Practical ML/AI Guide for Businesses

  Skin cancer is one of the most active types of cancer in the present decade.  As the skin is the body’s largest organ, the point of considering skin cancer as the most common type of cancer among humans is understandable . It is generally classified into two major categories: nonmelanoma (benign) and melanoma (malignant) skin cancer Melanoma type of cancers can only be cured if diagnosed early; otherwise, they spread to other body parts and lead to the victim’s painful death.  Therefore, the critical factor in skin cancer treatment is early diagnosis.  Yet, diagnoses is still a visual process , which relies on the long-winded procedure of clinical screenings, followed by dermoscopic analysis, and then a biopsy and finally a histopathological examination. This process  easily  takes months and the need for many medical professionals  and still  is only ~77% accurate. Current methods using AI and Deep Learning to diagnose lesions show  potential to spare time and mitigate errors- savi

  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