network (FPN)
- Regional Proposal Network (RPN)
- Mask R-CNN
![Hands On Experience skill]()
Hands-on/Demo
- Transfer Learning
- Object Detection
![skill you will learn skill]()
Skils
- Transfer Learning
- Object Detection
- Mask R-CNN
Boltzmann Machine & Autoencoder
Topics
- What is Boltzmann Machine (BM)?
- Identify the issues with BM
- Why did RBM come into the picture?
- Step-by-step implementation of RBM
- Distribution of Boltzmann Machine
- Understanding Autoencoders
- Architecture of Autoencoders
- Brief on types of Autoencoders
- Applications of Autoencoders
![Hands On Experience skill]()
Hands-on/Demo
- Implement RBM
- Simple encoder
![skill you will learn skill]()
Skills
- RBM
- Autoencoders
Generative Adversarial Network(GAN)
Topics
- Which Face is Fake?
- Understanding GAN
- What is Generative Adversarial Network?
- How does GAN work?
- Step by step Generative Adversarial Network implementation
- Types of GAN
- Recent Advances: GAN
![Hands On Experience skill]()
Hands-on/Demo
- Implement Generative Adversarial Network
![skill you will learn skill]()
Skills
- Generative Adversarial Network
Emotion and Gender Detection (Self-paced)
Topics
- Where do we use Emotion and Gender Detection?
- How does it work?
- Emotion Detection architecture
- Face/Emotion detection using Haar Cascade
- Implementation on Colab
![Hands On Experience skill]()
Hands-on/Demo
- Implement Emotion and Gender Detection
![skill you will learn skill]()
Skills
- Emotion and Gender Detection
Introduction to RNN and GRU (Self-paced)
Topics
- Issues with Feed Forward Network
- Recurrent Neural Network (RNN)
- Architecture of RNN
- Calculation in RNN
- Backpropagation and Loss calculation
- Applications of RNN
- Vanishing Gradient
- Exploding Gradient
- What is GRU?
- Components of GRU
- Update gate
- Reset gate
- Current memory content
- Final memory at current time step
![Hands On Experience skill]()
Hands-on/Demo
- Implement COVID RNN GRU
![skill you will learn skill]()
Skills
- RNN
- GRU
LSTM (Self-paced)
Topics
- What is LSTM?
- Structure of LSTM
- Forget Gate
- Input Gate
- Output Gate
- LSTM architecture
- Types of Sequence-Based Model
- Sequence Prediction
- Sequence Classification
- Sequence Generation
- Types of LSTM
- Vanilla LSTM
- Stacked LSTM
- CNN LSTM
- Bidirectional LSTM
- How to increase the efficiency of the model?
- Backpropagation through time
- Workflow of BPTT
![Hands On Experience skill]()
Hands-on/Demo
- Intent Detection using LSTM
![skill you will learn skill]()
Skills
- LSTM
- Sequence Prediction
- Sequence Generation
Auto Image Captioning Using CNN LSTM (Self-paced)
Topics
- Auto Image Captioning
- COCO dataset
- Pre-trained model
- Inception V3 model
- The architecture of Inception V3
- Modify the last layer of a pre-trained model
- Freeze model
- CNN for image processing
- LSTM or text processing
![Hands On Experience skill]()
Hands-on/Demo
- Auto Image Captioning
![skill you will learn skill]()
Skills
- Auto Image Captioning
- CNN for image processing
- LSTM or text processing
Developing a Criminal Identification and Detection Application Using OpenCV (Self-paced)
Topics
- Why is OpenCV used?
- What is OpenCV
- Applications
- Demo: Build a Criminal Identification and Detection App
![Hands On Experience skill]()
Hands-on/Demo
- Build a Criminal Identification and Recognition app on Streamlit.
![skill you will learn skill]()
Skills
- OpenCV
- Project Implementation with OpenCV
TensorFlow for Deployment (Self-paced)
Topics
- Use Case: Amazon’s Virtual Try-Out Room.
- Why Deploy models?
- Model Deployment: Intuit AI models
- Model Deployment: Instagram’s Image Classification Models
- What is Model Deployment
- Types of Model Deployment Techniques
- TensorFlow Serving
- Browser-based Models
- What is TensorFlow Serving?
- What are Servables?
- Demo: Deploy the Model in Practice using TensorFlow Serving
- Introduction to Browser based Models
- Demo: Deploy a Deep Learning Model in your Browser.
![Hands On Experience skill]()
Hands-on/Demo
- Learn and build a program that Detects Faces using your webcam using OpenCV.
- Learn Hyper parameter tuning techniques in Keras on a Fashion Dataset.
- Build and deploy a model using TensorFlow Serving.
- Build a neural network model for Handwritten digits use activation function, batch size, Optimizer and learning rate for betterment of you model.
- Build a Object detection model and detection is done by providing a video the model accurately identifies the objects that are depicted in the video.
![skill you will learn skill]()
Skills
- Deploying model with Tensorflow
Text Classification-II (Self-paced)
Topics
- Converting text to features and labels
- Multinomial Naive Bayes Classifier
- Leveraging Confusion Matrix
![Hands On Experience skill]()
Hands-on/Demo
- Converting text to features and labels
- Demonstrate text classification using Multinomial NB Classifier
- Leveraging Confusion Matri
![skill you will learn skill]()
Skills
- Converting text to features and labels
- Text classification
- Confusion Matrix
In Class Project (Self-paced)
Topics
- Sentiment Classification on Movie Rating Dataset
![Hands On Experience skill]()
Hands-on/Demo
- Implement all the text processing techniques starting with tokenization
- Express your end to end work on Text Mining
- Implement Machine Learning along with Text Processing
![skill you will learn skill]()
Skills
- Sentiment Analysis
Artificial Certification Course in Singapore Description
What is the Artificial Intelligence Course in Singapore?
How will Artificial Intelligence help your career in Singapore?
- Increased job opportunities: There is a growing demand for AI professionals in both technical and non-technical roles. Technical roles include AI engineers, data scientists, machine learning engineers, and software developers, while non-technical roles include AI project managers, AI consultants, and AI analysts.
- Advancements in technology: As AI technology advances, there are new opportunities for AI professionals to develop new applications and solutions that can solve complex problems.
- Emerging subfields: There are emerging subfields within AI, such as explainable AI, AI ethics, and AI security, which provide new opportunities for AI professionals to specialize and grow their careers.
- Continuous learning and development: AI is a constantly evolving field, and AI professionals must continuously learn and develop new skills to stay up-to-date with the latest advancements.
What are the essential concepts covered in this Artificial Intelligence Course in Singapore?
Who should take up this Artificial Intelligence Course in Singapore?
- Freshers
- Python Developers
- Researchers
- Data Scientists
- Data Analysts
- Machine Learning Engineers
- NLP Engineers
- Software Testers
- Software Developers
What are the basic skills of a Artificial Intelligence Engineer in Singapore?
- Strong Programming Skills: AI engineers need to have a strong foundation in programming languages such as Python, C++, Java, or R. This includes knowledge of data structures, algorithms, and object-oriented programming.
- Machine Learning: AI engineers must have a solid understanding of the concepts and algorithms of machine learning. This includes knowledge of supervised and unsupervised learning, deep learning, and natural language processing (NLP).
- Data Structures and Algorithms: A deep understanding of data structures and algorithms is essential for designing and implementing efficient algorithms for large data sets. This also includes knowledge of big data technologies and distributed computing.
- Statistics and Probability: Knowledge of statistics and probability is essential for understanding and designing machine learning algorithms. AI engineers need to know concepts like hypothesis testing, regression analysis, and Bayesian networks.
- Problem Solving: AI engineers must have strong problem-solving skills to design and implement complex AI systems. They must be able to identify problems, break them down into smaller components, and develop solutions.
- Creativity: AI engineers must be creative thinkers to develop novel solutions to complex problems. They should be able to think outside the box and come up with innovative ideas.
- Ethics and Accountability: AI engineers must understand the ethical implications of their work and the impact it has on society. They must ensure their AI systems are transparent, explainable, and accountable.
What are the prerequisites for this Artificial Intelligence Course in Singapore?
What is the main focus of Edureka’s Artificial Intelligence Course in Singapore?
What will I learn from this Artificial Intelligence Course in Singapore?
What are the system requirements for this Artificial Intelligence Course in Singapore?
- A system with an Intel i3 processor or above
- A minimum of 4GB RAM (8GB or above is recommended for faster processing)
- 50 GB HDD Storage
- Operating system: 32-bit or 64-bit
How will I execute the practicals in this Artificial Intelligence Course in Singapore?
Artificial Intelligence Course in Singapore Projects
Artificial Intelligence Certification in Singapore
- Completely participate in this Artificial Intelligence course.
- Evaluation and completion of the quizzes and projects listed.
- Demonstrates expertise: An AI certification can help demonstrate your proficiency in a specific area of AI technology, such as machine learning or natural language processing.
- Increased job opportunities: Having an AI certification can help open doors to job opportunities that require knowledge of AI technology. This can include jobs in data analysis, software development, and other technical roles.
- Higher salary potential: In general, individuals with specialized technical skills, such as AI, can command higher salaries than those without such skills. An AI certification can help increase your earning potential.
- Competitive advantage: An AI certification can help you stand out from other candidates when applying for jobs or pursuing business opportunities.
- Professional development: Pursuing an AI certification can help you continue to learn and grow in your field, keeping you up-to-date with the latest technology and trends.
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Artificial Intelligence Course in Singapore FAQs
What is Artificial Intelligence?
What is Deep Learning?
Can I attend a demo Artificial Intelligence session before enrollment?
Who are the instructors for this Artificial Intelligence Course?
How can I become an AI Engineer?
To become an AI engineer, you can follow these steps:
Learn programming: Start with languages like Python, Java, or C++, and gain proficiency in data structures and algorithms.
Understand mathematics and statistics: Study linear algebra, calculus, probability, and statistics to grasp the foundations of AI.
Master machine learning: Learn about various ML algorithms, techniques, and frameworks such as TensorFlow or PyTorch.
Gain practical experience: Work on real-world projects, participate in Kaggle competitions and build a portfolio to showcase your skills.
Specialize in AI subfields: Explore areas like natural language processing, computer vision, or reinforcement learning.
Continuous learning: Stay updated with the latest advancements and research in AI through online courses, tutorials, and academic papers.
What if I have more queries after this AI Course?
What if I miss a Artificial Intelligence Training class?
Which is the best AI Online Course for beginners?
Where can I learn artificial intelligence?
What is the course duration of the AI Training Course?
What online courses that are available on Edureka will help me to learn about Artificial Intelligence?
What is the Salary of an AI Engineer in India?
The salary of an AI engineer in India can vary depending on factors such as experience, skillset, location, industry, and the organization's size. On average, AI engineers in India can expect to earn a salary ranging from INR 5 lakh to INR 20 lakh per year. However, it's important to note that these figures are approximate and can vary significantly based on the factors mentioned earlier. Highly skilled and experienced AI engineers working in top tech companies or specialized fields may command higher salaries, potentially exceeding INR 20 lakh per year. Additionally, AI engineers with advanced degrees or certifications and those with expertise in specific subfields of AI, such as natural language processing or computer vision, may have better salary prospects. It's essential to refer to up-to-date salary surveys and resources for the most accurate and recent information on AI engineer salaries in India.
What kind of projects are included as part of the AI Certification Course?
As part of AI Courseto become an AI engineer, you may work on various projects focusing on different aspects of AI. Some common project areas include:
Machine Learning: Projects involving training models for classification, regression, or recommendation systems. Examples could include building a spam email classifier or predicting house prices.
Natural Language Processing (NLP): Projects related to language understanding and generation, such as sentiment analysis, chatbots, or language translation systems.
Computer Vision: Projects that deal with image or video analysis, such as object detection, facial recognition, or autonomous driving systems.
Reinforcement Learning: Projects centred around training agents to make decisions in dynamic environments, like teaching a robot to navigate a maze or playing complex games.
Data Analysis: Projects involving exploratory data analysis, data preprocessing, and feature engineering, often using statistical techniques and visualizations.
These projects aim to provide hands-on experience in applying AI techniques to solve real-world problems and help you develop practical skills and understanding in the field of AI engineering.


