Become a Generative Artificial Intelligence Associate and take your career to new heights
The Certified Artificial Intelligence Associate course provided by t-Academy a picture of the concepts of AI and their industrial applications. The syllabus consists of Deep Learning Foundations, Advanced Python for Deep Learning , Natural Language Processing.
Duration
3 days / 24 hours
Level
Beginner to Intermediate
Delivery
100% Online - Instructor Led
Request For Information
Key Features
- 24 hours of instructor led training
- Fully Online
- Class recording available
- Interactive Learning
- Additional Coaching Session
Pre-Requisites
- Generative Machine Learning Associate Course (CMLA) or Demonstrable competence at CMLA level
- 2. Recommended essential knowledge in :
a. Mathematics: Calculus, Statistics, Linear Algebra, Probability
b. Machine Learning and Python/R Programming - 3. Training: Though formal training is not mandatory; it is recommended to attend IABAC® registered course through Registered Education Partner
Who Should Join
- Individuals pursuing a career in Artificial Intelligence
- Beginners and students with good Machine Learning knowledge aspiring a career in Artificial Intelligence
Key Learning Outcomes
Upon completion, participants should be able to demonstrate each of the following outcome:-
- Master python for artificial intelligence projects
- Become proficient with tools such as Tensorflow 2.0 and KERAS for deep learning
- Develop a solid understanding of deep learning
- Understand and apply core concepts of deep learning and natural language processing in artificial intelligence projects
Course Modules Covered in the Artificial Intelligence Foundation program
Module 1 - Machine Learning Primer
Machine Learning Primer
- Machine Learning Primer
- Machine Learning core concepts, scalable algorithms, project workflow.
- Objective Functions and Regularization
- Understanding Objective Function of ML Algorithms
- Metrics, Evaluation Methods and Optimizers
- Popular Metrics in Detail: R2 Score, RMSE, Cross Entropy, Precision, Recall, F1 Score, ROC-AUC, SGD, ADAM
- Artificial Neural Network
- ANN in detail, Forward Pass and Back Propagation
- Machine Learning Vs Deep Learning
- Core difference b/w ML and DL from implementation perspective
Module 2 - Advanced Python For Deep Learning
Advanced Python For Deep Learning
- Python Programming Primer
- Installing Python, Programming Basics, Native Data types
- Class, Inheritance and Magic Functions
- Python Classes, Inheritance Concepts, Magic Functions
- Special Functions in Python
- Overview, Array, selecting data, Slicing, Iterating, Array Manipulations, Stacking, Splitting arrays, Key functions
- Decorators and Special Functions
- Decorators implementation with class
- Context Manager ‘with’ in Python
- Context Manager Application
- Exception Handling
- Try and Catch block
- Python Package Management
- Bundling and export python packages
Module 3 - Tensorflow 2.0 And Keras For Deep Learning
Tensorflow 2.0 And Keras For Deep Learning
- TensorFlow 2.0 Basics
- TensorFlow core concepts, Tensors, core APIs
- Concrete Functions, Datatypes, Control Statements
- Polymorphic Functions, Concrete Functions, Datatypes, Control Statements, NumPy, Pandas
- Autograph eager execution
- tf.function autograph implementation
- Sessions vs tf.function
- Keras (TensorFlow 2.0 Built-in API) Overview
- Sequential Models, configuring layers, loading data, train and test, complex models, call backs, save and restore Neural Network weights
- Building Neural Networks in Keras
- Building Neural networks from scratch in Keras
- Implementing RNN, CNN in Keras
- Building Recurrent Neural Networks for sequence data and Convolution Neural Networks for Image Classification
Module 4 - Mathematics For Deep Learning
Mathematics For Deep Learning
- Linear Algebra
- Vectors, Matrices, Linear Transformation, Eigen Vectors, Matrix Operations, Special Matrices
- Calculus – Derivatives: Calculus essentials, Derivatives and Partial Derivatives, Chain Rule, Derivatives of special functions
- Probability Essentials: Probability basics and notations, Conditional probability, Essential Probability theorems for Machine Learning
- Special functions: Relu, Sigmoid, SoftMax, Popular Loss Functions – Cross Entropy, Quadratic Loss Functions
Module 5 - Deep Learning Foundation
Deep Learning Foundation
- Deep Learning Network Concepts
- Core concepts of Deep Learning Networks
- Deep Dive into Activation Functions
- Relu, Sigmoid, Tanh, SoftMax, Linear
- Building simple Deep Learning Network
- Simple DL network from starch
- Tuning Deep Learning Network
- Tuning Deep Learning Network Parameters for optimal performance, Stopping Criteria
- Visualizing Training using TensorBoard
- Visualizing Deep Learning Network using TensorBoard
Module 6 - Advanced Deep Learning - CNN,RNN,LSTM RNN
Advanced Deep Learning - CNN,RNN,LSTM RNN
- Deep Learning Architectures
- Popular Deep learning Architectures – CNN, RNN, LSTM RNN, GRU RNN Introduction
- Deep Dive into Convolutional Neural Network
- Core Concepts of Convolutional Neural Network, Feature Maps, Relu Activation, Max Pooling
- CNN Application – Image Classification
- Image Classification implementation with CNN TensorFlow 2.0 (Keras)
- Recurrent Neural Networks (RNN) Basics
- RNN Architecture, BPTT Backprop through time, Mathematics of RNN
- RNN, LTSM RNN and GRU RNN
- Vanishing Gradient and exploding Gradient problem, LTSM architecture, GRU Architecture.
- LSTM RNN implementation in TensorFlow
- LSTM RNN project.
Module 7 - Natural Language Processing
Natural Language Processing
- Introduction to Natural Language Processing
- Language modelling
- Sequence to Sequence Models
- Transformers and BERT
Module 8 - Computer Vision
Computer Vision
- Introduction to Computer Vision
- Image Representation and Analysis
- Convolutional Neural Network
- Features and Object Recognition
- Image Segmentation
- Advanced CNN Architectures
- Recurrent Neural Networks
- Attention Mechanisms
Module 9 - Reinforcement Learning Introduction
Reinforcement Learning Introduction
- Reinforcement learning framework
- Dynamic programming algorithms
- Monte Carlo Methods
- Temporal Difference Methods
- Gym - OpenAI Framework Introduction
- Deep Q-Learning
- Introduction to Multi-Agent RL
Your Instructor
Thayanithy Jegan
Principal Data Scientist
CTO & Co-Founder of Thulija Technologies,
HRDF Certified Trainer and Consultant
CTO & Co-Founder of Thulija Technologies,
HRDF Certified Trainer and Consultant
A seasoned technology professional with over 17 years of industry experience as a software developer, solutions architect and technology consultant for major organizations. He is the Principal Lead Data Scientist at Thulija Academy.
Thayanithy Jegan has trained executives and developers in some of the leading organizations in Asia such as Maxis, IFCA, SWIFT, PSDC, DHL, Standard Chartered, Infineon Technologies, Siemens and Bank Negara to name a few. His expertise is highly sought after to help executives break into various technology stacks and as well as data science, big data, and artificial intelligence in Malaysia and regionally.
He has led major projects with clients such as Suruhanjaya Syarikat Malaysia (SSM), MYCOID, Kementerian Kerja Raya, Ministry of Education (MOE), Universiti Malaya, Perfisio Solutions, Kementerian Perdagangan Antarabangsa dan Industri, amongst others. He has also served as a Consultant for MIMOS Berhad, a Research and Development organisation that functions as an advisor to the Malaysian Government on technologies, policies and strategies relating to IT.
Our Training Methodology
Practical Assignments
We provide hands-on assignments that requires practical implementation.
Virtual Coaching Sessions
Online coaching sessions that happen over the phone, via video, or on a web platform.
1 Year Access to LMS
Get access to learning resources upto 1 year of class completion.
Live Project Experience
Hands-on learning and training gives participants the opportunity to experience real world situations.
Online Assessments
Participants can assess reflect on their own learning and their level/skills.
Free Industry Webinars
Stay current on market research trends, learn best practices through our webinar sessions.
Get in Touch With Us Today!
This training program is suitable for anyone who intends to enter into the field of Generative A.I Associate. This program is being conducted in Malaysia and can be joined by anyone, anywhere in the world remotely.
Program Fee
Public program price MYR 2,400.00 per pax.
Final payable amount MYR 899.00 per pax
One-time fee. One year access to course materials and resources.
Thulija Academy is a HRDF registered training provider. Our panel of expert trainers provide technology training for some of the biggest organizations in Asia.
READY TO KICKSTART YOUR CAREER?
Please fill in the form and a Program Advisor will reach out to you. You can also reach out to us at info@thulija.com or +60123661502
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