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Artificial Intelligence Certification Course in Australia
Artificial Intelligence Certification Course in Australia
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Live Online Classes starting on 22nd Jul 2023
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Why enroll for Artificial Intelligence Certification Course in Australia?
Artificial Intelligence Course in Australia Benefits
Today, the amount of data that is generated, by both humans and machines, far outpaces humans' ability to absorb, interpret, and make complex decisions based on that data. Artificial intelligence forms the basis for all computer learning, allows organizations to improve core business processes and is the future of all complex decision making. The best way to land you a good job with a handsome salary in this domain is to get AI Certification.
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Why Artificial Intelligence Certification Course from edureka in Australia
Live Interactive Learning
- World-Class Instructors
- Expert-Led Mentoring Sessions
- Instant doubt clearing
Lifetime Access
- Course Access Never Expires
- Free Access to Future Updates
- Unlimited Access to Course Content
24x7 Support
- One-On-One Learning Assistance
- Help Desk Support
- Resolve Doubts in Real-time
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 Course Curriculum in Australia
Curriculum Designed by Experts
Introduction to Text Mining and NLP
8 Topics
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/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
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
9 Topics
Topics
- Tokenization
- Frequency Distribution
- Different Types of Tokenizers
- Bigrams, Trigrams & Ngrams
- Stemming
- Lemmatization
- Stopwords
- POS Tagging
- Named Entity Recognition
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)
Skills
- Tokenization
- Stopword Removal
- UTF encoding
- POS Tagging
- Named Entity Recognition (NER)
Analyzing Sentence Structure
5 Topics
Topics
- Syntax Trees
- Chunking
- Chinking
- Context Free Grammars (CFG)
- Automating Text Paraphrasing
Hands-on/Demo
- Parsing Syntax Trees
- Chunking
- Chinking
- Automate Text Paraphrasing using CFG’s
Skills
- Chunking
- Chinking
- Automate Text Paraphrasing
Text Classification-I
5 Topics
Topics
- Machine Learning: Brush Up
- Bag of Words
- Count Vectorizer
- Term Frequency (TF)
- Inverse Document Frequency (IDF)
Hands-on/Demo
- Demonstrate Bag of Words Approach
- Working with CountVectorizer()
- Using TF & IDF
Skills
- Bag of Words
- CountVectorizer()
- TF-IDF
Introduction to Deep Learning
11 Topics
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/Demo
- Single Layer Perceptron
Skills
- Curse of Dimensionality
- Single Layer Perceptron
Getting Started with TensorFlow 2.0
14 Topics
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/Demo
- Classifying handwritten digits using TensorFlow 2.0
Skills
- Installing and Working with TensorFlow 2.0
Convolution Neural Network
12 Topics
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/Demo
- Saving and Loading a Model
- Face Detection using OpenCV
Skills
- Image Classification using CNN
- Face Detection using OpenCV
Regional CNN
20 Topics
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/Demo
- Transfer Learning
- Object Detection
Skils
- Transfer Learning
- Object Detection
- Mask R-CNN
Boltzmann Machine & Autoencoder
9 Topics
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/Demo
- Implement RBM
- Simple encoder
Skills
- RBM
- Autoencoders
Generative Adversarial Network(GAN)
7 Topics
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/Demo
- Implement Generative Adversarial Network
Skills
- Generative Adversarial Network
Emotion and Gender Detection (Self-paced)
5 Topics
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/Demo
- Implement Emotion and Gender Detection
Skills
- Emotion and Gender Detection
Introduction to RNN and GRU (Self-paced)
14 Topics
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/Demo
- Implement COVID RNN GRU
Skills
- RNN
- GRU
LSTM (Self-paced)
18 Topics
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/Demo
- Intent Detection using LSTM
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/Demo
- Auto Image Captioning
Skills
- Auto Image Captioning
- CNN for image processing
- LSTM or text processing
Developing a Criminal Identification and Detection Application Using OpenCV (Self-paced)
4 Topics
Topics
- Why is OpenCV used?
- What is OpenCV
- Applications
- Demo: Build a Criminal Identification and Detection App
Hands-on/Demo
- Build a Criminal Identification and Recognition app on Streamlit.
Skills
- OpenCV
- Project Implementation with OpenCV
TensorFlow for Deployment (Self-paced)
13 Topics
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/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.
Skills
- Deploying model with Tensorflow
Text Classification-II (Self-paced)
17 Topics
Topics
- Converting text to features and labels
- Multinomial Naive Bayes Classifier
- Leveraging Confusion Matrix
Hands-on/Demo
- Converting text to features and labels
- Demonstrate text classification using Multinomial NB Classifier
- Leveraging Confusion Matri
Skills
- Converting text to features and labels
- Text classification
- Confusion Matrix
In Class Project (Self-paced)
1 Topics
Topics
- Sentiment Classification on Movie Rating Dataset
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
Skills
- Sentiment Analysis
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Artificial Certification Course in Australia Description
What is the Artificial Intelligence Course in Australia?
Edureka’s Artificial Intelligence Course in Australia is well-researched amalgamation of Natural Language Processing and Deep Learning, specifically designed for professionals and beginners to meet the industry standards. This course gives you an in-depth understanding of Tokenization, Stemming, Lemmatization, POS tagging, Named Entity Recognition, Syntax Tree Parsing using Python’s NLTK package, CNN, RCNN, RNN, LSTM, RBM, and their implementation using TensorFlow 2.0 package. You will learn to build real-time projects on NLP and Deep Learning, to make you industry-ready and help you to kickstart your career in this domain.
Why take up the Online Artificial Intelligence Course in Australia?
The demand for AI engineers is increasing rapidly and is expected to continue growing in the future, driven by the increasing adoption of AI technologies in various industries and the growing importance of AI skills in the job market.
There are many reasons why someone might want to take up an online artificial intelligence course. Here are a few:
How will Artificial Intelligence help your career in Australia?
The field of artificial intelligence (AI) is rapidly growing and is expected to continue growing in the coming years. AI is being used in many industries such as healthcare, finance, education, and more. With the increasing adoption of AI, there is a high demand for professionals with the necessary skills and knowledge to work in this field.
Here are some ways AI career growth is happening:
Overall, the field of AI provides many opportunities for career growth and development, and there is a high demand for skilled professionals in this field. Enroll in this Artificial Intelligence training today.
What are the essential concepts covered in this Artificial Intelligence Course in Australia?
This Artificial Intelligence Course in Australia provides in-depth knowledge of concepts such as Natural Language Processing, Text Classification, Text Processing, Image Processing, Object Detection, Deep Learning, TensorFlow, OpenCV, and many more.
Who should take up this Artificial Intelligence Course in Australia?
The Artificial Intelligence course in Australia is suitable for anyone who wants to stay up-to-date with the latest advances in AI and wants to build the skills needed to develop and deploy intelligent systems.
This course will be ideal for the following professionals.
If you are one of the above, then do not hesitate to talk to our assistant team and enroll in our AI Certification training today.
What are the basic skills of a Artificial Intelligence Engineer in Australia?
Artificial Intelligence (AI) is a broad field with many subfields, and the skills required for an AI engineer can vary depending on the specific area of expertise. However, there are some basic skills that most AI engineers should possess:
What are the prerequisites for this Artificial Intelligence Course in Australia?
Prior knowledge of Python and Machine Learning will be helpful but not at all mandatory. To refresh your skills in Python and ML, we will provide self-paced videos absolutely free as prerequisites in your LMS.
What is the main focus of Edureka’s Artificial Intelligence Course in Australia?
Edureka’s Artificial Intelligence Course in Australia enables you to move ahead in your career by helping you get skilled with the fundamentals of AI. The course focuses on providing hands-on experience to make you ready for any AI related opportunity.
What will I learn from this Artificial Intelligence Course in Australia?
Learn the fundamentals of Natural Language Processing (NLP), sentiment analysis, language translation, text summarization, deep learning, convolutional neural networks, recurrent neural networks, and autoencoders. Additionally, you will be working with the OpenCV library, object detection, image segmentation, and image classification along with various real-life projects.
What are the system requirements for this Artificial Intelligence Course in Australia?
How will I execute the practicals in this Artificial Intelligence Course in Australia?
You will execute your Assignments/Case Studies using Python Jupyter Notebook/Google Colab. Detailed step-by-step installation guides are available on the LMS. In case you come across any doubt, the 24*7 support team will promptly assist you.
Artificial Intelligence Course in Australia Projects
Airline Sentiment Analysis
In this project, you will be provided with a dataset having tweets about 6 US Airlines along with their sentiments: positive, negative and neutral. You have to perform Sentiment ....Face Mask Detection
The goal of the project is to create a Deep Learning model to detect in real time whether a person is wearing a face mask or not.Artificial Intelligence Certification in Australia
To unlock Edureka’s Artificial Intelligence course completion certificate, you must ensure the following:
- Completely participate in this Artificial Intelligence course.
- Evaluation and completion of the quizzes and projects listed.
Yes, Artificial Intelligence (AI) is a rapidly growing field with many opportunities for career growth and development. AI has the potential to transform industries such as healthcare, finance, transportation, and more, and as a result, there is a high demand for skilled professionals in this field.
There are many career paths available in AI, including machine learning engineer, data scientist, AI researcher, AI consultant, and more. These roles often require a combination of technical skills, such as programming and data analysis, as well as soft skills like communication and problem-solving.
Furthermore, the field of AI is constantly evolving, with new technologies and techniques emerging regularly. This means that AI professionals need to stay up-to-date with the latest developments and continue learning throughout their careers, which can make for an exciting and challenging work environment.
Overall, if you have a strong interest in technology and a passion for problem-solving, a career in AI could be a great choice.
Artificial Intelligence (AI) is a complex and rapidly evolving field, learning its capabilities and functionality requires appropriate direction and a well-structured training path. Beginners interested in a career in Artificial Intelligence using Python can sign up for our training and earn certificates to demonstrate their expertise in this domain.
Artificial intelligence (AI) certification is becoming increasingly valuable in today's job market, as businesses across all industries are seeking to integrate AI technology into their operations. Obtaining an AI certification can demonstrate to potential employers that you possess the skills and knowledge necessary to work with AI and can help you stand out from other candidates.
Here are some potential benefits of obtaining an AI certification:
- 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.
Overall, obtaining an AI certification can be a valuable investment in your career and can help you stay competitive in the rapidly evolving job market.
Our Artificial Intelligence course is designed to develop skills and evaluate candidates' knowledge. Following the completion of this certification, you will have access to a wide range of job possibilities. Some of the most important employment roles include AI Engineer, Data Scientist, NLP Engineer, Deep Learning Engineer, Machine Learning Engineer, and others.
Please visit the page which will guide you through the top Artificial Intelligence Interview Questions.
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Artificial Intelligence Course in Australia FAQs
What if I miss a Artificial Intelligence Training class?
You will never miss a lecture at Edureka! You can choose either of the two options:
View the recorded session of the class available in your LMS.
You can attend the missed session, in any other live batch.
Will I get placement assistance?
To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.
Can I attend a demo Artificial Intelligence session before enrollment?
We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.
Who are the instructors for this Artificial Intelligence Course?
All the instructors at edureka are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience to the participants.
What if I have more queries?
Just give us a CALL at +91 98702 76459/1844 230 6365 (US Tollfree Number) OR email at sales@edureka.co
What online courses that are available on Edureka will help me to learn about Artificial Intelligence?
Some of the top courses are
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