Get In Touch
PT. Algoritma Data Indonesia.
RDTX Square, 9th Floor. Setiabudi, DKI Jakarta 12930.
Office: 0816-692-471
Email: community@algorit.ma
Courses under the Machine Learning specialization
Neural Network And Deep Learning
Coding Artificial Intelligence Systems
Ad-Hoc Course Registration:
ON-SITE CLASS
- Date: 8 – 11 February 2021
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
- Investment: Rp. 5.200.000
ONLINE CLASS
- Date: 8 – 11 February 2021
- Time: 18.30 – 21.30
- Investment: Rp. 2.600.000
Course details :
Develop artificial neural networks that can recognize a face, handwriting patterns and are at the core of some of the most cutting-edge cognitive models in the AI landscape. We will learn to create a backpropagation neural network from scratch, and use our neural network for classification tasks. This class is the final course in the Machine Learning Specialization.
We strongly recommend that you complete the pre-requisite workshops prior to taking this course. Some concepts presented throughout the lecture may be less-than-ideal for practitioners who have not completed the pre-requisite courses.
Schedule
Course Producer
Samuel Chan
An RStudio-certified instructor and machine learning practitioner in the field of marketing automation, fraud detection, finance and e-commerce. Samuel is Indonesia’s top-ranked Stack Overflow user in R (top 5% worldwide) for three years running, and boasts certifications from RStudio, Microsoft, MongoDB, Neo4J Database, Stanford University, John Hopkins University, among others.
Prior to Algoritma, he has 8 years of working experience, including a stint as in-house consultant to several public-trading companies from his time staying in China, Japan and Singapore. He is today an active trainer and consultant for various companies in the financial industry. He has guest lectured in various campuses: Binus, NUS (National University of Singapore)’s The Logistics Institute, University of Indonesia, Universitas Gadjah Mada (UGM), Binus, Institute of Technology Bandung (ITB), Telkom University etc. Courses he authored are offered also in Singapore through Ngee Ann Polytechnic.
Samuel is also among the first recipients of Microsoft Professional Program Certificate in Data Science in Southeast Asia, having demonstrated proficiency in R, Python, Microsoft Azure, SQL / T-SQL, PowerBI and a list of other technologies, and among the first to be certified in RStudio’s program. Technical committee member and competition judge on Finhacks 2018, the largest Machine Learning competition of the year organized by PT. Bank Central Asia (BCA) and DailySocial.
4-Day Workshop Modules
Syllabus: Neural Network & Deep Learning
Module 1: Neural Network
Artificial Neural Networks
- The Biological Brain Inspiration
- Cost Function
- The Building Blocks of Neural Networks
Neural Network Architecture
- Layers, Nodes, and Signals
- Network topology
- Feed-forward vs Recurrent Signal
Neural Network Architecture II
- Hidden Layers
- Computing with Neural Network
- Mathematical Details
Multi-Layer Perceptrons (MLP)
- Backpropagation of Error
- Feed-forward vs Recurrent
- Mathematical Details
Module 2: Deep Learning
Neural Networks from First Principles
- Sum of Squared Errors
- Cross-Entropy Error
- The Gradient Descent Algorithm
Neural Networks from Scratch
- Gradient Descent by Hand
- Neural Network by Hand
- Learning Rate and Implementation Details
Neural Networks in Action
- Putting It All Together
- Parameterization and Practical Advice
- Deep Learning for Classification and Regression
Deep Learning in Action
- Theorizing With Effect of Depth
- Activation Functions
- Visualizing Logarithmic Loss
Deep Learning in Action II
- Predicting Bank Telemarketing Campaign
- Visualizing Tricks for Deep Neural Networks
- Parameterization and Practical Advice
MXNet in Action
- Thinking About Parallelism
- MNIST Handwritten Digit Recognition
- Predictions With MXNet and Practical Advice
Academy Modules
Graded Quiz
Learning-by-Building Module (3 Points)
Image Classification Using Neural Network
- Build a neural network capable of classifying images into one of many classes and explain the choice of your architecture. Test your neural network using unseen images – can your algorithm correctly classify 80% of the images?
Ad-Hoc Course Registration:
ON-SITE CLASS
- Date: 8 – 11 February 2021
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
- Investment: Rp. 5.200.000
ONLINE CLASS
- Date: 8 – 11 February 2021
- Time: 18.30 – 21.30
- Investment: Rp. 2.600.000
Workshop Receivables:
This workshop is recommended for:
The Neural Network and Deep Learning workshop is an advanced-level programming workshop best suited to R programmers that have completed the pre-requisite courses offered through the machine learning specialization.
Students are assumed to have a working knowledge of R and have completed the necessary pre-requisites. Consider taking the pre-requisite course or a beginner-level course instead if you have no prior programming experience or statistics knowledge.
Past Workshops in this Series:
Students work through tons of real-life examples using sample datasets donated by our team of mentors and corporate partners. We believe in a learn-by-building approach, and we employ instructors who are uncompromisingly passionate about your growth and education.
Part of the Machine Learning Specialization
This workshop is part of the Machine Learning Specialization offered by Algoritma Data Science Academy. Participants are rewarded with a certificate of completion upon passing criteria, and are encouraged to advance further in the respective data science specialization.
Time Series And Forecasting
Decomposition and Forecasting Methods
Ad-Hoc Course Registration:
ON-SITE CLASS
- Date: 1 – 4 February 2021
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
- Investment: Rp. 5.200.000
ONLINE CLASS
- Date: 1 – 4 February 2021
- Time: 18.30 – 21.30
- Investment: Rp. 2.600.000
Course details :
Decomposition of time series allows us to learn about the underlying seasonality, trend and random fluctuations in a systematic fashion. In this workshop, we learn the methods to account for seasonality and trend, work with autocorrelation models and create industry-scale forecasts using modern tools and frameworks.
We strongly recommend that you complete the pre-requisite workshops prior to taking this course. Some concepts presented throughout the lecture may be less-than-ideal for practitioners who have not completed the pre-requisite courses.
Schedule
Course Producer
Samuel Chan
An RStudio-certified instructor and machine learning practitioner in the field of marketing automation, fraud detection, finance and e-commerce. Samuel is Indonesia’s top-ranked Stack Overflow user in R (top 5% worldwide) for three years running, and boasts certifications from RStudio, Microsoft, MongoDB, Neo4J Database, Stanford University, John Hopkins University, among others.
Prior to Algoritma, he has 8 years of working experience, including a stint as in-house consultant to several public-trading companies from his time staying in China, Japan and Singapore. He is today an active trainer and consultant for various companies in the financial industry. He has guest lectured in various campuses: Binus, NUS (National University of Singapore)’s The Logistics Institute, University of Indonesia, Universitas Gadjah Mada (UGM), Binus, Institute of Technology Bandung (ITB), Telkom University etc. Courses he authored are offered also in Singapore through Ngee Ann Polytechnic.
Samuel is also among the first recipients of Microsoft Professional Program Certificate in Data Science in Southeast Asia, having demonstrated proficiency in R, Python, Microsoft Azure, SQL / T-SQL, PowerBI and a list of other technologies, and among the first to be certified in RStudio’s program. Technical committee member and competition judge on Finhacks 2018, the largest Machine Learning competition of the year organized by PT. Bank Central Asia (BCA) and DailySocial.
4-Day Workshop Modules
Syllabus: Time Series & Forecasting
Module 1: Time Series I
Working with Time Series
- Application of Time Series
- Definition of a ts Object
- Functions to Work with Time Series
Time Series in Action
- Indonesia’s Gas Emissions, 1970-2012
- Frequency, Start and End
- Time Series Plots
Classical Decomposition
- Trend, Seasonality and Residuals
- Understanding Lags
- Additive vs Multiplicative
Classical Decomposition in Action
- Monthly Airline Passenger, 1949-1960
- The decompose Function
- Understanding Smoothing
Techniques to Work with Time Series
- Adjusting for Seasonality
- Detrending
- Decomposing Non-Seasonal Time Series
Module 2: Forecasting
Forecasting I
- Simple Moving Average
- Simple Moving Average from First Principles
- Log-Transformation
Forecasting II
- Forecasting Using One-sided SMA
- Forecasting Using Exponential Smoothing
- Holt’s Exponential Smoothing
Forecasting III
- The beta and gamma Coefficients
- Mathematical Details
- Holt-Winters Exponential Smoothing
Advanced Time Series
- ACF and PACF
- ARMA and ARIMA Models
- Stationarity and Differencing
Advanced Time Series II
- Augmented Dickey-Fuller (ADF) Test
- Seasonal ARIMA
- Tips to Work With xts
- Facebook’s Prophet
- Quantmod for Quantitative Traders
Academy Modules
Graded Quiz
Learning-by-Building Module (3 Points)
Forecasting the Crime rate in Chicago
- Download the dataset from Chicago Crime Portal, and use a sample of these data to build a forecasting project where you inspect the seasonality and trend of crime in Chicago. Submit your project in the form of an RMD format, and address the following questions:
- Is crime generally rising in Chicago in the past decade (last 10 years)?
- Is there a seasonal component to the crime rate?
- Which time series method seems to capture the variation in your time series better? Explain your choice of algorithm and its key assumptions
The student should be awarded the full (3) points if they address at least 2 of the above questions.
Ad-Hoc Course Registration:
ON-SITE CLASS
- Date: 1 – 4 February 2021
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
- Investment: Rp. 5.200.000
ONLINE CLASS
- Date: 1 – 4 February 2021
- Time: 18.30 – 21.30
- Investment: Rp. 2.600.000
Workshop Receivables:
This workshop is recommended for:
The Time Series and Forecasting workshop is an intermediate-level programming workshop best suited to R programmers that are taking their first steps into data science and machine learning.
Students are assumed to have a working knowledge of R and have completed the necessary pre-requisites. Consider taking the pre-requisite course or a beginner-level course instead if you have no prior programming experience or statistics knowledge.
Past Workshops in this Series:
Students work through tons of real-life examples using sample datasets donated by our team of mentors and corporate partners. We believe in a learn-by-building approach, and we employ instructors who are uncompromisingly passionate about your growth and education.
Part of the Machine Learning Specialization
This workshop is part of the Machine Learning Specialization offered by Algoritma Data Science Academy. Participants are rewarded with a certificate of completion upon passing criteria, and are encouraged to advance further in the respective data science specialization.
Unsupervised Machine Learning
Discovering Hidden Structures in Data
Ad-Hoc Course Registration:
ON-SITE CLASS
- Date: 25 – 28 January 2021
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
- Investment: Rp. 5.200.000
ONLINE CLASS
- Date: 25 – 28 January 2021
- Time: 18.30 – 21.30
- Investment: Rp. 2.600.000
Course details :
Learn PCA (Principal Component Analysis), Clustering, and other algorithms to work with unsupervised machine learning tasks where the target variable is not known or defined. Applying what you’ll learn from this workshop, you will be tasked to develop an anomaly detection or an e-commerce product recommendation model that can be related to real-life business scenarios.
We strongly recommend that you complete the pre-requisite courses prior to taking this course. Some concepts presented throughout the lecture may be less-than-ideal for practitioners who are new to the field of machine learning.
Schedule
Course Producer
Samuel Chan
An RStudio-certified instructor and machine learning practitioner in the field of marketing automation, fraud detection, finance and e-commerce. Samuel is Indonesia’s top-ranked Stack Overflow user in R (top 5% worldwide) for three years running, and boasts certifications from RStudio, Microsoft, MongoDB, Neo4J Database, Stanford University, John Hopkins University, among others.
Prior to Algoritma, he has 8 years of working experience, including a stint as in-house consultant to several public-trading companies from his time staying in China, Japan and Singapore. He is today an active trainer and consultant for various companies in the financial industry. He has guest lectured in various campuses: Binus, NUS (National University of Singapore)’s The Logistics Institute, University of Indonesia, Universitas Gadjah Mada (UGM), Binus, Institute of Technology Bandung (ITB), Telkom University etc. Courses he authored are offered also in Singapore through Ngee Ann Polytechnic.
Samuel is also among the first recipients of Microsoft Professional Program Certificate in Data Science in Southeast Asia, having demonstrated proficiency in R, Python, Microsoft Azure, SQL / T-SQL, PowerBI and a list of other technologies, and among the first to be certified in RStudio’s program. Technical committee member and competition judge on Finhacks 2018, the largest Machine Learning competition of the year organized by PT. Bank Central Asia (BCA) and DailySocial.
4-Day Workshop Modules
Syllabus: Unsupervised Machine Learning
Module 1: Dimensionality Reduction
Background
- Understanding Unsupervised Learning
- The “Dimensionality” Problem
- Industrial Use of PCA
Principal Component Analysis
- Rethinking About Covariances
- The Case for PCA
- Eigenvalues and Eigenvectors
PCA from First Principles
- Just Enough Matrix Algebra
- Mathematical Proof
- Visualization and Visual Proof
PCA in Action
- Dubious Property Sales in NYC
- PCA on US Arrests Data
- Biplot and The Variables Factor Map
PCA in Action II
- Eigenfaces
- PCA on Credit Loan Data
- Deconstruction and Reconstructing Faces with PCA
- Principal Components by Hand
Module 2: k-Means Clustering
Understanding Clustering
- Centroid-based Clustering Algorithms
- The k-Means Procedure
- Mathematical Details
k-Means Clustering in Action
- Cluster-based Product Recommendation
- Scaling and Implementation Details
- Visualizing Clusters
Evaluating k-Means
- Between Sum-of-squares
- Within Sum-of-squares
- Combining k-Means with PCA
Academy Modules
Graded Quiz
Learning-by-Building Module (3 Points)
Diving into Wholesale Transactions
- Using any of the two unsupervised learning algorithms you’ve learned, produce a simple R markdown document where you demonstrate an exercise of either clustering or dimensionality reduction on the wholesale.csv data provided to you
Digging Deep into NYC Property Sales
- Using any of the two unsupervised learning algorithms you’ve learned, produce a simple R markdown document where you demonstrate an exercise of either clustering or dimensionality reduction on the nyc data provided to you
Explain your choice of parameters (how you choose k for k-means clustering, or how you choose to retain n number of dimensions for PCA) from the original data. What are some business utilities for the unsupervised model you’ve developed? The R Markdown document should be no longer than 4 paragraph and contain one or two visualizations.
Ad-Hoc Course Registration:
ON-SITE CLASS
- Date: 25 – 28 January 2021
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
- Investment: Rp. 5.200.000
ONLINE CLASS
- Date: 25 – 28 January 2021
- Time: 18.30 – 21.30
- Investment: Rp. 2.600.000
Workshop Receivables:
This workshop is recommended for:
The Unsupervised Machine Learning workshop is an intermediate-level programming workshop best suited to R programmers that are taking their first steps into data science and machine learning.
Students are assumed to have a working knowledge of R and have completed the necessary pre-requisites. Consider taking the pre-requisite course or a beginner-level course instead if you have no prior programming experience or statistics knowledge.
Past Workshops in this Series:
Students work through tons of real-life examples using sample datasets donated by our team of mentors and corporate partners. We believe in a learn-by-building approach, and we employ instructors who are uncompromisingly passionate about your growth and education.
Part of the Machine Learning Specialization
This workshop is part of the Machine Learning Specialization offered by Algoritma Data Science Academy. Participants are rewarded with a certificate of completion upon passing criteria, and are encouraged to advance further in the respective data science specialization.
Machine Learning: Classification 2
The science of solving classification tasks
Ad-Hoc Course Registration:
ON-SITE CLASS
- Date: 18 – 21 January 2021
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
- Investment: Rp. 5.200.000
ONLINE CLASS
- Date: 18 – 21 January 2021
- Time: 18.30 – 21.30
- Investment: Rp. 2.600.000
Course details :
Learn to apply the law of probabilities, boosting, bootstrap aggregation, k-fold cross-validation, ensembling methods, and a variety of other techniques as we build some of the most widely used machine learning algorithms today. Learn to add performance to your models using mathematically sound principles you’ll learn in this course.
We strongly recommend that you complete the Classification in Machine Learning 1 workshop prior to taking this course. Some concepts presented throughout the lecture may be less-than-ideal for practitioners who have not completed the pre-requisite courses.
Schedule
Course Producer
Samuel Chan
An RStudio-certified instructor and machine learning practitioner in the field of marketing automation, fraud detection, finance and e-commerce. Samuel is Indonesia’s top-ranked Stack Overflow user in R (top 5% worldwide) for three years running, and boasts certifications from RStudio, Microsoft, MongoDB, Neo4J Database, Stanford University, John Hopkins University, among others.
Prior to Algoritma, he has 8 years of working experience, including a stint as in-house consultant to several public-trading companies from his time staying in China, Japan and Singapore. He is today an active trainer and consultant for various companies in the financial industry. He has guest lectured in various campuses: Binus, NUS (National University of Singapore)’s The Logistics Institute, University of Indonesia, Universitas Gadjah Mada (UGM), Binus, Institute of Technology Bandung (ITB), Telkom University etc. Courses he authored are offered also in Singapore through Ngee Ann Polytechnic.
Samuel is also among the first recipients of Microsoft Professional Program Certificate in Data Science in Southeast Asia, having demonstrated proficiency in R, Python, Microsoft Azure, SQL / T-SQL, PowerBI and a list of other technologies, and among the first to be certified in RStudio’s program. Technical committee member and competition judge on Finhacks 2018, the largest Machine Learning competition of the year organized by PT. Bank Central Asia (BCA) and DailySocial.
4-Day Workshop Modules
Syllabus: Classification in Machine Learning 2
Module 1: Naive Bayes
Law of Probability
- Dependent and Independent Events
- Bayes Theorem
- Formula for Posterior Probability
Naive Bayes Classifier
- Characteristics of a Naive Bayes Classifier
- The “Naive” Assumptions
- Customer Churn Example
Practical and Performance Considerations
- The Case for Smoothing
- Laplace (Add-One)
- Thinking about Training vs Prediction Speed
Naive Bayes in Action
- Spam Classification
- Predicting on Text (Corpus)
- Predicting Political Party Affiliation
Module 2: Tree-Based Methods and Ensembles
Decision Trees
- Advantages and Model Characteristics
- Information Gain and Splitting Criterion
- Pruning and Tree Size
Decision Trees in Action
- Predicting Diabetes from Diagnostics Measurement
- AUC Curve
- Key Considerations and Practical Advice
Machine Learning Theories
- Logistic Regression, Naive Bayes, and Decision Trees Have More in Common Than You Think
- Industrial Applications
- Thinking About Decision Boundaries
High-Performance Machine Learning
- Bias-Variance Tradeoff Revisited k-Fold Cross-Validation
- Predicting Exercise Form with Fitness Tracker Data
Academy Modules
Graded Quiz
Learning-by-Building Module (3 Points)
Identifying Risky Bank Loans
- Use any of the 3 classification algorithms you’ve learned in this lesson to predict the risk status of a bank loan. The variable default in the dataset indicates whether the applicant did default on the loan issued by the bank.Use an R Markdown document to lay out your process, and explain the methodology in 1 or 2 brief paragraph. The student should be awarded the full (3) points when:
- The preprocessing steps are done, and the student show an understanding of holding out a test/cross-validation set for an estimate of the model’s performance on unseen data
- The model’s performance is sufficiently explained (accuracy may not be the most helpful metric here! Recall about what you’ve learned regarding specificity and sensitivity)
- The student demonstrated extra effort in evaluating his/her model and proposes ways to improve the accuracy obtained from the initial model
Ad-Hoc Course Registration:
ON-SITE CLASS
- Date: 18 – 21 January 2021
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
- Investment: Rp. 5.200.000
ONLINE CLASS
- Date: 18 – 21 January 2021
- Time: 18.30 – 21.30
- Investment: Rp. 2.600.000
Workshop Receivables:
This workshop is recommended for:
The Machine Learning: Classification 2 workshop is an intermediate-level programming workshop best suited to R programmers that are taking their first steps into data science and machine learning.
Students are assumed to have a working knowledge of R and have completed the necessary pre-requisites. Consider taking the pre-requisite course or a beginner-level course instead if you have no prior programming experience or statistics knowledge.
Past Workshops in this Series:
Students work through tons of real-life examples using sample datasets donated by our team of mentors and corporate partners. We believe in a learn-by-building approach, and we employ instructors who are uncompromisingly passionate about your growth and education.
Part of the Machine Learning Specialization
This workshop is part of the Machine Learning Specialization offered by Algoritma Data Science Academy. Participants are rewarded with a certificate of completion upon passing criteria, and are encouraged to advance further in the respective data science specialization.
Machine Learning: Classification 1
The science of solving classification tasks
Ad-Hoc Course Registration:
ON-SITE CLASS
- Date: 11 – 14 January 2021
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
- Investment: Rp. 5.200.000
ONLINE CLASS
- Date: 11 – 14 January 2021
- Time: 18.30 – 21.30
- Investment: Rp. 2.600.000
Course details :
Learn to solve binary and multi-class classification models using machine learning algorithms that are easily understood and readily interpretable. You will learn to write a classification algorithm from scratch, and appreciate the mathematical foundations underpinning logistic regressions and nearest neighbors algorithms.
We strongly recommend that you complete the Regression Models workshop prior to taking this course. Upon completion of this workshop, you will acquire the depth to develop, apply, and evaluate two highly versatile algorithms widely used today.
Schedule
Course Producer
Samuel Chan
An RStudio-certified instructor and machine learning practitioner in the field of marketing automation, fraud detection, finance and e-commerce. Samuel is Indonesia’s top-ranked Stack Overflow user in R (top 5% worldwide) for three years running, and boasts certifications from RStudio, Microsoft, MongoDB, Neo4J Database, Stanford University, John Hopkins University, among others.
Prior to Algoritma, he has 8 years of working experience, including a stint as in-house consultant to several public-trading companies from his time staying in China, Japan and Singapore. He is today an active trainer and consultant for various companies in the financial industry. He has guest lectured in various campuses: Binus, NUS (National University of Singapore)’s The Logistics Institute, University of Indonesia, Universitas Gadjah Mada (UGM), Binus, Institute of Technology Bandung (ITB), Telkom University etc. Courses he authored are offered also in Singapore through Ngee Ann Polytechnic.
Samuel is also among the first recipients of Microsoft Professional Program Certificate in Data Science in Southeast Asia, having demonstrated proficiency in R, Python, Microsoft Azure, SQL / T-SQL, PowerBI and a list of other technologies, and among the first to be certified in RStudio’s program. Technical committee member and competition judge on Finhacks 2018, the largest Machine Learning competition of the year organized by PT. Bank Central Asia (BCA) and DailySocial.
4-Day Workshop Modules
Syllabus: Classification in Machine Learning 1
Module 1: Logistic Regression
Relating Probabilities to Odds
- Understanding Odds
- Understanding Log of Odds
- Plotting Odds and Log of Odds
Logistic Regression from First Principles
- Sigmoidal Logistic Function
- Key Assumptions of Sigmoid Function
- Extra Proof: Intuition Behind The
- Sigmoid Function
Logistic Regression in Action
- Binary Logistic Regression
- Interpreting Coefficients
- Interpretation Against Continuous & Discrete Variables
Practical Tips and Case Study
- Flight Delay Prediction Examples
- Customer Churn and Attrition Examples
- Risk Modeling on Loans from Quarter 4, 2017
Performance Evaluation and Model Selection
- AIC (Akaike Information Criteria)
- Null Deviance and Residual Deviance
- Hauck Donner Effect
Module 2: Nearest Neighbours
Algorithm
Closer Look at Classification
- Probabilities vs Class responses
- Cross Validation and Out-of sample error
- Bias-variance trade off
- Confusion matrix (accuracy, sensitivity, specificity, & precision)
k-NN in Action
- Characteristics of k-NN
- Positives and Negatives
- Diagnosing Breast Cancer with k-NN
Building Blocks of k-NN
- Distance Function (Euclidean, Minkowsky)
- The k Parameter
- Standardization vs Min-Max Normalization
k-NN from First Principles
- Classifying Customer Segments with k-NN
- Writing Your Own k-NN Classifier
- Predicting Using Your Own k-NN Classifier
Academy Modules
Graded Quiz
Learning-by-Building Module (3 Points)
Logistic Regression on Credit Risk
- Applying what you’ve learned, present a simple R Markdown document in which you demonstrate the use of logistic regression on the lbb_loans.csv dataset. Explain your findings wherever necessary and show the necessary data preparation steps. To help you through the exercise, consider the following questions throughout the document:
- How do we correctly interpret the negative coefficients obtained from your logistic regression?
- How do we know which of the variables are more statistically significant as predictors?
- What are some strategies to improve your model?
Customer Segment Prediction
- Applying what you’ve learned, present a simple R Markdown document in which you demonstrate the use of k-NN on the wholesale.csv dataset. Compare the k-NN to the logistic regression model and answer the following questions throughout the document:
- What is your accuracy? Was the logistic regression better than k-NN in terms of accuracy? (recall the lesson on obtaining an unbiased estimate of the model’s accuracy)
- Was the logistic regression better than our kNN model at explaining which of the variables are good predictors of a customer’s industry?
- List down 1 disadvantage and 1 strength of each of the approach (k-NN and logistic regression)
Ad-Hoc Course Registration:
ON-SITE CLASS
- Date: 11 – 14 January 2021
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
- Investment: Rp. 5.200.000
ONLINE CLASS
- Date: 11 – 14 January 2021
- Time: 18.30 – 21.30
- Investment: Rp. 2.600.000
Workshop Receivables:
This workshop is recommended for:
The Machine Learning: Classification 1 workshop is an intermediate-level programming workshop best suited to R programmers that are taking their first steps into data science and machine learning.
Students are assumed to have a working knowledge of R and have completed the necessary pre-requisites. Consider taking the pre-requisite course or a beginner-level course instead if you have no prior programming experience or statistics knowledge.
Past Workshops in this Series:
Students work through tons of real-life examples using sample datasets donated by our team of mentors and corporate partners. We believe in a learn-by-building approach, and we employ instructors who are uncompromisingly passionate about your growth and education.
Part of the Machine Learning Specialization
This workshop is part of the Machine Learning Specialization offered by Algoritma Data Science Academy. Participants are rewarded with a certificate of completion upon passing criteria, and are encouraged to advance further in the respective data science specialization.
Regression Models
An in-depth look at regression models
Ad-Hoc Course Registration:
ON-SITE CLASS
- Date: 4 – 7 January 2021
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
- Investment: Rp. 5.200.000
ONLINE CLASS
- Date: 4 – 7 January 2021
- Time: 18.30 – 21.30
- Investment: Rp. 2.600.000
Course details :
This course strives for a fine balance between business applications and mathematical rigor in its treatment to regression models, one of the most essential statistical techniques in the field of machine learning. Its aim is to equip you with the knowledge to investigate relationships between variables of a data effectively and rigorously.
We strongly recommend that you complete Practical Statistics prior to taking this course. Upon completion of this workshop, you will acquire a rigorous statistical understanding of machine learning models, allowing you to extrapolate the same ideas into other, more advanced machine learning models.
Schedule
Course Producer
Samuel Chan
An RStudio-certified instructor and machine learning practitioner in the field of marketing automation, fraud detection, finance and e-commerce. Samuel is Indonesia’s top-ranked Stack Overflow user in R (top 5% worldwide) for three years running, and boasts certifications from RStudio, Microsoft, MongoDB, Neo4J Database, Stanford University, John Hopkins University, among others.
Prior to Algoritma, he has 8 years of working experience, including a stint as in-house consultant to several public-trading companies from his time staying in China, Japan and Singapore. He is today an active trainer and consultant for various companies in the financial industry. He has guest lectured in various campuses: Binus, NUS (National University of Singapore)’s The Logistics Institute, University of Indonesia, Universitas Gadjah Mada (UGM), Binus, Institute of Technology Bandung (ITB), Telkom University etc. Courses he authored are offered also in Singapore through Ngee Ann Polytechnic.
Samuel is also among the first recipients of Microsoft Professional Program Certificate in Data Science in Southeast Asia, having demonstrated proficiency in R, Python, Microsoft Azure, SQL / T-SQL, PowerBI and a list of other technologies, and among the first to be certified in RStudio’s program. Technical committee member and competition judge on Finhacks 2018, the largest Machine Learning competition of the year organized by PT. Bank Central Asia (BCA) and DailySocial.
4-Day Workshop Modules
Syllabus: Regression Models
Module 1: Regression Models I
OLS Regression
- Understanding Least Squares
- Simple Linear Regression
Linear Models in R
- Understanding Coefficients
- Plotting Regression
- Model Construction
Interpreting Linear Models
- Residuals Manually
- Coefficients Manually
- R-Squared Manually
Module 2: Regression Models II
Interpreting Linear Models
- Estimates and Standard Errors
- t-Value and p-Value
- Adjusted R-Squared
Multiple Regression
- Multicollinearity and VIF
- Model Assumptions
- Bias-Variance Trade-off
- Outliers: Leverage and Influence
- Model Limitation and Evaluation
Dive Deeper: Regression Models
- Model Selection and Specification
- Step-wise Regression
- All-possible Regressions
- Residual Plots
- Model Diagnostics
- Limitations of Regression Models
Academy Modules
Graded Quiz
Learning-by-Building Module (3 Points)
Recommendation on Lowering Crime Rates
- Write a regression analysis report applying what you’ve learned in the workshop. Using the dataset provided by you, write your findings on the different socioeconomic variables most highly correlated to crime rates.Explain your recommendations where appropriate.
Ad-Hoc Course Registration:
ON-SITE CLASS
- Date: 4 – 7 January 2021
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
- Investment: Rp. 5.200.000
ONLINE CLASS
- Date: 4 – 7 January 2021
- Time: 18.30 – 21.30
- Investment: Rp. 2.600.000
Workshop Receivables:
This workshop is recommended for:
The Regression Models workshop is an intermediate-level programming workshop best suited to R programmers that are taking their first steps into data science and data visualization.
Students are assumed to have a working knowledge of R and have completed the necessary pre-requisites. Consider taking the pre-requisite course or a beginner-level course instead if you have no prior programming experience or statistics knowledge.
Past Workshops in this Series:
Students work through tons of real-life examples using sample datasets donated by our team of mentors and corporate partners. We believe in a learn-by-building approach, and we employ instructors who are uncompromisingly passionate about your growth and education.
Part of the Machine Learning Specialization
This workshop is part of the Machine Learning Specialization offered by Algoritma Data Science Academy. Participants are rewarded with a certificate of completion upon passing criteria, and are encouraged to advance further in the respective data science specialization.
Practical Statistics
An in-depth statistics course from a data science perspective
Ad-Hoc Course Registration:
ON-SITE CLASS
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
ONLINE CLASS
- Time: 18.30 – 21.30
- Venue: Google Classroom
Course details :
Pave the statistical foundation for more advanced machine learning theories later on in the specialization by picking up the key ideas in statistical thinking. Learn to interpret correlations, construct confidence intervals and other statistical principles that form the basis of many common machine learning models.
The 2-day course is optional for participation of the Data Visualization and Machine Learning Specialization and intended for learners without prior experience in statistics.
Course schedule:
Course Producer
Samuel Chan
An RStudio-certified instructor and machine learning practitioner in the field of marketing automation, fraud detection, finance and e-commerce. Samuel is Indonesia’s top-ranked Stack Overflow user in R (top 5% worldwide) for three years running, and boasts certifications from RStudio, Microsoft, MongoDB, Neo4J Database, Stanford University, John Hopkins University, among others.
Prior to Algoritma, he has 8 years of working experience, including a stint as in-house consultant to several public-trading companies from his time staying in China, Japan and Singapore. He is today an active trainer and consultant for various companies in the financial industry. He has guest lectured in various campuses: Binus, NUS (National University of Singapore)’s The Logistics Institute, University of Indonesia, Universitas Gadjah Mada (UGM), Binus, Institute of Technology Bandung (ITB), Telkom University etc. Courses he authored are offered also in Singapore through Ngee Ann Polytechnic.
Samuel is also among the first recipients of Microsoft Professional Program Certificate in Data Science in Southeast Asia, having demonstrated proficiency in R, Python, Microsoft Azure, SQL / T-SQL, PowerBI and a list of other technologies, and among the first to be certified in RStudio’s program. Technical committee member and competition judge on Finhacks 2018, the largest Machine Learning competition of the year organized by PT. Bank Central Asia (BCA) and DailySocial.
2-Day Workshop Modules
Syllabus: Practical Statistics
Module 1: Descriptive Statistics
5-Number Summary
- Mean, Median, and Mode
- Measures of Central Tendency
- Quantiles in R
Central Tendency & Variability
- Visualizing Central Tendency
- Variance, and Covariance
Standard Score and z-Score
- Standard Normal Curve
- Central Limit Theorem
- z-Score Calculation & Student’s T-test
Module 2: Inferential Statistics
Probabilities
- Probability Mass Function
- Probability Density Function
- Expected Values
- p-Values
Intervals
- Confidence Intervals
- Prediction Intervals
Inferential Statistics in Practice
- Hypothesis Testing
- Deriving Scientific Truths from Data
- Case Study
Academy Modules
Tips & Techniques: R for Statisticians
- Density Plots
- Interpreting Box Plots (Box-and-Whisker)
- Better Summary Statistics with skimr()
Learning-by-Building Module (Not Graded)
Statistical Treatment of Retail Dataset
- Using what you’ve learned, formulate a question and derive a statistical hypothesis test to answer the question. You have to demonstrate that you’re able to make decisions using data in a scientific manner.
Examples of questions can be:
- Is there a difference in profitability between standard shipment and same-day shipment?
- Supposed there is no difference in profitability between the different product segment, what is the probability that we obtain the current observation due to pure chance alone?
Workshop Receivables:
This workshop is recommended for:
The Programming for Data Science workshop is designed for casual learners, working professionals and non-programmers that are taking their first steps into data science and machine learning.
Students are not assumed to have a working knowledge of R or prior proficiency in statistics / mathematics / algebra. At such the workshop follows a gentle learning curve and emphasize on hands-on, one-to-one tutoring from our team of instructors and teaching assistants.
Consider taking our Intermediate-level workshops instead for more advanced-level materials in statistical programming and machine learning.
Past Workshops in this Series:
Students work through tons of real-life examples using sample datasets donated by our team of mentors and corporate partners. We believe in a learn-by-building approach, and we employ instructors who are uncompromisingly passionate about your growth and education.
Part of the Data Visualization and Machine Learning Specialization Track
This workshop is part of the two specialization tracks offered by Algoritma Data Science Academy. Participants are rewarded with a certificate of completion upon passing criteria, and are encouraged to advance further in the respective data science specialization.
Programming for Data Science
R programming for the modern-day data scientist
Ad-Hoc Course Registration:
ON-SITE CLASS
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
ONLINE CLASS
- Time: 18.30 – 21.30
- Venue: Google Classroom
Course details :
Programming for Data Science is a course that covers the important programming paradigms and tools used by data analysts and data scientists today. You will be guided through a series of coding exercises designed to maximize your familiarity with data science programming in RStudio, an integrated development environment for the statistical computing language R.
Upon completion of this workshop, you will be familiar with the programming language, popular tools, libraries (data science packages) and toolkits required to excel in your data analysis and statistical computing projects.
Schedule
Course Producer
Samuel Chan
An RStudio-certified instructor and machine learning practitioner in the field of marketing automation, fraud detection, finance and e-commerce. Samuel is Indonesia’s top-ranked Stack Overflow user in R (top 5% worldwide) for three years running, and boasts certifications from RStudio, Microsoft, MongoDB, Neo4J Database, Stanford University, John Hopkins University, among others.
Prior to Algoritma, he has 8 years of working experience, including a stint as in-house consultant to several public-trading companies from his time staying in China, Japan and Singapore. He is today an active trainer and consultant for various companies in the financial industry. He has guest lectured in various campuses: Binus, NUS (National University of Singapore)’s The Logistics Institute, University of Indonesia, Universitas Gadjah Mada (UGM), Binus, Institute of Technology Bandung (ITB), Telkom University etc. Courses he authored are offered also in Singapore through Ngee Ann Polytechnic.
Samuel is also among the first recipients of Microsoft Professional Program Certificate in Data Science in Southeast Asia, having demonstrated proficiency in R, Python, Microsoft Azure, SQL / T-SQL, PowerBI and a list of other technologies, and among the first to be certified in RStudio’s program. Technical committee member and competition judge on Finhacks 2018, the largest Machine Learning competition of the year organized by PT. Bank Central Asia (BCA) and DailySocial.
3-Day Workshop Modules
Module 1: Data Science in R
Data Science in R
- R Programming Basics
- Why Learn R?
- R Studio Interface
- Data Structures in R
Working with Data
- Reading & Extracting Data
- Understanding Statistics
- Exploratory Data Analytics
Data Manipulation
- Working with Your Global Environment
- Getting Familiar with Your Workspace
- Continuous and Categorical Data
Module 2: Data Manipulation
Data Manipulation II
- Vector Types and Classes
- List and Objects
- Matrix and Data Frames
Practical Data Cleansing
- The Data Transformation Process
- Reproducible Data Science Projects
- Reading and Writing from Your IDE
R in Practice
- Programming Exercise: e-Commerce Retail Datasets
- In-depth Review of Data Frame Subsetting
- Sampling and Randomization
- Cross-Tabulations
- Aggregations
Academy Modules
Graded Quiz
Working with R
- R Scripts and Functions
- R Markdown
- Why Care About Reproducibility
Learning-by-Building Module (2 Points)
Writing your code as R scripts make up for automation and integration with other tools and services, while writing a R Markdown presents your findings and recommendations in a way that is friendly to non-technical / managerial team members.
- R Script to clean & transform the data
Write a R script containing a function (name the function however way you want) that reads a dataset as input, perform the necessary transformation and export a cross-tabulation numeric result or plot as output.
- Reproducible Data Science
Create an R Markdown file that combines your step-by-step data transformation code with some explanatory text. Add formatting styles and hierarchical structure using Markdown.
Workshop Receivables:
THIS WORKSHOP IS RECOMMENDED FOR:
The Programming for Data Science workshop is designed for casual learners, working professionals and non-programmers that are taking their first steps into data science and machine learning.
Students are not assumed to have a working knowledge of R or prior proficiency in statistics / mathematics / algebra. At such the workshop follows a gentle learning curve and emphasize on hands-on, one-to-one tutoring from our team of instructors and teaching assistants.
Consider taking our Intermediate-level workshops instead for more advanced-level materials in statistical programming and machine learning.
Past Workshops in this Series:
Students work through tons of real-life examples using sample datasets donated by our team of mentors and corporate partners. We believe in a learn-by-building approach, and we employ instructors who are uncompromisingly passionate about your growth and education.
Part of the Data Visualization and Machine Learning Specialization Track
This workshop is part of the two specialization tracks offered by Algoritma Data Science Academy. Participants are rewarded with a certificate of completion upon passing criteria, and are encouraged to advance further in the respective data science specialization.