Fleming’s Left Hand Rule in Motors and Robotics
Why it matters: Fleming’s left hand rule is an important concept to understand when working with electric motors which are used in robotics.
Introduction to Naive Bayes Classifiers
Why it matters: Naive Bayes probabilistic classifiers are one of the simplest machine learning algorithms based on the Bayes theorem, it is fast, accurate, and reliable.
What is Joint Distribution in Machine Learning?
Why it matters: Joint distribution is used a lot in statistical analysis, but it can also be used in machine learning as a classification strategy to produce generative models.
MLFlow vs. Kubeflow: What is the Difference?
Why it matters: MLFlow vs. Kubeflow: let us understand the similarities, their differences, when are they used. What use cases work best?
Introduction to Autoencoders and Common Issues and Challenges
Why it matters: An autoencoder is an artificial neural deep network that uses unsupervised machine learning to efficiently encode and compress data.
What is Semi-Supervised Learning?
Why it matters: Semi supervised machine learning algorithms are a hybrid that takes the best parts of supervised learning and unsupervised learning algorithms.
Introduction to DALL·E 2 Art Generator: How Does it Work?
Why it matters: Dall E 2 AI art generator uses Contrastive Language Image Pre-training (CLIP) to generate images from text.
Introduction to Classification and Regression Trees in Machine Learning
Why it matters: Classification and Regression trees or CART for short is a term used to describe decision tree algorithms that get used for classification and regression tasks. The results of these trees are very easy to understand which gives them an edge over other algorithms.
Chatbots vs. IVR: The Difference and Pros and Cons
Why it matters: Chatbots and IVR systems are two commonly used support systems in business. It is important to know what they are, the advantages and disadvantages of both, and the differences between them.
