Intro to Machine Learning

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Machine Learning

What is Machine Learning?

Machine learning is a branch of artificial intelligence (AI) that focuses on creating applications that learn from data and improve their accuracy over time, without the need for programming. In data science, an algorithm is a series of statistical processing steps. In machine learning, algorithms are "trained" to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions, because it processes more data. Today, examples of machine learning are everywhere around us. The digital assistant will search the Internet and play music based on our voice commands. The website recommends products, movies and songs based on what we have previously purchased, watched or heard. When we do, the robot will vacuum our floor. .. Our time has better things. The spam detector prevents spam from reaching our inbox. Medical imaging systems can help doctors detect tumors they may have missed. The first self-driving car is on the road.

Why to learn in strydo?

Enhance your skill set and boost your hirability through innovative, independent learning. Accelerate your career with the credential that fast-tracks you to job success. Ideal for those beginning a career in data science. Master the tools and methods of data analysts. Ideal for those searching for a general purpose programming language. Learn a programming language designed with data science in mind


Intro To Machine Learning

Principal Component Analysis

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– Why learn it.
– Geometric intuition.
– Mathematical objective function.
– Alternative formulation of PCA: distance minimization
– Eigen values and eigenvectors.

Classification and Regression Models

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– How “Classification” works?
– Data matrix notation.
– Classification vs Regression (examples)
– K-Nearest Neighbors Geometric intuition with a toy example.
– Failure cases.
– Distance measures: Euclidean(L2) , Manhattan(L1), Minkowski, Hamming
– Cosine Distance & Cosine Similarity

Classification algorithms in various situations

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– Introduction
– Imbalanced vs balanced datasets.
– Multi-class classification.
– k-NN, given a distance or similarity matrix.
– Train and test set differences.
– Impact of Outliers

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