This online course on machine learning covers big data analytics stages, machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, and models. The course aims to teach the foundational concepts of machine learning and predictive analytics. The teaching method is self-paced tutorials. The intended audience includes those pursuing online degrees in computer science, data science, artificial intelligence, or anyone seeking to gain more experience in machine learning.
Overview
Syllabus
Machine Learning Tutorial 1 - Intro to Machine Learning and A.I..
Machine Learning Tutorial 2 - Intro to Predictive Data Analytics.
Machine Learning Tutorial 3 - Intro to Models.
Machine Learning Tutorial 4 - Generalization (Algorithms).
Machine Learning Tutorial 5 - Big Data, Data Warehouse, Hadoop, Federation.
Machine Learning Tutorial 6 - Analytical Base Table (ABT).
Machine Learning Tutorial 7 - Measures of Central Tendency.
Machine Learning Tutorial 8 - Standard Deviation.
Machine Learning Tutorial 9 - Continuous and Categorical Features (Cardinality).
Machine Learning Tutorial 10 - Binning Data.
Machine Learning Tutorial 11 - Cleaning Bad Data.
Machine Learning Tutorial 12 - Cleaning Missing Values (NULL).
Machine Learning Tutorial 13 - Imputation.
Machine Learning Tutorial 14 - Cleaning Irregular Cardinality.
Machine Learning Tutorial 15 - Outliers.
Machine Learning Tutorial 16 - Clamp Transformation.
Machine Learning Tutorial 17 - Using Models for New Data.
Machine Learning Tutorial 18 - Algorithms and Models.
Machine Learning Tutorial 19 - Supervised & Unsupervised Algorithms.
Machine Learning Tutorial 20 - Trees and Binary Trees.
Machine Learning Tutorial 21 - Decision Trees.
Machine Learning Tutorial 22 - Discriminatory Power.
Machine Learning Tutorial 23 - Recursion.
Machine Learning Tutorial 24 - Recursion Base Cases.
Machine Learning Tutorial 25 - Intro to the ID3 Algorithm.
Machine Learning Tutorial 26 - ID3 Algorithm Part 2.
Machine Learning Tutorial 27 - ID3 Algorithm Part 3.
Machine Learning Tutorial 28 - Bar Plots (Bar Graphs).
Machine Learning Tutorial 29 - Histograms.
Taught by
Caleb Curry
