Machine Learning MASTERCLASS - course for beginners

Provide an accessible overview of machine learning for beginners, outlining core concepts and how to start learning.

Explore the definition of machine learning, including Tom Mitchell's view that programs learn from experience to improve at tasks, and learn from data to predict unseen values.

Learn to define a dataset and organize data in rows and columns like a database table, noting that dataset size and quality affect machine learning model accuracy.

Explore how machine learning powers recommendations and feeds across Netflix, YouTube, Spotify, Google, and social feeds like Facebook and Twitter, including speakers from Google and Amazon.

Explore supervised, unsupervised, and reinforcement learning, where supervised uses input-output pairs to derive rules, unsupervised finds input patterns, and reinforcement learns through interaction with an environment.

Develop mathematical knowledge across linear algebra, probability and statistics, and optimization. Advance in calculation algorithms to learn machine learning.

Explore supervised learning where training data come with correct labels and classes are predefined. Practice with algorithms such as logistic regression and support vector machines.

Explore logistic regression as a supervised classification method, estimating probabilities with a logistic function to assign binary outcomes from independent variables, including binomial logistic regression for binary targets.

Train the algorithm to recognize categories from a labeled dataset using input features (descriptive variables) and a class label, and distinguish binary from multiclass classification.

Learn how support vector machines build a linear binary classifier by mapping data into space and maximizing the margin between two classes to predict new instances.

Explore unsupervised learning as a core approach in artificial intelligence where computers identify processes and patterns without human input. See examples such as k means clustering and principal component analysis.

Partition a dataset of attribute vectors into groups using k-means clustering, a variant of the expectation maximization method for Gaussian data, by initializing partitions, computing centroids, and iterating until convergence.

Explore neural networks in machine learning, where neurons with values between zero and one connect through weights across layers to recognize data and compute probabilities.

Python serves as a primary language for machine learning, with libraries that simplify machine learning programming, vector and matrix operations, data handling, and models for classification, clustering, and neural networks.