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BRTC Makeover!

bumpyroadtocode Blog January 21, 2019January 21, 2019 0 Minutes

bumpy road to code [BRTC] gets a makeover, new color scheme and logo, yay!
Also, yes I have neglected my code-blog for a while, its true, I have been massively busy, but I believe I can do better in the future, I promise 🙂

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Published January 21, 2019January 21, 2019

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Instagram Grid: @artificialintelligence.digest

Machine Learning Model Multiple Linear Regression . :: Multiple Linear Regression is quite an exciting machine learning model. Instead of looking into one independent variable and one dependent variable, we can look into multiple independent variables, like in the example above experience x1 and age x2 to predict the dependent variable salary y. I chose to only use two independent variables because this allows me to represent the model in a three-dimensional space. Of course, Multiple Linear Regression gets more exciting with many more independent variables, especially looking into variable coefficients, which determines a weight for the variable, indicating how important one independent variable is or furthermore exploring ways of dimensionality reduction, once we have too many independent variables and we need to figure out which are the most impactful independent variables. . #artificialintelligence #machinelearning #ai #ml #data #multiplelinearregression #regression #dataprocessing #datascience #datascientist #datavisualization #statistics #analytics #dataanalytics

How Artificial Intelligence, Machine Learning and Deep Learning integrate. . :: A lot of people ask me, what is this good for and how is Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are related to one another. I will start a new series of images explaining this, in baby steps, starting from a big picture and slowly going into details. This is the first post of that series of exploring AI, ML and DL in more detail. Yes, this is already a lot of detail as a start, but somewhere one needs to start I figured. . :: So, this first post could make a point about how AI, ML and DL are not to be looked at as different things, in fact, they rely on each other. AI is using methods of ML and DL to mimic human behaviour, like speaking (cortana by @Microsoft), or seeing (self-driving cars by @Tesla and many more). ML is a method using statistics, probably the earliest form of ML were expert systems. Finally, DL is one specific technique of ML, to be precise, neural networks and even further, multi-layered neural networks. . #artificialintelligence #machinelearning #deeplearning #ai #ml #dl #data #dataprocessing #datascience #datascientist #datavisualization #statistics #analytics #dataanalytics

Machine Learning Model Dicision Tree Regression . :: There are two types of Decision Trees under the term CART, Classification Trees and Regression Trees. In the image above is a Regression Tree Model explained. Your data might be of two, three or any other dimension, actually the Decision Tree Regression is very powerful in higher dimension regression problems. The example shown has three dimensions, the experience variable x1 in years, the salary variable x2 in kilo amount and the according z variable for each data point {x1, x2}. How this works is, Step 1: to split the initial two dimensional data into so called leafs. Step 2: each leaf has now a collection of data points, the z value of those points is averaged for each leaf. Step 3: Introducing a new data point {x1, x2} and through Step 4, the decision tree we can now find out its corresponding z value from the leaf it lies in. . #artificialintelligence #machinelearning #ai #ml #data #decisiontreeregression #regression #dataprocessing #datascience #datascientist #decisiontrees #naivebayes #markov #neuralnetworks #geneticprograms #supportvectors #datavisualization #statistics #analytics #dataanalytics

The Five Tribes in Machine Learning Evolution . :: According to Pedro Domingos, there are five tribes in Machine Learning, looking into different understanding of logic and reasoning and methodology: Symbolists, Bayesians, Connectionists, Evolutionaries and Analogizers. Each of them have their strength and weaknesses. Do we need to combine them to go the next step? . :: Source: Pedro Domingos, The Master Algorithm, 2015 . #artificialintelligence #machinelearning #ai #ml #data #dataprocessing #datascience #datascientist #fivetribes #symbolists #bayesians #connectionists #evolutionaries #analogizers #decisiontrees #naivebayes #markov #neuralnetworks #geneticprograms #supportvectors #datavisualization #statistics #analytics #dataanalytics

Machine Learning Model Support Vector Regression (SVR) . :: Support Vector Regression was one of the ML models that took my a while to fully understand. Not to confuse with Support Vector Machines (SVM), which is used for classification, while SVR and SVM have the same underlying algorithm, they are indeed to be differentiated. . :: In a nutshell: SVR is just like Linear Regression (SVR can also be used for non-linear models), but with a loss-function, basically penalising data points which lie outside of a certain threshold, or the so called soft margin, represented by the area in light red. The optimisations objective is to maximise the y distance of every data point to the regression function, represented as the red line. . :: Now, what is it good for? Well, to explain a specific use-case would probably be a bit too much, but think of SVR as a Linear Rregression Model, but with tolerance. (Please let me know if anything is unclear, there are a bunch of papers and research articles about the topic, but on a very high level, not very friendly to people new to the field) . #artificialintelligence #machinelearning #ai #ml #data #dataprocessing #datascience #datascientist #supportvectorregression #linear #linearregression #regression #datavisualization #statistics #analytics #dataanalytics

Machine Learning (ML) divided into: . :: Supervised Learning: :: Classification + Regression :: and Clustering models :: and their corresponding algorithms and methods . #data #datanalytics #datascience #datascientist #ai #ml #artificialintelligence #machinelearning #deeplearning #neuralnetworks #supervisedlearning #unsupervisedlearning #clustering #classification #regression

Machine Learning Model Polynomial Linear Regression . :: Why Polinomial LINEAR Regression? Because the function y = b0 + b1x1 + b2x1^2 + bnx1^n is a linear combination of coefficients, represented by “b”, which are representing the unknown values. . :: The dataset consists of 15 entries represented as points, with two variables or columns. The Independend Variables X and the Dependend Variables y. :: Preprocessing the data by splitting the dataset into 80 % for the training dataset and 20 % for the test data set. In the case of 15 data entries that means a split into 12 and 3. :: After the Linear Regression is applied and fitted to my dataset, I can now retrieve a predicted Dependend Variable y by inputting a new Independend Variable X. . #artificialintelligence #machinelearning #ai #ml #data #dataprocessing #datascience #datascientist #linear #linearregression #regression #datavisualization #statistics #analytics #dataanalytics #polinomialregression

Neuron of a Neural Network . One of the most powerful machine learning models is the Neural Network (NN), instead of hard coding features, the NN figures them out itself. . :: " Neuron, also called nerve cell, basic cell of the nervous system in vertebrates and most invertebrates from the level of the cnidarians (e.g., corals, jellyfish) upward. A typical neuron has a cell body containing a nucleus and two or more long fibres. Impulses are carried along one or more of these fibres, called dendrites, to the cell body; in higher nervous systems, only one fibre, the axon, carries the impulse away from the cell body. Bundles of fibres from neurons are held together by connective tissue and form nerves. Some nerves in large vertebrates are several feet long. A sensory neurontransmits impulses from a receptor, such as those in the eye or ear, to a more central location in the nervous system, such as the spinal cord or brain. A motor neuron transmits impulses from a central area of the nervous system to an effector, such as a muscle." - Encyclopaedia Britannica @encyclopaedia_britannica . #data #machinelearning #artificialintelligence #ai #ml #nn #neuralnetworks #neuron #datascience #datascientist #dataprocessing #datamining #neurocomputing #bigdata #bigdataanalytics #analytics #analyst

Logistic Regression Classification . :: The Logistic Regression classifier is a simple Machine Learning Classification Model, the depended variable is a true or false, or a percentage. The independent variable could be something like age, experience, salary. You could predict if someone is doing something or not. The output or prediction really is binary. . #data #datascience #datascientist #datavisualization #ai #ml #machinelearning #artificialintelligence #logisticregression #classifier #classification #dataprocessing

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