I don’t know, I am figuring this out as well.

Photo by Emily Morter on Unsplash


FYI, pretty simple stuff.

Photo by Gia Oris on Unsplash


“From the beginning to the end, losers lose, winners win” — 50 Cent


I promise it’s not just another “ML Article.”

Photo by Javier Allegue Barros on Unsplash

Terminology:

  1. Variance: It measures how far a data set is spread out.
  2. Mean: A calculated “central” value of a set of numbers.
  3. Standard Deviation: Similar to variance, but it is the root value of the variation.
  4. The coefficient of Variation (CV): A measure of relative variability. The ratio of the standard deviation to the mean. The coefficient of variation is useful when comparing two datasets. The larger the CV, the more spread the data is relative to its mean.

Trending AI Articles:

1. Deep Learning Book Notes, Chapter 1

2. Deep Learning Book Notes, Chapter 2

3. Machines Demonstrate Self-Awareness

4. Visual Music &…


I promise it’s not just another “ML Article.”

Terminology:

  1. Vectors: A vector is composed of a magnitude and direction. Geometrically, a vector in a 2-Dimensional plane (x and y graph) is a line from the origin to its coordinates. For example, if we have the coordinates (3,4), we can sketch a line from the origin to (3,4) which is three on the x-axis, and four on the y-axis.
  2. Magnitude: To calculate for magnitude, we have to find the length between the origin (0,0) and (3,4). Using the Pythagorean Theorem, we compute the square root of ³²+⁴² which is 5!
  3. Direction: For direction, we use the trigonometric functions: sin, cos…


I promise it’s not just another “ML Article.”

Terminology:

  1. Euclidean Distance: The distance between data “points” (p1, p2, …, pn). It computes the square root of the sum of the squares of the differences between the data “points.”
  2. Manhattan Distance: The distance between data “points” (p1, p2, …, pn). It computes the sum of the absolute differences between the data “points.”
  3. Chebyshev distance: Unlike the previous two methods, it calculates the maximum of the absolute differences between the data “points.”
  4. K: Or neighbors. It’s a core concept of the K-Nearest Neighbor. It determines how much values we are using in our model.

Concept:


I promise it’s not just another “ML Article.”

Terminology:

  1. Naive Bayes: The Naive Bayes Classifier technique derives from on the Bayesian theorem.
  2. Bayes Theorem: Bayes’ theorem is a mathematical equation used in probability and statistics to calculate the conditional probability. In other words, it is used to calculate the probability of an event based on its association with another event — Prof. Helmenstine.
  3. Conditional Independence: A great example from Wikipedia: “
    A and B are conditionally independent given C if and only if, given the knowledge that C occurs, knowledge of whether A occurs provides no information on the likelihood of B occurring, and knowledge of whether B occurs provides…


I promise it’s not just another “ML Article.”

Terminology:

  1. Supervised Learning: Analyzed the dataset to produce a predicted function which will be used for forecasting new examples.
  2. Ground Truth: The actual result.
  3. Cross-Entropy: “Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverge from the actual label.”

Concept:


I promise it’s not just another “ML Article.”

Terminology:

  1. Supervised Learning: Analyzed the dataset to produce a predicted function to forecast new examples.
  2. Overfitting: A model that has learned ‘too much’ of the dataset. Hence, the model will not be as useful on new examples.
  3. Ground Truth: The actual result.
  4. MSE: Mean Squared Error. It’s a formula that measures how well the model is performing. For each observation, it calculates the difference between the predicted and the ground truth. It then calculates the summation of the squares the difference. Lastly, it calculates the mean (divide by all the observations).
  5. Gradient Descent: A first-order iterative optimization algorithm for finding the…

Alex Guanga

Data Engineer @ Cherre. Mets die-hard. Hip-hop junkie.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store