Course Overview

With this couse you get to learn the basic concepts of Deep Leaning and various libraires like TensorFlow & Keras. This hands-on intensive curriculum takes you to the world of computer vision & natural language processing using various deep architectures like CNN, RNN & LSTM

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Course Key Features

High Lights

Why Learn Deep Learning using Python for Supply Chains and Logistics?

You’ll learn the necessary technical knowledge of machine learning and the business applications of artificial intelligence (AI) in Logistics and Supply Chain. Through studying history and latest trends, you’ll learn the fundamentals of AI and how AI brings about change. You will learn how to formulate an implementation plan for AI and understand the intricacies when planning for AI transformation in your organisation.

Why most prefer SCALA for their training neeeds?

 

Upcoming Intakes

October 2020
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Syllabus

Python for Deep Learning (1 Hours)

Topics:

  • Intro to TensorFlow:
    • Skill: Python
    • Subskill: Intermediate
    • Core competencies: Using TensorFlow
    • Delivery Type: Theory + Workshop
  • Intro to Keras:
    • Skill: Python
    • Subskill: Intermediate
    • Core competencies: Using Keras
    • Delivery Type: Theory + Workshop
  • Intro to OpenCV
    • Skill: Python
    • Subskill: Intermediate
    • Core competencies: Using OpenCV
    • Delivery Type: Theory + Workshop
  • Intro to Gensim
    • Skill: Python
    • Subskill: Intermediate
    • Core competencies: Using Gensim
    • Delivery Type: Theory + Workshop
  • Intro to YOLO
    • Skill: Python
    • Subskill: Intermediate
    • Core competencies: Using YOLO
  • Intro to NLTK
    • Skill: Python
    • Subskill: Intermediate
    • Core competencies: Using NLTK
    • Delivery Type: Theory + Workshop

Learning Objectives:

This module introduces to various libraries that are used for Deep Learning algorithms.

After this module, you will be able to:
1. Know various libraries & their purposes

Hands-on workshop
Learn to implement various libraries required for deep learning

Home Assignment
No

Introduction to Neural Networks (4 Hours)

Topics:

  • Introduction to Neural Network:
    • Skill: ML, Python
    • Subskill: Intermediate
    • Core competencies: Understanding NN
    • Delivery Type: Theory + Workshop
  • Loss Functions:
    • Skill: ML, Python
    • Subskill: Intermediate
    • Core competencies: Understanding Loss Functions
    • Delivery Type: Theory + Workshop
  • Regularization:
    • Skill: ML, Python
    • Subskill: Intermediate
    • Core competencies: Applying Regularization to NN
    • Delivery Type: Theory + Workshop
  • Building Blocks of Neural Network:
    • Skill: ML, Python
    • Subskill: Intermediate
    • Core competencies: Understanding NN components
    • Delivery Type: Theory + Workshop
  • Neural Network from scratch:
    • Skill: ML, Python
    • Subskill: Intermediate
    • Core competencies: Building NN from scratch without using any Python libraries
    • Delivery Type: Workshop

Learning Objectives:

This module introduces you to Neural Network fundamentals.

After this module, you will be able to:

  1. Understand how Neural Networks work
  2. What are the building blocks of Neural Network
  3. Create Neural Network from scratch

Hands-on workshop
Use Python to build a NN from scratch without using any pibraries

Home Assignment
No

Convolutional Neural Networks (4 Hours)

Topics:

  • Convolution network vs plain neural network:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Difference between NN & CNN
    • Delivery Type: Theory + Workshop
  • Locally Connected Layer:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding CNN components
    • Delivery Type: Theory + Workshop
  • Transitional Invariance:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding CNN components
    • Delivery Type: Theory + Workshop
  • Convolutions (Discrete 1D):
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding CNN components
    • Delivery Type: Theory + Workshop
  • Spatial dimension - convolutions:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding CNN components
    • Delivery Type: Theory + Workshop
  • Convolution- backward:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding CNN components
    • Delivery Type: Theory + Workshop

Learning Objectives:

This module introduces you to Convolutional Neural Networks

After this module, you will be able to:

  1. Understand the building blocks of Convolutional Neural Network
  2. How CNN works
  3. Learn CNN from scratch

Hands-on workshop
Build CNN models

Home Assignment
No

Computer Vision (8 Hours)

Topics:

  • Working with Images:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding images
    • Delivery Type: Theory + Workshop
  • Convolutional Neural Network Building Blocks:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: CNN Components
    • Delivery Type: Theory + Workshop
  • CNN Architectures:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: CNN Architectures
    • Delivery Type: Theory + Workshop
  • Transfer Learning:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding TL
    • Delivery Type: Theory + Workshop
  • Semantec Segmentation:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding working with images
    • Delivery Type: Theory + Workshop
  • Object Detection:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding working with images
    • Delivery Type: Theory + Workshop
  • Bounding Box
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding working with images
    • Delivery Type: Theory + Workshop
  • CNN Applications
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding working with images
    • Delivery Type: Theory + Workshop

Learning Objectives:

This module introduces you to Computer Vision.

After this module, you will be able to:

  1. Concepts behind Image Processing
  2. Techniques to process images
  3. Apply various libraries for object recognition & identification

Hands-on workshop
Build Computer Vision models for image classifications & identifications

Home Assignment
No

Recurrent Neural Networks (4 Hours)

Topics:

  • What are RNNs?:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding RNN
    • Delivery Type: Theory + Workshop
  • Forward pass in RNN:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding RNN
    • Delivery Type: Theory + Workshop
  • Backward pass in RNN:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding RNN
    • Delivery Type: Theory + Workshop
  • Vanishing and exploding gradient problem:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding RNN
    • Delivery Type: Theory + Workshop
  • Different types of RNN architecture:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding RNN
    • Delivery Type: Theory + Workshop
  • Bi-directuonal RNNs:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding RNN
    • Delivery Type: Theory + Workshop
  • Advantages and disadvantages of RNN
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding RNN
    • Delivery Type: Theory + Workshop
  • Application area of RNN
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding RNN applications
    • Delivery Type: Theory + Workshop

Learning Objectives:

This module introduces you to Recurrent Neural Networks

After this module, you will be able to:

  1. Understand the building blocks of Recurrent Neural Network
  2. How RNN works
  3. Learn RNN from scratch

Hands-on workshop
Build RNN models

Home Assignment
No

Natural Language Processing (8 Hours)

Topics:

  • Intro to Statistical NLP Techniques:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding NLP for text analytics
    • Delivery Type: Theory + Workshop
  • Natual Language Understanding:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding NLP for text analytics
    • Delivery Type: Theory + Workshop
  • Natural Language Generation:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding NLP for text analytics
    • Delivery Type: Theory + Workshop
  • Working with Texts - Tokenization, Stemming, Lematization:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding NLP for text analytics
    • Delivery Type: Theory + Workshop
  • Word Embeddings - Word2Vec:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding NLP for text analytics
    • Delivery Type: Theory + Workshop
  • Word Embeddings - POS Tagging:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding NLP for text analytics
    • Delivery Type: Theory + Workshop
  • Word Embeddings - Names Entity Recognition:
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding NLP for text analytics
    • Delivery Type: Theory + Workshop
  • Word Embeddings - TF-IDF
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding NLP for text analytics
    • Delivery Type: Theory + Workshop
  • Introduction to Sequential Models - RNN
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding NLP for text analytics
    • Delivery Type: Theory + Workshop
  • Introduction to Sequential Models - LSTM 
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding NLP for text analytics
    • Delivery Type: Theory + Workshop
  • NLP Applications
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Understanding NLP for text analytics
    • Delivery Type: Theory + Workshop

Learning Objectives:

This module introduces you to Natural Language Processing.

After this module, you will be able to:

  1. Understand the concepts behind Text analytics
  2. Learn Text proessing techniques
  3. Know algorithms like RNN & LSTM
  4. Apply RNN, LSTM in real life problems

Hands-on workshop

Use NLP for Text analytics, classifications

Home Assignment
No

Capstone Project (3 Hours)

Capstone Project

    • Learning Objectives: Combine all the knowledge to work on a real life capstone project
    • Skill: DL, Python
    • Subskill: Advanced
    • Core competencies: Executing an end-to-end project on Computer Vision OR Natural Language Processing
    • Delivery Type: Theory + Workshop
    • Home Assignment: No
  • Elementary programming knowledge
  • Familiarity with statistics
  1. Statistical Learning - Understand the behavior of data as you build significant models
  2. Python for Machine Learning - Learn about the various libraries offered by Python to manipulate, preprocess and visualize data
  3. Fundamentals of Machine Learning - Learn about Supervised and Unsupervised Machine Learning and look into how statistical modelling relates to machine learning
  4. Optimization Techniques - Learn to use optimization techniques to find the minimum error in your machine learning model
  5. Machine Learning Algorithms - Learn various machine learning algorithms like KNN, Decision Trees, SVM, Clustering in detail and build model using them to implement in real life scenarios using Python libraries such as Scikit learn
  6. Dimensionality Reduction - Learn the technique to reduce the number of variables using Feature Selection and Feature Extraction
  7. Neural Networks - Understand Neural Network and apply them to classify image and perform sentiment analysis using CNN and RNN
  8. Ensemble Learning - Learn to use multiple learning algorithms to obtain better predictive performance

This course is for you if:

  • You are interested in the field of machine learning and want to learn essential machine learning algorithms and implement them in real life business problem
  • You're a Software or Data Engineer interested in learning the fundamentals of quantitative analysis and machine learning

Covers projects using Linear Regression, Logistic Regression, Decision Tree, Time Series Forecasting, K-Nearest Neighbor, Support Vector Machine, Neural Networks, CNN, RNN, Adaboost, GBM, Random Forest etc.

PROJECT 1

TITLE - Predict Property Pricing using Linear Regression
DESCRIPTION - With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descentT

PROJECT 2

TITLE - Classify good and bad customer for bank to decide on granting loans
DESCRIPTION - This dataset classifies people described by a set of attributes as good or bad credit risks. Using logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers

PROJECT 3

TITLE - Classify chemicals into 2 classes, biodegradable and non-biodegradable using SVM
DESCRIPTION - Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set containing 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes - biodegradable and non-biodegradable. Build Models to study the relationships between chemical structure and biodegradation of molecules and correctly classify if a chemical is biodegradable and non-biodegradable.

PROJECT 4

TITLE - Cluster teen student into groups for targeted marketing campaigns usng Kmeans Clustering
DESCRIPTION - In marketing, if you’re trying to talk to everybody, you’re not reaching anybody..This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns.

PROJECT 5

TITLE - Predict quality of Wine
DESCRIPTION - Wine comes in various style. With the ingredient composition known, we can build a model to predict the the Wine Quality using Decision Tree (Regression Trees)

SCALA Trainers/Mentors

Benny Gan

Benny Gan

Jess Lee Yan Keow (Jezz)

JESS LEE YAN KEOW (Jezz)

Lionel Seah

LIONEL SEAH

Patrick Tan

PATRICK TAN

Call Us @ (65)64172475

OR

Book An Appointment

Tools and Frameworks used

Python, MS Excel

Tools and Frameworks used
Tools and Frameworks used

 

Companies We Work With

Join over 200 companies that have placed and trained new hires in SCALA's PCP and received course fee grant and salary support for eligible new hires.

Subscribe to us today to enjoy the following

About SCALA

Industry Curated Curriculum

Our curriculum is created with the combined effort of our Board of Advisors and industry veterans. Focusing on providing both immediate and future knowledge, the curriculum equips SCALA’s trainees with in-depth logistical and supply chain knowledge as well as keeping them up to date on the latest development in the technology space. Our curriculum is also accredited by Workforce Singapore and is recognized country-wide for its efficacy.

Strong Leadership

Founded by Dr Robert Yap in 2016, SCALA is an industry-level academy that helps organizations unleash the hidden value in their supply chain. Lead by SCALA’s Board of Advisors, which comprise of distinguish leaders from industry and academia, SCALA’s vision is to become the standard for practical, hands-on training of logistics and supply chain industry in Singapore and the region.

Mentorship/Coaching for Professional Growth

Mentoring is a key element of any successful talent development strategy to help enterprises build high-performing team. Tailored individually to every SCALA's Professional Conversion Programme (PCP) participants, mentors facilitate our participants, who are mid career switchers, towards a deeper understanding of their strengths, talents, personalities and values. As our PCP participants become more purposeful in charting their personal growths, they will be better positioned for success in their new roles.

Network of over 500 Corporate Members

SCALA’s Corporate Network organizes networking opportunities and events that help companies embrace innovation to uncover hidden value. By bringing together diverse experiences and businesses, the Corporate Network aims to raise the level within and outside the industry, to create a profession out of supply chain and logistics.

FAQs

What practical skill sets can I expect to have upon completion of the course?
  • "-Get advanced knowledge on machine learning techniques using Python
  • Be proficient with frameworks like TensorFlow and Keras
What can I expect to accomplish by the end of this course?

By the end of this course, you would have gained knowledge on the use of machine learning techniques using Python and build applications models. This will help you land jobs as Data Scientist.

Does this class have any restrictions?

There are no restrictions but participants would benefit if they have elementary programming knowledge and familiarity with statistics.

What should my personal setup look like?

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor

Testimonials

Read what our customers are saying

Kim Wong

"

The salary support has greatly alleviated our manpower costs since we are a non-profit Social Enterprise startup trying to build a critical mass in the wholesale and retail sector, using e-commerce as a strategic tool. The lower cost was critical for our survival in the midst of a tough economy undergoing restructuring.

Kim Wong

Administration Manager, Actsmarket LimitedActs market logo

Timothy Ng

"

The Supply Chain PCP has been really helpful for us to quickly upskill new hires from a different sector, and equip them with fundamental skills and knowledge for them to excel in the logistics industry. Our new hires have really appreciated the training and guidance given during the programme. The salary support has also been very helpful in incentivising commitment to this programme and helping us to find alternative means to cover the impact on manpower, and in the long run, as employers and employees, are all better off for it.

Timothy Ng

Head, Engagement, Learning and Development, Ninja Van
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Gabriel_Lam

"

The mentors who were assigned to them had many years of experience and who selflessly share their knowledge. The projects enabled them to think from different perspectives and are beneficial to their employers.

Gabriel Lam

Chief Operating Office, Shalom Movers
Testimonial

Alice_Wee

"

The salary support has helped to accelerate our expansion plans. The invaluable training materials are very informative, and allow my staff to think about various possible scenarios in the workplace.

Alice Wee

Executive Director, Wine Clique Pte. Ltd.
Wine Clique

Wilson Chong

"

Our employees applied the concepts from the classroom training to transform and digitize our supply chain processes to great effect. As a company of over 26 years in oil and gas equipment trading, it is very timely that we get this opportunity to understand and implement industry best practices in today's digital age.

Wilson Chong

Manager, Wah Kee Marine Supplies Pte. Ltd.
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