Play all

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

Description:

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.

Machine Learning

Caleb Curry

Add to list

#Computer Science #Machine Learning #Data Science #Data Visualization #Supervised Learning #Business #Business Intelligence #Data Warehousing #Big Data #Hadoop #Data Analysis #Data Cleaning #Strategic Management #Business Analysis #Predictive Analytics #Algorithms #Big Data Analytics

0:00 / 0:00