• No products in the cart.

ratings 

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.

PRIVATE
Course Access

Unlimited Duration

Last Updated

March 3, 2021

Students Enrolled

Total Reviews

Posted by
Certification

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

Course Reviews

Profile Photo
ashar hafeez
0
62

Students

About Instructor

Pak

Course Events

[wplms_eventon_events]

More Courses by Insturctor

© 2021 Ernesto.  All rights reserved.  
X