You have now enough background knowledge about AI and ML. Now it's time to start your practical journey towards your goal to become an AI/ML expert: one of the highest-paid and most sought-after tech professionals on earth.
Google Colab. Easy working environment for Machine Learning professionals.
Python. Popular and easy-to-use programming language. Widely used in the Machine Learning community.
ML Workflow. Work like a ML professional. Right from the beginning.
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Logistic Regression is a mathematical model used in statistics to estimate (guess) the probability of an event occurring having been given some previous data.
Linear Discriminant Analysis (LDA) is a common dimensionality reduction technique used for supervised classification problems. LDA estimates the probability that a new set of inputs belongs to every class.
The K-Nearest Neighbors algorithm is a simple, supervised machine learning algorithm that can be used to solve both classification and regression problems. When a new situation occurs, it scans through all past experiences and looks up the closest experiences. Those experiences (or data points) are what we call the K-Nearest Neighbors.
A Classification and Regression Tree (CART) is a predictive algorithm used in machine learning. It explains how a target variable’s values can be predicted based on other values. The representation for the CART model is a binary tree.
In Gaussian Naive Bayes, continuous values associated with each feature are assumed to be distributed according to a Gaussian distribution. A Gaussian distribution is also called Normal distribution. When plotted, it gives a bell shaped curve which is symmetric about the mean of the feature values.
Support Vector Machines are a famous classification technique. It doesn't use any sort of probabilistic model like any other classifiers. Instead it generates hyperplanes or simply putting lines, to separate and classify the data into different regions.
A lot of words above you don't (yet) understand? Don't worry!
You can do this experiment without any prior knowledge about Python or Machine Learning. We will teach you everything you need to know, step-by-step. All the way from the start to the end. This is your gently introduction to the fascinating world of Machine Learning.
This exciting experiment should only take less than 1 hour to complete.