Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms,[1] without needing to be explicitly told what to do by any human-developed algorithms

(Wikipedia: https://en.wikipedia.org/wiki/Machine_learning)

Webinar agenda:

• Definitions of AI and ML
• Origins of ML
• About ML
• ML vs Neural Network
• Pattern Recognition
• Supervised Learning:
• Data labelling
• Classification vs Regression
• Classification – Decision tree
• Classification – Random forest
• Regression
• K-Nearest Neighbour (KNN)
• Naïve Bayes
• Unsupervised Learning:
• Unlabelled dataset
• Clustering
• Dimensionality reduction (PCA)
• Dimensionality reduction (f-SNE)
• Semi-supervised learning:
• Boosting
• Reinforcement
• Selecting the best algorithm
• Resources lists
• Next steps