Data Science Fundamentals
Machine Learning
3
Days
Data Science Toolkit
Day 1
Get In Touch
PT. Algoritma Data Indonesia.
RDTX Square, 9th Floor. Setiabudi, DKI Jakarta 12930.
WhatsApp: 0816-692-471
Email: community@algorit.ma
Course details :
This 3-day workshop is a careful combination of statistical theory, hands-on coding and programming exercises to help students understand — and implement — some of the most widely used, and fundamental, machine learning algorithms.
By building regressors and classifier algorithms from scratch, the student will go beyond applying machine learning models to actually developing their own models — and learn the right approach to fine-tuning the model performance as well as evaluating model fit against unseen data. Upon completion of the workshop, the student will be well versed in an array of important, versatile machine learning algorithms and equipped with the right knowledge to apply them to future datasets in their daily job.
Data are becoming the new raw material of business ~ Craig Mundie, Microsoft
Please bring along:
- 1x Laptop
- Purchased ticket (from organizer’s website)
Schedule
Event Ended
Explore other data science workshops
Trainer
Viska Aprillia
viska@algorit.ma
Detailed Syllabus
Syllabus: Data Science Fundamentals Machine Learning
23 October 2017
Data Science Explained
- Description of course materials and the learning environment
- A comprehensive view on the roles of data science, the relating professions, career prospects and outlook.
- Description of the workflow, tools, setup and programming languages in the course
R Programming Basics
- Setting up the Workspace and Environment
- Working with data types: scalar, vector, list, matrix, data frame
- R’s built-in functions
- Inspecting data using built-in functions
- R’s plotting capabilities
- R Markdown and reproducible research
Statistics Fundamental
- Demonstrate the use of various statistics in exploratory data analysis: 5-number summary, mean, mode, interquartile range, variance, standard deviation and correlation
- Plots: scatterplots, scatterplot matrices, line graphs, histogram, ab-line, x and y-axis styling, plot title, tips and tricks for plotting in R
- Quick way to get a “sense” of the distribution of our dataset
- Confidence intervals and Hypothesis Testing
Machine Learning Fundamental
- Prediction with linear models
- Precision and Recall
- Prediction on unseen data
24 October 2017
Data Wrangling
- Continuous variables and Categorical variables
- Factors and levels
- Description of the workflow, tools, setup and programming languages in the course
- Reading from different data formats: CSVs, JSON, webpages, API
- Various data preprocessing and data cleansing techniques
Linear Regression
- Code examples of linear regression
- Statistical principles behind least squares regression
- Linearity assumption
- Dependent and Independent variables
- Inspecting data using built-in functions
- R-squared
- Interpreting coefficients
Improving Model’s Performance
- Limitations of common machine learning techniques
- Preventing overfitting
- Bias-Variance Tradeoff
- k-fold Cross Validation
25 October 2017
Multivariate Regression
- Interaction term
- Confounding variables
- Measures of fit
- ANOVA
Classification in Machine Learning
- k Nearest Neighbors and distance function
- Logistic Regression and the sigmoid curve
- Decision Tree
- Random Forest
- Bootstrap Aggregation and Boosting
- Multiclass classification
- Evaluating model’s performance
Building a classification algorithm
- Finding datasets
- Feature engineering
- Training on unseen data
This workshop will cost 3 workshop credits for subscribers. Non-subscribers are welcomed to participate at a cost of IDR 3,000,000.
Workshop Receivables:
Data Science Fundamentals Series
Workshops in our Data Science Fundamentals series are tailored to 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 Data Science Intermediate workshops instead for more advanced-level materials in statistical programming and machine learning.
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.
