Machine Learning for Analysts

In 6 months you will learn one of the highest paid and fastest growing skills in the Tech Industry
This intense, immersive course is guaranteed to boost your employability.

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Online: Part-time

Jul 06, 2020 - Sep 30, 2020

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Overview

Build systems and models that drive decision making and learns from data.

Machine Learning Engineers are key to future decision-making in any data-driven company.

Guided by some of the Best


Our support team have been there and done that - they are experienced practitioners of data science in various fields such as Utilities, Retail and Financial Services; with a solid academic background.

Learn How to Solve Problems


The course is built around solving actual problems whilst learning about the latest machine learning techniques.

Apply the skills


EXPLORE prides itself on the practicality of the course. Participants must be able to apply the skills/knowledge that they acquired in your everyday job to impact their business.

Extended access to Material


Participants will have extended access to the material that are covered in the course, allowing them to revisit the techniques to keep it fresh in their mind.

Curriculum

Start thinking like a machine learning engineer

Students will master the model building process and the various machine learning algorithms to use when predicting or classifying. For those not born programmers, there will be material to cover the basics of programming in python.

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Programming

Students will learn to code in python. From basic syntax to complex logic statements and functions. They will also cover utilising external libraries to make programming simpler.

OUTCOMES:

  • Able to write basic python code and debug errors
  • Able to write logic statements and for-loops
  • Able to write functions to code efficiently
  • Able to import and use libraries such as Numpy and Pandas

Instructors

Instructors

We take teaching seriously. Great teachers inspire us to connect with topics on a profound level. With experience in the field and the classroom, our data science instructors are unmatched. Simply put: students learn from the best.

Jaco Jansen van Rensburg

Lead Instructor

Jaco is a Lead Data Scientist in the EXPLORE Data Science Academy. He has spent the bulk of his career on scientific and industrial research and holds a PhD in Mechanical Engineering with a focus on mathematical modelling and optimisation.

Jason Webster

Lead Instructor

Jason holds a Master's in Physics and loves to explore the way the universe works. He's recently found a home in data science, and now likes to build self-driving cars for fun. He currently works in the content creation side of the academy, working towards bringing Data Science to all parts of South Africa.

Dewald Botha

Lead Instructor

Dewald is an actuary and senior data scientist. He was one of the first brave souls to join the EXPLORE ship in 2016. He is managing EXPLORE's learning platform, Athena, and leading the corporate training - 2/3 day bootcamps to upskill employees.

Why Machine Learning?

Make better decisions and smart actions in real time without human intervention

Four megatrends are fundamentally changing the shape of our world:

  • Vast amounts of data are being generated every minute.
  • The processing speed of our machines is increasing exponentially.
  • We now have cloud providers who can store insane amounts of data for a few dollars.
  • Powerful open source algorithms that can read, write, translate and see are now available to everyone.
 

All of these things above mean that it’s becoming easier and quicker to produce models that can analyze bigger/more complex data and deliver more accurate results – even on a very large scale. By building prediction models a business has a better chance of identifying profitable opportunities – or avoiding unknown risks.

Tuition and Financing

Find the right tuition plan for you

Upfront Payment

One upfront payment, payable before the start of the course


Amount:

$550
Monthly Payment

3 Monthly payments, payable in advance at the start of each month


Amount:

$250 per month

FAQ

You have questions; we have answers

All applications happen via our website: www.explore-datascience.net. Select the course and location (on campus or online) which works for you and click Apply Now. For on campus study, there are various requirements, for online applications it is simpler.
For on campus study there are some qualifying criteria, which can be found in the T’s and C’s of our Money-Back Promise (on the website). For online study, anyone qualifies!
This depends on what your constraints are - can you study on campus, full time, or do you need the flexibility of online study. On campus is the premium experience, backed up with a Money-Back Promise! Still not sure… Use our handy tool to find the course for you. Look for the link under ‘Find your tribe’
We are SETA Accredited and our Data Science course is at NQF level 5. Upon completion of our courses, you will receive a completion certificate.
EXPLORE Data Science Academy graduates are Amazing People, doing Amazing Things. Our Alumni are highly sought after and 97% of them have found industry related employment within 6 months of graduating.
First stop is to check your Spam or Junk mail folders. Sometimes the internet gremlins hide our emails there. Alternatively, log back into your student profile and check that you completed all the steps for application. Look for the sign-in button on the top right of the website page.
Webinars will be scheduled for content briefing, and, we have a really cool student chat platform which gives you access to the student community and course facilitators.
EXPLORE Data Science Academy graduates are Amazing People, doing Amazing Things. Our Alumni are highly sought after and 97% of them have found industry related employment within 6 months of graduating.
All applications happen via our website: www.explore-datascience.net. Select the course and location (on campus or online) which works for you and click Apply Now. For on campus study, there are various requirements, for online applications it is simpler.
For on campus study there are some qualifying criteria, which can be found in the T’s and C’s of our Money-Back Promise (on the website). For online study, anyone qualifies!
This depends on what your constraints are - can you study on campus, full time, or do you need the flexibility of online study. On campus is the premium experience, backed up with a Money-Back Promise! Still not sure… Use our handy tool to find the course for you. Look for the link under ‘Find your tribe’
We are SETA Accredited and our Data Science course is at NQF level 5. Upon completion of our courses, you will receive a completion certificate. 
EXPLORE Data Science Academy graduates are Amazing People, doing Amazing Things. Our Alumni are highly sought after and 97% of them have found industry related employment within 6 months of graduating.
First stop is to check your Spam or Junk mail folders. Sometimes the internet gremlins hide our emails there. Alternatively, log back into your student profile and check that you completed all the steps for application. Look for the sign-in button on the top right of the website page.
You will code using Python in a Jupyter Notebook environment.
You will predict whether someone will make an insurance claim in the following year and what the amount of that claim will be.
Yes. Even though there is complex maths behind some of the models, we won’t explicitly cover it. We will mainly cover how the algorithms work and how to implement them in Python.
No. For those not born programmers, we have material on programming in python, to make sure you have the building blocks for the more complex machine learning programming.
Yes, however you can’t pass or fail. In order to complete the course you will have to submit a few multiple choice quizzes and model building project.
Supervised Learning - linear regression, logistic regression, decision trees, random forests, SVM’s, k-nearest neighbours.
Unsupervised learning - variance thresholds, PCA, k-means clustering, hierarchical clustering, Gaussian mixture models.
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