thekemmie / 70-days-of-machine-learning

70 Days Pre-Bootcamp Online Class with Data Science Nigeria; Machine Learning Stream #DSN70daysofML

70 days of Machine Learning

This repository was made to keep track of my progress during Data Science Nigeria's 70 days of Machine Learning

• Day 1 - Introduction to Machine Learning
• Day 7 - How Linear Regression works
• Day 10 - R-Squared theory;
  Another name for R-squared error is Coefficient of Determination.
  Error is the distance between a point and a line of best fit.
  The error is squared to get a positive value of our error.
• Day 13 - Introduction to K Nearest Neighbors algorithm;
  'K' in the "K Nearest Neighbors" algorithm is a parameter that refers to the number of nearest neighbors to consider during voting process.
  K Nearest Neighbors algorithm is classified as supervised learning.
  Clustering is the process of dividing data points into a number of similar groups.
  
• Day 21 - Understanding Vectors;
  The magnitude of a vector is denoted with Bars.
  Learnt how to calculate the magnitude of the vector.
  
• Day 22 - Support Vector Assertion;
  Dot product is the relationship between the input and weight.
  If vector "u", dotted with vector "w + b" equals zero, it means that Vector u is on the decision boundary.
  If vector "u", dotted with vector "w + b" is greater or equal to zero, it means that the sample is of a class above the hyperplane.
  
• Day 23 - Support Vector Machine Fundamentals;
  A support vector is a feature set that if moved, affects the position of the best separating hyperplane.
  
• Day 24 - Support Vector Machine Optimization;
  Equation for hyperplane is X.W + b
  Support Vector Machines are less effective when the data is noisy and contains overlapping points.
  
• Day 29 & 30 - Introduction to Kernels;
  Kernels are done using inner product.
  Kernels take two inputs and outputs the similarities.
  Inner Product is a projection of x1 onto x2.
  Kernel is represented using the greek letter "phi".
  Transformation of the old and creation of new hyperplane helps SVM to perform better on non-linearly separable data.
  The default kernel for SVM using sckit-learn is Radio base function.
  
• Day 31 - Soft Margin SVM;
  For a more generalised model, the best kernel that represents the dataset has to be found.
  Soft Margin Classifier is a classifier with violating data in the separating hyperplane.
  Hard Margin Classifier is a classifier having perfectly separated data points in the decision hyperplane.
  In Soft margin there's a degree of error called Slack.
  The value of slack in SVM can best be represented with S>=0 .
  A slack value of Zero indicates a Hard Margin.
  Given that (SV(support Vectors) / Number of samples ) > 1, indicates; Overfitting, Non- linearly separable data and Wrong kernel.
  
• Day 33 - Support Vector Machine Parameters
  SVM is a binary classifier, so it can only seperate two groups per decision boundary; OVO - One versus One and OVR - One Versus Rest.
  I learnt about a lot of SVM Parameters.
  
• Day 43 - Introduction to Neural Networks.
  A neural network with two or more hidden layers.
  The layers of a neural network are; Input, Hidden and Output.
  An activation function serves as a threshold for determining output value.
  The idea of a Neural Network is dea is to mimic a neuron, and, with a basic neuron, you have the dendrites, a nucleus, axon, and terminal axon.
  
• Day 44 - Installation of TensorFlow.
  I wrote an article on it. Click here to read.
  
• Day 65 - Introduction to 3D Convolutional Neural Network
  

Done with 70 Days of ML:
Resources;

• Machine Learning Tutorial Series
• Text Version

+ #DSN70daysofML