As the size and complexity of data continues to evolve, data users ability to drive actionable insight from data is quickly becoming not only a differentiator but a table stake for the world’s most successful companies. While historically a skillset possessed only by those with degrees in computer science, probability / statistics and mathematics, the advancement of end user analytics toolkits such as Python had dramatically brought down the barrier of entry.
We will enable you to derive deep insights from data to drive actionable decisions within your business to unlock the full potential of enterprise scale data. In this course, you will work through a series of real live business cases that utilizes a number of machine learning methodologies to uncover hidden value and insights within a company’s data.
Data users such as Project Managers, Accountants, Business Analysts, managers or any role that needs to understand how to make dependable data guided decisions
This course provides participants a broad overview of practical Machine Learning (ML) methodologies deployed through Python and requires no previous coding background. At the completion of the course, participants will have
1. Understanding the various data types you will encounter using Python
2. Assigning values to variables and building user-defined functions
3. Working with the most common data structures in Python (lists, sets and tuples)
4. Teaching Python to make decisions (conditional statements)
5. Creating dynamic pieces of code (loops)
6. Building advanced data structures (dictionaries)
7. Supercharging Python with re-usable, shared code (libraries)
8. Introducing ML with Python (Supervised vs. Unsupervised)
9. Running Supervised ML models using Python (Regressions, Random Forest, Gradient Boosting and Neural Networks)
10. Running Unsupervised ML models using Python (k-nearest neighbor, k-means)
Join our public courses in our New Zealand facilities. Private class trainings will be organized at the location of your preference, according to your schedule.