Course Overview

Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world? Machine Learning with Python dives into the basics of machine learning. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each using Python libraries. Learn Machine Learning techniques to build predictive models.

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Course Key Features

High Lights

Why Learn Machine Learning with Python for Supply Chains & 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

Statistical Learning (3 Hours)

Topics:

  • Statistical analysis concepts
  • Descriptive statistics
  • Introduction to probability and Bayes theorem
  • Probability distributions
  • Hypothesis testing & scores

Learning Objectives:

  • Visit basics like mean (expected value), median and mode
  • Distribution of data in terms of variance, standard deviation and interquartile range
  • Basic summaries about the data and the measures. Together with simple graphics analysis
  • Basics of probability with daily life examples
  • Marginal probability and its importance with respective to datascience
  • Learn baye's theorem and conditional probability
  • Learn alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value

Skill
Statistics, Probability

Subskill
Basics/Intermediate

Core competencies

  • Mean, Medain, Mode
  • Measure of Central Tendency and Dispersion
  • Probability Interpretations, Conditional Probability
  • Various Probability Functions and their Constructions
  • Formulating and Testing Hypothesis

Delivery Type:
Theory + Workshop

Hands-on workshop
Learn to implement statistical operation in Excel

Home Assignment
Yes

Python for Machine Learning (5 Hours)

Topics:

  • Python Overview
  • Pandas for Pre-Processing and Exploratory Data Analysis
  • Numpy for Statistical Analysis
  • Matplotlib & Seaborn for Data Visualization
  • Scikit Learn

Learning Objectives:

  • Get a taste of how to start work with data in Python. Learn how to define variables, sets and conditional statements, the purpose of having functions and how to operate on files to read and write data in Python.
  • Learn how to use pandas, a must have package for anyone attempting data analysis in Python. Learn to visualization data using Python libraries like matplotlib, seaborn and ggplot

Skills
Python

Subskills

Basics/Intermediate

Core Competencies

  • Variables, Data Types, List, Tuple, Set, Dictionary
  • Dataframe Manipulation, EDA
  • Numerical Library
  • Visualization
  • ML Library

Delivery Type:

Theory + Workshop

Hands-on workshop

Write python code to write functions, to operate conditional statements, to read and write data into notebook, and learn how to use various important libraries like pandas which is used for data analysis. visualization libraries like matplot seaborn and ggplot

Home Assignment
Yes

Introduction to Machine Learning (2 Hours)

Topics:

  • Machine Learning Modelling Flow
  • How to treat Data in ML
  • Types of Machine Learning
  • Performance Measures
  • Bias-Variance Trade-Off
  • Overfitting & Underfitting

Learning Objectives:

Look at real-life examples of Machine Learning and how it affects society in ways you may not have guessed! Explore many algorithms and models like Classification, Regression, Clustering. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning

Skills
ML

Subskills
Basics

Core Competencies

  • ML Basics
  • Machine Learning Phases
  • Supervised and Unsupervised Learning Algorithms
  • MSE, MAE, RMSE, Confusion Matrix, Accuracy, Precision, Recall, AUC ROC,
  • Bias Error, Variance Error, Bias-Variance Balance, Data Inconsistencies in ML

Delivery Type:
Theory

Hands-on workshop
No hands-on

Home Assignment
Yes

Optimization (1 Hour)

Topics:

  • Maxima and Minima
  • Cost Function
  • Learning Rate
  • Optimization Techniques

Learning Objectives:

Understand various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp

Skills
ML

Subskills

Intermediate

Core Competencies

  • Maths for Optimization
  • Optimization Strategies

Delivery Type:
Theory

Hands-on workshop
No hands-on

Home Assignment
Yes

Supervised Learning (25 Hours)

Topics:

  • Linear Regression
    • Learning Objective: Learn Linear Regression with Stochastic Gradient Descent with real-life case study. Covers hyper-parameters tuning like learning rate, epochs, momentum
    • Case Study: Real Life Case Study on Linear Regression
    • Skills: ML, Python
    • Sub Skills: Linear Regression with Stochastic Gradient Descent, sklearn library, Case Study
    • Core Competencies: Cost Function in Linear Regression, Stochastic Gradient Descent, Optimization Process
    • Delivery Type: Theory+Workshop
    • Project:
      TITLE - Predict House Price 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 descent
    • Home Assignment: Yes
  • Logistic Regression
    • Learning Objective: Learn Logistic Regression with Stochastic Gradient Descent with real-life case study. Covers hyper-parameters tuning like learning rate, epochs, momentum and class-balance
    • Case Study: Real Life Case Study on Logistic Regression
    • Skills: ML, Python
    • Sub Skills: Logistic Regression with Stochastic Gradient Descent, sklearn library, Case Study
    • Core Competencies: Activation(Cost) Function in Logistic Regression, Stochastic Gradient Descent, Regularization, Hyperparameters. Grid Search
    • Delivery Type: Theory+Workshop
    • Project:
      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
    • Home Assignment: No
  • Decision Trees
    • Learning Objective: Decision Trees - for regression & classification problem. Covers both Classification & regression problem. Candidates get knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, CHAID
    • Case Study: Real Life Case Study on Decision Tree
    • Skills: ML, Python
    • Sub Skills: Building Decision for Regression and Classification problems with sklearn library, Case Study
    • Core Competencies: ID3, CHART, CHAID, Entropy, Information gain, gini index
    • Delivery Type: Theory+Workshop
    • Project:
      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)
    • Home Assignment: Yes
  • K-NN Classification
    • Learning Objective: Learn how KNN can be used for a classification problem
    • Case Study: Real Life Case Study on KNN Classification
    • Skills: ML, Python
    • Sub Skills: Maths behind K-Nearest Neighbors Algorithm, sklearn library, Case Study
    • Core Competencies: Method-based Learning, Instance-based Learning, Lazy Learning, Types of KNN, Common Distance Metrics
    • Delivery Type: Theory+Workshop
    • Project:
      Predict if a patient is likely to get any chronic kidney disease depending on the health metrics
  • Naive Bayesian classifiers
    • Case Study: Real Life Case Study on Naive Bayesian Classifiers
    • Skills: ML, Python
    • Delivery Type: Theory+Workshop
    • Project:
      We receive 100s of emails & text messages everyday. Many of them are spams. We would like to classify our spam messages and send them to the spam folder. We would also not like to incorrectly classify our good messages as spam. So correctly classifying a message into spam and ham is of utmost importance. We will use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham.
    • Home Assignment: No
  • SVM - Support Vector Machines
    • Learning Objective: Learn how Support Vector Machines can be used for a classification problem with real-life case study. Covers hyper-parameter tuning like regularization
    • Case Study: Real Life Case Study on SVM
    • Skills: ML, Python
    • Sub Skills: Support Vectors, sklearn library, Case Study
    • Core Competencies: Support Vectors, Decision Boundries, Margin, Kernel Types, Hyperplanes, Hyperparameter tuning, GridSerach
    • Delivery Type: Theory+Workshop
    • Project:
      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.
    • Home Assignment: No
Unsupervised Learning (4 Hours)

Topics:

  • Clustering approaches
  • K Means clustering
  • Hierarchical clustering
  • Case Study

Learning Objectives:

  • Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering
  • Real Life Case Study on K-means Clustering

Skills
ML, Python

Subskills

  • Types of Clustering
  • Clustering Technique
  • K-means Clustering Case Study

Core Competencies

  • Hierarchical, Agglomorative
  • Maths behind KMeans Algorithm, sklearn library,
  • Agglomerative Clustering, Proximity Matrix, Dendrogram, Divisive Clustering Algorithm

Delivery Type:
Theory + Workshop

Hands-on workshop

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."

Home Assignment
Yes

Ensemble Techniques (4 Hours)

Topics:

  • Introduction to Ensemble Learning
  • Different Ensemble Learning Techniques
  • Bagging
  • Boosting
  • Random Forests
  • Case Study

Learning Objectives:

  • Cover basic ensemble techniques like averaging, weighted averaging & max-voting
  • Learn about bootstrap sampling and its advantages followed by bagging.
  • Boost model performance with Boosting
  • Learn Random Forest with real-life case study and how it helps avoid overfitting comapred to decision trees
  • Real Life Case Study on Random Forests

Skills: ML, Python

Sub Skills: 
Basics
Bagging Algorithm
Ensemble Technique

Core Competencies:

  • Averaging, Max-voting, Ensembling, Weighted Averages
  • Bootstrap Method, Bagging Algorithm, Variable Importance
  • Types of Boosting Algorithm
  • Random Forest

Delivery Type:
Theory+Workshop

Hands-on workshop
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. In this case study, use AdaBoost, GBM & Random Forest on Lending Data to predict loan status. Ensemble the output and see your result perform than a single model

Home Assignment
No

Recommendation Systems (4 Hours)

Topics:

  • Introduction to Recommendation Systems
  • Types of Recommendation Techniques
  • Collaborative Filtering
  • Content based Filtering
  • Hybrid RS
  • Performance measurement

Learning Objectives:

  • Hands-on implementation of Association Rules. Use Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Learn what is UBCF and how is it used in Recommender Engines. Covers concepts like cold-start problems. Learn what is IBCF and how is it used in Recommender Engines

Skills: ML, Python

Sub Skills: 

  • Association Rule Concepts
  • Types of recommendation engines
  • Recommender System Evolution
  • Recommender system performance evaluation

Core Competencies:

  • Support, Confidence, Lift, Conviction,
  • Types of Recommender Engines, Memory-based Collaborative Methods,
  • Model-based Collaborative Methods, Hybrid Recommender Systems
  • Memory-based Collaborative Methods, Model-based Collaborative Methods
  • Memory-based Collaborative Methods, Model-based Collaborative Methods
  • Hybrid Recommender Systems
  • Performance Metrics

Delivery Type:
Theory

Hands-on workshop
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. In this case study, use AdaBoost, GBM & Random Forest on Lending Data to predict loan status. Ensemble the output and see your result perform than a single model

Home Assignment
Yes

Case Study

Learning Objectives:

Real Life Case Study on building a Recommendation Engine

Skills: ML, Python

Sub Skills: Recommender system

Delivery Type: Workshop

Hands-on workshop: 

You do not need a market research team to know what your customers is willing to buy. And Netflix is a big example. Netflix successfully used recommeder system to recommend movies to its viewers. And Netflix estimated, that its recommendation engine is worth a yearly $1billion.

An increasing number of online companies are using recommendation systems to increase user interaction and benefit from the same. Build Recommender System for a Retail Chain to recommend the right products to its users

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

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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

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Kim Wong

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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.

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Timothy Ng

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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.

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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.

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Alice_Wee

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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.

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Wilson Chong

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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.

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Manager, Wah Kee Marine Supplies Pte. Ltd.
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