April 9th to May 3rd
Mondays, Tuesdays and Thursdays from 6pm to 9pm
Moringa School, Ngong Lane Plaza, 1st floor
The course is 40 hours in total (3 times per week for 3 hours each, for a total of 4 weeks) and includes theoretical background for various methods and hands-on practical exercises implemented in Python, with a final Kaggle project. The content includes an introduction to statistical techniques, regression, classification, analyzing and visualizing data, and neural networks. We will focus on core Data Science concepts that highlight the importance of exploring data with code in order to extract understanding.
- Intermediate / Advanced Data science students
- Professionals with a high level in Data science
- Undergraduate degree in STEM field with coding experience (R, Python, MATLAB).
Deeper understanding of data science methods with familiarity of appropriate tools. Ability to properly distinguish which methods are appropriate for various problem types. Ability to distinguish model shortcomings.
In order for you to attend the class, you will have to go through the following:
- One programming language.
- College level probability. If not take:
- Complete as many of the remaining statistics ad probability modules:
- College level linear algebra. If not focus on vectors, Matrices, dot product, cross product, matrix operations,
- As much as possible of sequence of classes at:
- Skyler Speakman, Research Scientist at IBM Africa, PhD from Carnegie Mellon University in Information Systems, M.S. in Machine Learning.
- Isaac Markus, Research, Scientist at IBM Africa, PhD from UC Berkeley in Computational Material Science.
- Srihari Sridharan, Research Scientist at IBM Africa, M.S. in Human Computer Interaction from New York University.
- Meenal Pore, Research Scientist at IBM Africa, PhD in Chemical Engineering from Cambridge University.
- Setting up python and Jupiter
- Capstone project
- Python Tutorial
- Overview of Machine Learning Methods
- Linear Regression
- Math Intro
- Hands on Session (House Prices)
- Preparing a Data Set
- Feature Engineering
- Accuracy, recall, precision, etc
- Classification (Supervised)
- Single Class
- Hands-on Session (Iris Data Set)
- Decision Trees
- Logistic Regression
- Dimensionality Reduction
- Descriptive Statistics
- Conditional Probabilities
- Neural Network
- Deep Neural Network
- Hands-on Session (Cats and Dogs)
- Projects Presentations