Certified Deep
Learning Expert
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Become a certified deep learning expert
with mastery of Python, Tensorflow and KERAS
today!

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


Get in Touch With Us
Certified Deep
Learning Expert
Become a certified deep
learning expert with mastery of
Python, Tensorflow and KERAS
today!

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

logo
Get in Touch With Us
Become a certified deep learning expert with mastery of Python, Tensorflow and KERAS today!
Expert level certification in Artificial Intelligence enables the candidate to learn deep learning techniques with industry best practices. The course syllabus covers mathematics, machine learning, programming platform(Tensorflow), deep learning techniques, CNN, RNN, big data foundation, Tensorflow 2.0 and Keras for deep learning.

Duration

5 days / 40 hours

Level

Beginner to Intermediate

Delivery

100% Online - Instructor Led
Request For Information

    Key Features

    • 40 hours of instructor led training
    • Fully Online
    • Class recording available
    • Interactive Learning
    • Additional Coaching Session
    • 100% HRDF SBL-KHAS Claimable!

    Pre-Requisites

    • Certified Machine Learning Expert Course (CMLE) or Demonstrable competence at CMLE level
    • Programming Knowledge
    • Essential Knowledge in
      i. Calculus, Derivative, core concepts and theorems.
      ii. Statistics
      iii. Linear Algebra
      iv. Probability
      b. Machine Learning
    • Training: Though formal training is not mandatory; it is recommended to attend IABAC® registered course for Certified Data Scientist through Registered Education Partners

    Who Should Join

    • Professionals pursuing a career in Deep Learning
    • Beginners and students with good Machine Learning knowledge aspiring a career in Deep Learning
    Key Learning Outcomes
    Upon completion, participants should be able to demonstrate each of the following outcome:-
    • Understand how Python is used for artificial intelligence and deep learning
    • Gain knowledge on Tensorflow 2.0 and Keras to apply in deep learning projects
    • Develop a solid understanding of deep learning
    • Understand and apply core concepts of machine learning in deep learning projects
    IABAC Exam Information

    Materials Permitted

    • The examination is an ‘open book’
    • Candidates can refer to any material

    Exam duration & format

    • Exam format is the project submission
    • The assessment duration is 8 hours
    • The project is graded for Deep Learning model conceptual coding standards and performance

    Exam mode

    • Project needs to be submitted at IABAC Project Submit page, as per exam guidelines
    • Any copied work, ideas, concepts or a piece of text need to be marked with reference as per IABAC project plagiarism guidelines

    Pass criteria

    • The candidate needs to score assessment grade A+, A, B+, B, C+, C as a PASS Criteria
    • The candidate will be awarded grade F in case of failing to meet the pass criteria
    • The results will be declared after validation of the project as per guidelines

    Results timeline

    • The preliminary results are usually released within 9 days of the exam date
    • The official results are usually released within 15 days from the exam date

    Certificate Issuance

    • IABAC® e-certificate will be issued through the candidate’s registered email
    • The e-certificate is digital verifiable at https://www.iabac.org/verify-certificate
    • The candidate has license to share digital certificate validation in professional networking portals such as www.linkedin.com
    • The candidate has a license to print physical copy (hardcopy) of the certificate
    Course Modules Covered in the Certified Deep Learning Expert 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, Derivativesof 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 - Mathematics For Deep Learning

    Mathematics For Deep Learning

    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 - ADV DEEP LEARNING - CNN, RNN, LSTM RNN

    ADV 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
    9. RNN, LTSM RNN and GRU RNN
    10. Vanishing Gradient and exploding Gradient problem, LTSM architecture, GRU Architecture.
    11. LSTM RNN implementation in TensorFlow
    12. LSTM RNN project
    Module 7 - Big Data Foundation

    Big Data Foundation

    1. Big Data Platforms
    2. Importing Big Data
    3. PySpark functions for importing data from various sources and other big data frameworks
    4. Machine Learning with PySpark
    5. Implementing scalable ML models with PySpark
    Module 8 - Deep Learning Projects

    Deep Learning Projects

    1. Image Classification
    2. Image Classification with CIFAR-10 Dataset
    3. Human Face Detection
    4. Traffic Sign Detection
    5. Human Activity Detection
    6. 20BN-something-something Dataset V2
    7. Image Caption Generation
    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.
    Program Key Highlights
    online-learning-2
    40 hours of Remote Online Learning
    learning-hours
    Additional Coaching Hours
    hands-on
    Live Hands-on Projects
    certification
    Certified by International Body
    mentor
    Mentorship with Industry Experts
    industry
    Designed for Beginners & Professionals
    Request For Information

      Program Benefits

      International Credential

      IABAC® is a widely recognized credentialing framework based on European commission funded EDISON Data Science body of knowledge. This credential provides distinction as high potential certified Data Science Professionals enabling better career prospects.

      Global Opportunities

      IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

      Specialization

      IABAC Certification designed to cater to the job requirements of all experience levels and specializations, which suits roles aligned with the industry standards.

      Relevant and updated

      IABAC® CPD (Continuing Professional Development) program enables credential holders to update their skills and stay relevant to the industry requirements.

      Higher Salaries

      On an average, a certified professional earns 30-40% more than their non-certified as per recent study by Forbes.

      Summits & Webinars

      In addition, IABAC members will have exclusive access to seminars and Data Science summits organised by IABAC partners across the globe.
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      Get Professionally Certified
      Upon successfully completing this program, participants will be awarded the Certified Deep Learning 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 Machine Learning. 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 .
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      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.
       
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      Thulija Academy is a HRDF registered training provider. Our panel of expert trainers provide technology training for some of the biggest organizations in Asia.

       
       
       



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