Dive into the world of machine learning and predictive analytics with this comprehensive 2.5-hour tutorial series. Explore big data analytics stages, covering essential topics such as machine learning algorithms, supervised learning, data planning, cleaning, and visualization. Learn about models, the ID3 algorithm, decision trees, and data transformation techniques. Gain practical skills in handling outliers, missing values, and irregular cardinality. Understand key concepts like measures of central tendency, standard deviation, and data binning. Discover the differences between supervised and unsupervised algorithms, and explore data visualization techniques including bar plots and histograms. Perfect for aspiring data scientists, AI enthusiasts, or anyone looking to enhance their machine learning expertise in a self-paced learning environment.
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
