Practical ML/AI Guide for Businesses

  (the image was created using Visme [1]). Introduction Stock markets are analyzed either technically or fundamentally [2]. Fundamental analysis studies supply and demand relationships that define the stock price at any given time.  Technical analysis uses specialized methods of predicting prices by analyzing past price patterns and levels.  T here are many techniques used to examine stock price lines and patterns [2]: bar or high/low/close charts moving averages trend lines channels cycles resistance and support planes corrections double tops and bottoms head and shoulders formation trading volume open interest.  Theere are numerous limitations of these techniques: moving averages responds to general trends only is not highly precise short-term moving averages can give false indications, especially in times of volatile prices Trend lines work best with sustained trends positioning of trend lines is subjective and takes practice trends must be established before they become recognizabl

  This is the application of supervised machine learning to real estate. The goal is to predict sale prices ($) for N selected properties in a state (N>>1000).  We are given a csv dataset as a NxM table, where M is the number of property features describing every aspect of the house and surroundings (typically, M<100).    The dataset is partitioned into the training, test and deployment subsets as 70:20:10%. The data preparation phase (Step 0) also includes data cleaning and editing to remove outliers, missing values, etc. Step 1 (regression) performs mean fitting of a continuous surface such as plane F(data;A) to the training data by updating the fitting parameters A.  The fitting parameter set that provides the minimum average error between predicted and input house prices is stored in an array A. Step 2 tests the solution A (current best output of Step 1) by computing the mean difference or error between actual and predicted house prices within the test dataset. The large

  What is ML? Machine Learning (ML) is defined as follows: A code learns from experience E with respect to a task T and a performance measure P, if its performance on T, as measured by P, improves with E. [1].   Example 1: your code monitors spam and classifies emails as spam or not spam (SNS). In this case, T = classifying emails as SNS; E = watching you label emails as SNS; P = the fraction of emails correctly classified as SNS. Example 2: playing checkers. E = the experience of playing many games of checkers; T = the task of playing checkers; P = the probability that the program will win the next game. ML is a part of Artificial Intelligence (AI). ML algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so [2]. ML is an important subset of data science. Through the use of statistical methods, data science algorithms are trained to make classifications or predictions, uncove