Machine learning in Data Science with Python

Master big data; with our complete, weekend only, data science course.


ByAswadi Abdul Rahman, Senior Data Scientist @ Axiata Digital
Artificial IntelligenceBusinessCoding & DevelopmentData Science & Analytics

This course will teach you how to use machine learning tools, related to data science, in your own field of expertise and gives you a solid training in Python and data analysis. This is a start to end complete data science course.

Course Overview

weekend

28/04/2018 @ 12:00 AM - 27/05/2018 @ 11:59 PM

4 days, 32 hours

PJ Trade Center, Ispace

beginner

MYR 3000

see full course

Why you should take this course

Data is the new oil and learning how to collect, manipulate, ascertain decisions and solutions to your problems is a skill highly sought after in this new data driven world. Every day, organizations generate a multitude of new data on customers, products, processes, and the industry as a whole. To tackle this data and to make something useful out of it, we have data analytics and data science. You can see data analytics, or business analytics, as a super-set of data science, business analytics provides the data for the data scientists. A huge part of data science is machine learning, which is also a subset of artificial intelligence. Machine learning refers to the ability of a "machine" to learn and to make data-driven decisions on its own and to analyze data. Jobs requiring machine learning skills are paying an average of MYR 27,000 a month for Southeast Asia. The explosion in digital data, bandwidth and processing power, combined with new tools for analyzing data has sparked massive interest in the emerging fields of data science. Organisation of all sizes are turning to people who can translate this trove of data created by mobile sensors, social media, surveillance, medical imaging, smart grid and the like into predictive insights that lead to business value. Despite the growing opportunity, demand for data scientist that have machine learning skill has outpaced supply of talent. So, this course will help you to skills up and get yourself familiar into data science world. This course is for anyone that is interested in a career as a data scientist or who is just interested in knowing more about machine learning. This course has been designed to help you learn complex theory, algorithms and coding libraries in a straightforward way. Attendees should have a basic understanding of Python and mathematics. You must bring a laptop installed with Anaconda, Jupyter notebook and Anaconda.

Why you should learn from Aswadi Abdul Rahman

Aswadi Abdul Rahman

Aswadi Abdul Rahman

Senior Data Scientist @ Axiata Digital

Despite the growing opportunity, demand for data scientist that have machine learning skill has outpaced supply of talent.

What you will learn

Introduction to Data Science and machine learning

You will learn about the end to end journey of a data scientist. From getting the data up to data visualization. We will make sure you know all the terminology, tools, and practices before we dive into specifics. The instructor can answer all your questions and help you with installing the tools if necessary.

Data pre-processing and feature engineering

After we made sure everybody is on the same line and prepared for take off, we go straight into data pre-processing and feature engineering. You will learn how to manipulate the data using pandas and numpy package which are libraries in python. You will also learn on how to transform the data from object to array for further processing in machine learning.

Dimensionality Reduction

In the third module you will explore PCA, which stands for principal component analysis in python, and manifold learning. You will learn to reduce your feature or category while maintaining its accuracy and increase the computing time.

Supervised Learning - Regression

You will learn about simple linear regression, multiple linear regression and polynomial regression. You will explore the different regression methods which includes estimating the relationship among features and also to forecast.

Supervised Learning - Classification

During the fifth module we will continue with logistic regression, K-NN, SVM, Kernel SVM, naive bayes, decision tree classification and random forest classification. These models can predict labels in data sets as two or more discrete categories.

Unsupervised Learning - Clustering

Clustering is one of the most well known methodologies in data science. You will learn and practice with K-Means and Gaussian mixture models. You will learn models that identify structure in unlabeled data.

Association Rule Learning

Explore all facets of market basket analysis, you will learn what association analysis is ,that it is easy to run and relatively easy to interpret and how to apply it in your field of expertise.

Reinforcement Learning

Reinforcement Learning is all about neural networks. You'll learn what reinforcement learning is, how it's used to optimize decision making over time, and how it solves problems in games, advertising, and stock trading.

Natural Language Processing

You will perform your own sentiment analysis using NLP and how to use it with machine learning.

Model Selection

The last day of our data science program, you will be covered in model selection, like k-fold cross validation, parameter tuning and grid search. You'll explore the concept of model validation and hyper-parameter optimization, focusing on intuitive aspects of the bias–variance trade-off and how it comes into play when fitting models to data.

Introduction to Deep Learning

In the last part of this course you will be introduced to Deep Learning. You will learn what Deep Learning is, why we use and you will see how neural network change the whole machine learning game.

Who is this course for?

Developers Data/business analysts Entrepreneurs
Developers Data/business analysts Entrepreneurs

Attendees should have a basic understanding of Python and mathematics. You must bring a laptop installed with Anaconda, Jupyter notebook and Anaconda. Although this is a beginner course and we take you from 0 to 1 in data science, we do expect that attendees have a basic knowledge of Python and minimal level of mathematics and statistics (bachelor degree level). Everybody is welcome but keep in mind that the learning curve might be steeper for you since a lot of the exercises are with python and demand a certain level of mathematical thinking. This course adds the most value to data and business analysts who are looking to step up their data game and expand to machine learning and its tools.

All the instructors are very helpful and eager to share their knowledge. I learned directly how to apply new knowledge, boosted my network by working on case studies with other attendees and got amazing career guidance by UBARU after the course. Every course feels like it is made for me.

- Ruvina Danaraj - Branding and Partnerships at SOCAR Malaysia.

A day in an UBARU Course

9.00 AM

Class

Learn the key skills and methods through theory lecture, Q&A and activities. You will enjoy a formidable presentation based on the instructors industry expertise. To ensure every chapter is relevant to you, interaction plays a big part.

12.00 PM

Lunch (optional)

Enjoy a fully provided lunch, review the morning session with your classmates and network with the instructors. We will make sure everything gets prepared for the afternoon session. This is also a good time to get some extra help, where needed.

12.45 PM

Practice

Theory is one part, exercise is what is creating a new skill. Practice your new knowledge with real-life case-studies, examples and exercises. Work together in groups, get extra help from the instructor and learn how to apply your new skills in your area of expertise.

6.00 PM

Network (optional)

Optional network session after the course, talk to your instructor, get to know your classmates and expend your professional network. Enjoy our meet ups, open discussions, and happy hours, in our personal after course sessions.

9.00 AM

Class

Learn the key skills and methods through theory lecture, Q&A and activities. You will enjoy a formidable presentation based on the instructors industry expertise. To ensure every chapter is relevant to you, interaction plays a big part.

12.00 PM

Lunch (optional)

Enjoy a fully provided lunch, review the morning session with your classmates and network with the instructors. We will make sure everything gets prepared for the afternoon session. This is also a good time to get some extra help, where needed.

12.45 PM

Practice

Theory is one part, exercise is what is creating a new skill. Practice your new knowledge with real-life case-studies, examples and exercises. Work together in groups, get extra help from the instructor and learn how to apply your new skills in your area of expertise.

6.00 PM

Network (optional)

Optional network session after the course, talk to your instructor, get to know your classmates and expend your professional network. Enjoy our meet ups, open discussions, and happy hours, in our personal after course sessions.

Every course with UBARU is another great experience. Every time I attend, the instructor gives a unique twist to the whole experience. These instructors are the best in their field, and you can definitely see that during the course. When I attend a course in which I have zero experience, the instructors are patient and there to help.

Overall great experience, good vibe and walked away with some good connection due to the networking sessions. Thanks!

- Khaled Emdad - Performance Marketing Manager at Admiral Digital.

Every course with UBARU is another great experience. Every time I attend, the instructor gives a unique twist to the whole experience. These instructors are the best in their field, and you can definitely see that during the course. When I attend a course in which I have zero experience, the instructors are patient and there to help.

Overall great experience, good vibe and walked away with some good connection due to the networking sessions. Thanks!

- Khaled Emdad - Performance Marketing Manager at Admiral Digital.

Frequently Asked Questions

You have a question? We have an answer!

What is the benefit of classroom learning over online courses?

To ensure you get the most out of the course, all our teaching moments are on location in one of our classrooms. Whether you learn something you have never done before or you are enhancing your current skills with an intermediate or expert course, you will get the most out of it by interacting with your instructor and classmates face to face. Also, we strive to make every course as relevant for each attendee as we can. By learning in a classroom you have the chance to get tips and exercises to apply your new knowledge in your field of expertise. We have classroom locations in TTDI and PJ Trade Center. You will find the location for your course in the course overview at the top of the page.

I cannot attend on this date, can I sign up for the next one?

Yes you can! Your time is precious and we understand that. Luckily all our courses are being given on a regular basis. Drop us a message through the contact section below and we will notify you as soon as the next enrolment dates are known!

How do I secure my spot in a course?

To ensure every student gets full attention, guidance and help where needed, all our courses have a limited amount of open seats. We work according to first come, first serve. Your spot in one of our courses, bootcamps, guest lectures or events, is secured as soon as you payed and received your ticket.

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