machine learning using python
#12 association analysis Machine Learning ♡ supervised learning 👉 ♡ unsupervised learning ♡ reinforcement learning unsupervised learning is where your program has to find how the data relates to each other. there is no prior training types of unsupervised learning ♠️ clustering ♠️ association association analysis finds associations between items, like what customers usually buy …
#11 unsupervised learning Machine Learning ♡ supervised learning 👉 ♡ unsupervised learning ♡ reinforcement learning unsupervised learning is where your program has to find how the data relates to each other. there is no prior training types of unsupervised learning ♠️ clustering ♠️ association 🎲 clustering the k means (the distance-based) algorithm is used to …
#10 naive bayes Machine Learning 👉♡ supervised learning ♡ unsupervised learning ♡ reinforcement learning recap: 🔖 types of supervised learning ✅ classification 📑 ✅ regression 📈 ✅ mixed ⚗ naive bayes classification ⚱ bayes theorem bayes theorem states that P(B|A) = (P(A|B) * P(B)) / P(A) Probability of B given A = … 🔎 naive …
#9 neural networks Machine Learning 👉♡ supervised learning ♡ unsupervised learning ♡ reinforcement learning recap: 🔖 types of supervised learning ✅ classification 📑 ✅ regression 📈 ✅ mixed ⚗ – tree based – random forest – neural networks 🎈 – support vector machines neural networks neural networks require a bit of explanation. entire books can …
#8 support vector machines Machine Learning 👉♡ supervised learning ♡ unsupervised learning ♡ reinforcement learning recap: 🔖 types of supervised learning ✅ classification 📑 ✅ regression 📈 ✅ mixed ⚗ – tree based – random forest – neural networks – support vector machines 🎈 support vector machines (svm) 🔎 support vectors : read on to …
Machine Learning Part 8: Support Vector Machines Read More »
#7 random forest Machine Learning 👉 ♡ supervised learning♡ unsupervised learning♡ reinforcement learning recap:🔖 types of supervised learning ✔ classification 📑 ✔ regression 📈 ✔ mixed ⚗– tree based – random forest 🎈– neural networks– support vector machines 🌳 overfitting and the problem with trees trees classify by drawing square boxes around the data, which …
Machine Learning part 7: Random Forests Explained Read More »
Machine Learning 👉 ♡ supervised learning ♡ unsupervised learning ♡ reinforcement learning recap: 🔖 types of supervised learning ✔ classification 📑 ✔ regression 📈 ✔ mixed ⚗ tree based :balloon: random forest neural networks support vector machines 🎗 enthropy enthropy is just another word for expected value in the past post, we decided what to …
#5 mixed methods Machine Learning 👉 ♡ supervised learning♡ unsupervised learning♡ reinforcement learning types of supervised learning ✔ classification 🗒 ✔ regression 📈 ✔ mixed ⚗– tree based– random forest– neural networks– support vector machines mixed methods are used for classification and regression. 🌱 tree based method those trees used for both for classification and …
#4 gradient descent and cost function Machine Learning 👉 ♡ supervised learning♡ unsupervised learning♡ reinforced learning ☄ cost function cost function is also called mean squared error. well mean means sum of elements / number of elements. here we take the sum of all squared errors (error1 ^ 2 + error2 ^ 2 + error3 …
Machine Learning part 4: Gradient Descent and cost function Read More »
Machine Learning ♡ supervised learning♡ unsupervised learning♡ reinforcement learning #2 supervised learning in supervised learning we have labelled data available. the machine just see and do as we do. types of supervised learning ✅ classification 🔖 :– logistic regression– supervised clustering ✅ regression 📈 – linear regression (single value)– multivariate linear regression ✅ mixed ⚗– …
what is it? wikipaedia defines machine learning as: _the scientific study of algorithms and statistical models that computer systems use to progressively improve their performance 📈 on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed …
if you want to calculate the distance between 4 features coordinates, you do it like that : A (1,2,5,1) B (7, 1, 3, 0) import math dist = math.sqrt((7-1)**2+(1-2)**2+(3-5)**2+(0-1)**2) print(dist) >>> 6.4807 …
machine learning is becoming crucial to add to your backpack. this post attempts to show a basic application of machine learning. we need to determine whether some cutlery items are bowls or mugs or plates. this is a practical demonstration of applied theory machine learning is just applied maths from start to finish, machine learning …
home machine learning project : identifying cutlery items Read More »
