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Machine Learning Foundation is a hands-on introduction to the mathematics and algorithms used in Data Science, as well as creating the foundation and building the intuition necessary for solving complex machine learning problems.

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Unlimited Duration

Last Updated

March 3, 2021

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This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. In this course you will learn about:

· Popular machine learning algorithms, their applicability and limitations

· Practical application of these methods in a machine learning environment

· Practical use cases and limitations of algorithms

· Core machine learning mathematics and statistics

· Supervised Learning vs. Unsupervised Learning

· Classification Algorithms including Support Vector Machines, Discriminant Analysis, Naïve Bayes, and Nearest Neighbor

· Regression Algorithms including Linear and Logistic Regression, Generalized Linear Modeling, Support Vector Regression, Decision Trees, k-Nearest Neighbors (KNN)

· Clustering Algorithms including k-Means, Fuzzy clustering, Gaussian Mixture

· Neural Networks including Hidden Markov (HMM), Recurrent (RNN) and Long-Short Term Memory (LSTM)

· Dimensionality Reduction, Single Value Decomposition (SVD), Principle Component Analysis (PCA)

· How to choose an algorithm for a given problem

· How to choose parameters and activation functions

· Ensemble methods

Course Curriculum

    • Statistics Overview and Review 00:00:00
    • Mean, Median, Variance, and deviation 00:00:00
    • Normal / Gaussian Distribution 00:00:00
    • Probability Theory 00:00:00
    • Discrete Probability Distributions 00:00:00
    • Continuous Probability Distributions 00:00:00
    • Measure-Theoretic Probability Theory 00:00:00
    • Central Limit and Normal Distribution 00:00:00
    • Probability Density Function 00:00:00
    • Probability in Machine Learning 00:00:00
    • Supervised Learning Explained 00:00:00
    • Classification vs. Regression 00:00:00
    • Examples of Supervised Learning 00:00:00
    • Key supervised algorithms 00:00:00
    • Unsupervised Learning 00:00:00
    • Clustering 00:00:00
    • Examples of Unsupervised Learning 00:00:00
    • Key unsupervised algorithms (overview) 00:00:00
    • Linear Regression 00:00:00
    • Logistic Regression 00:00:00
    • Support Vector Regression 00:00:00
    • Decision Trees 00:00:00
    • Random Forests 00:00:00
    • Bayes Theorem and the Naïve Bayes classifier 00:00:00
    • Support Vector Machines 00:00:00
    • Discriminant Analysis 00:00:00
    • k-Nearest Neighbor (KNN) 00:00:00
    • k-Means Clustering 00:00:00
    • Fuzzy Clustering 00:00:00
    • Gaussian Mixture Models 00:00:00
    • Neural Network Basics 00:00:00
    • Hidden Markov Models (HMM) 00:00:00
    • Recurrent Neural Networks (RNN) 00:00:00
    • Long-Short Term Memory Networks (LSTM) 00:00:00
    • Choosing between Supervised and Unsupervised algorithms 00:00:00
    • Choosing between Classification Algorithms 00:00:00
    • Choosing between Regressions 00:00:00
    • Choosing Neural Networks 00:00:00
    • Choosing Activation Functions 00:00:00
    • Ensemble Theory and Methods 00:00:00
    • Ensemble Classifiers 00:00:00
    • Bucket of Models 00:00:00
    • Boosting 00:00:00
    • Stacking 00:00:00
    • Machine Learning in Python: NumPy, Pandas, SciKit-ML, and MatPlotLIb; NLTK, Keras 00:00:00
    • Machine Learning in R 00:00:00
    • Machine Learning in Java 00:00:00
    • Machine Learning with Apache Madlib 00:00:00
    • Hadoop, MapReduce, and Mahout 00:00:00
    • Spark and MLLib 00:00:00
    • TensorFlow 00:00:00

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