Applied Machine Learning (Level 8)

Introduction to Machine Learning, History, Early Applications, Machine Learning Techniques, Linear Algebra Review, Regression – One Variable. Learning, Representation, and Neural Network Investigate non-linear hypotheses, how neurons and the brain function, and how they might be used for multi-class categorization. Using the back-propagation technique to aid in the learning of neural network parameters.

System Design Using Machine Learning Choosing the best machine learning assignment for a possible application. Vector Machines should be used. SVM may be used to improve classification systems. Investigate big margin intuition, large margin classification, and the use of an SVM. The Decision Tree and its components, Nearest Neighbours (KNN), Navive Bayes, and many other algorithms.

Precision, recall, f1 score, Root mean score, r score, and their applications are examples of evaluation metrics. Components linked to natural language processing, such as Bag of words, and Components connected to image processing, such as RGB scale, grayscale, and so on. Machine Learning Applications on a Large Scale.

Dimensionality Reduction, Unsupervised Learning — expressing data as features and lowering the dimensions of the data to serve as input to various machine learning models. For each job, evaluate model quality in terms of important error measures. Recommender Systems, Anomaly Detection Using a Gaussian distribution, figure out which sets of data diverge considerably from the average set to discover anomalies. Examine the patterns amongst diverse system users and create recommender algorithms such as collaborative filtering and low-rank matrix factorisation.