This learning track introduces you to the fundamental concepts of AI and machine learning breaking down the big terms used to describe the technology. It is recommended for anyone enthusiastic about AI and interested in understanding what the technology is about. It also targets those interested in having a basic understanding of AI and machine learning and its application in other fields such as business, health and law etc. This path is also suitable for business people and leaders curious about AI and how it can impact their businesses. A learner can choose to take one or all courses in each category based on their preference and career goals. All the courses are free (MOOC) but a learner can upgrade to paid option at will for certification and participation in the projects.

This track targets learners looking to go beyond the fundamentals and ready to dig deep into the workflows, methods and modeling techniques. Some of the resources involve programming either in Python or R hence the necessity of  having a basic understanding of the languages. It is important that you understand how to use the programming languages to manipulate data before delving into machine learning. Here are some of the resources you can use to familiarize yourself with the programming languages.

Having acquired the basic programming knowledge you can start your journey into machine learning using the following resources.

Developing machine learning or data science frameworks and solutions may not necessarily involve direct mathematical computations unless you are creating a custom algorithm. This is mainly because there numerous machine learning and deep learning packages continuously being developed for this purpose such as scikit-learn, TensorFlow, Weka and R-caret among others. However, having mathematical intuition is essential to developing useful machine learning and deep learning products. You will need mathematical intuition for cases such as selecting model validation techniques, understanding Bias-variance tradeoff, and selecting the right algorithm for your solution among others. Having a basic understanding of Linear Algebra, Probability Theory & Statistics, Multivariate Calculus and Algorithms is key to obtaining the fundamental mathematical intuition for developing effective machine learning solutions. Here are some of the resources for studying mathematics for machine learning.

Machine learning as a branch of Artificial Intelligence uses data-driven algorithms to develop software applications that can accurately predict outcomes without explicit programming. Data science is an interdisciplinary field that incorporates techniques such as visualization and data mining to extract insights or knowledge from data. While data science deals with curating data for analytics and visualization  machine learning deals with implementing this data in machine. Therefore, the intersection between the two makes data science essential to developing and deploying AI applications. As a beginner you can start your journey into data science by learning the fundamentals of statistics to give you a strong foundation in analytical techniques. Sharpen your analytical skills using the following resources.

Having grasped the fundamentals of statistics you now have foundational knowledge essential to understanding data science methods and tools. By taking the following courses you will be able to experiment and implement data analysis techniques and machine learning algorithms.