Certified Artificial

Intelligence Expert

Become a certified Artificial intelligence expert
and take your career to new heights

5 Days / 40 Hours / 9 Modules
+ 3 month coaching + 1 year eLearning access

Get in Touch With Us
Certified Artificial

Intelligence Expert

Become a certified Artificial
intelligence expert and take
your career to new heights

5 Days / 40 Hours / 9 Modules
+ 3 month coaching + 1 year eLearning access

Become a certified Artificial intelligence expert and take your career to new heights

The Certified Artificial Intelligence Expert course provided by IABAC renders 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.
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
IABAC Exam Information
Course Modules Covered in the Artificial Intelligence Foundation program
Module 1 - Machine Learning Primer

Machine Learning Primer

  1. Machine Learning Primer
  2. Machine Learning core concepts, scalable algorithms, project workflow.
  3. Objective Functions and Regularization
  4. Understanding Objective Function of ML Algorithms
  5. Metrics, Evaluation Methods and Optimizers
  6. Popular Metrics in Detail: R2 Score, RMSE, Cross Entropy, Precision, Recall, F1 Score, ROC-AUC, SGD, ADAM
  7. Artificial Neural Network
  8. ANN in detail, Forward Pass and Back Propagation
  9. Machine Learning Vs Deep Learning
  10. Core difference b/w ML and DL from implementation perspective
Module 2 - Advanced Python For Deep Learning

Advanced Python For Deep Learning

  1. Python Programming Primer
  2. Installing Python, Programming Basics, Native Data types
  3. Class, Inheritance and Magic Functions
  4. Python Classes, Inheritance Concepts, Magic Functions
  5. Special Functions in Python
  6. Overview, Array, selecting data, Slicing, Iterating, Array Manipulations, Stacking, Splitting arrays, Key functions
  7. Decorators and Special Functions
  8. Decorators implementation with class
  9. Context Manager ‘with’ in Python
  10. Context Manager Application
  11. Exception Handling
  12. Try and Catch block
  13. Python Package Management
  14. Bundling and export python packages
Module 3 - Tensorflow 2.0 And Keras For Deep Learning

Tensorflow 2.0 And Keras For Deep Learning

  1. TensorFlow 2.0 Basics
  2. TensorFlow core concepts, Tensors, core APIs
  3. Concrete Functions, Datatypes, Control Statements
  4. Polymorphic Functions, Concrete Functions, Datatypes, Control Statements, NumPy, Pandas
  5. Autograph eager execution
  6. tf.function autograph implementation
  7. Sessions vs tf.function
  8. Keras (TensorFlow 2.0 Built-in API) Overview
  9. Sequential Models, configuring layers, loading data, train and test, complex models, call backs, save and restore Neural Network weights
  10. Building Neural Networks in Keras
  11. Building Neural networks from scratch in Keras
  12. Implementing RNN, CNN in Keras
  13. Building Recurrent Neural Networks for sequence data and Convolution Neural Networks for Image Classification
Module 4 - Mathematics For Deep Learning

Mathematics For Deep Learning

  1. Linear Algebra
  2. Vectors, Matrices, Linear Transformation, Eigen Vectors, Matrix Operations, Special Matrices
  3. Calculus – Derivatives: Calculus essentials, Derivatives and Partial Derivatives, Chain Rule, Derivatives of special functions
  4. Probability Essentials: Probability basics and notations, Conditional probability, Essential Probability theorems for Machine Learning
  5. Special functions: Relu, Sigmoid, SoftMax, Popular Loss Functions – Cross Entropy, Quadratic Loss Functions
Module 5 - Deep Learning Foundation

Deep Learning Foundation

  1. Deep Learning Network Concepts
  2. Core concepts of Deep Learning Networks
  3. Deep Dive into Activation Functions
  4. Relu, Sigmoid, Tanh, SoftMax, Linear
  5. Building simple Deep Learning Network
  6. Simple DL network from starch
  7. Tuning Deep Learning Network
  8. Tuning Deep Learning Network Parameters for optimal performance, Stopping Criteria
  9. Visualizing Training using TensorBoard
  10. Visualizing Deep Learning Network using TensorBoard
Module 6 - Advanced Deep Learning - CNN,RNN,LSTM RNN

Advanced Deep Learning - CNN,RNN,LSTM RNN

  1. Deep Learning Architectures
  2. Popular Deep learning Architectures – CNN, RNN, LSTM RNN, GRU RNN Introduction
  3. Deep Dive into Convolutional Neural Network
  4. Core Concepts of Convolutional Neural Network, Feature Maps, Relu Activation, Max Pooling
  5. CNN Application – Image Classification
  6. Image Classification implementation with CNN TensorFlow 2.0 (Keras)
  7. Recurrent Neural Networks (RNN) Basics
  8. RNN Architecture, BPTT Backprop through time, Mathematics of RNN
  10. Vanishing Gradient and exploding Gradient problem, LTSM architecture, GRU Architecture.
  11. LSTM RNN implementation in TensorFlow
  12. LSTM RNN project.
Module 7 - Natural Language Processing

Natural Language Processing

  1. Introduction to Natural Language Processing
  2. Language modelling
  3. Sequence to Sequence Models
  4. Transformers and BERT
Module 8 - Computer Vision

Computer Vision

  1. Introduction to Computer Vision
  2. Image Representation and Analysis
  3. Convolutional Neural Network
  4. Features and Object Recognition
  5. Image Segmentation
  6. Advanced CNN Architectures
  7. Recurrent Neural Networks
  8. Attention Mechanisms
Module 9 - Reinforcement Learning Introduction

Reinforcement Learning Introduction

  1. Reinforcement learning framework
  2. Dynamic programming algorithms
  3. Monte Carlo Methods
  4. Temporal Difference Methods
  5. Gym - OpenAI Framework Introduction
  6. Deep Q-Learning
  7. Introduction to Multi-Agent RL
Our Training Methodology
Program Key Highlights

40 hours of Remote Online Learning
Additional Coaching Hours
Live Hands-on Projects
Certified by International Body
Mentorship with Industry Experts
Designed for Beginners & Professionals
Program Benefits

Get Professionally Certified

Upon successfully completing this program, participants will be awarded the Artificial Intelligence Expert Certification by International Association of Business Analytics Certification (IABAC).
This award is a validation to the efforts taken to master the domain expertise that will set you apart from your competition.
Be a part of the global network of data science professionals and join the community across sectors.
Get in Touch With Us Today!

This training program is suitable for anyone who intends to enter into the field of Artificial Intelligence. This program is being conducted in Malaysia and can be joined by anyone, anywhere in the world remotely.
Program Fee

MYR 6800 per pax.
Funding price MYR 3700 per pax .

Funding Schemes for Companies who are claiming from their HRDF levy or from the MDEC MyWiT scheme.

Limited scholarships available for early self applying individual applicants.

Find out how you can qualify for a scholarship.

Enquire NOW on the various funding options available.

One-time fee. One year access to course materials and resources.

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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