MACHINE LEARNING

machine

MACHINE LEARNING

This foundation-level hands-on course explores core skills and concepts in machine learning practices. You’ll learn machine learning concepts and algorithms from scratch. This includes the foundations, applicability and limitations, and an exploration of implementation and use.

WHAT YOU’LL LEARN

Join an engaging hands-on learning environment, where you’ll learn:

Popular machine learning algorithms, their applicability and limitations
Practical application of these methods in a machine learning environment
Practical algorithm use cases and limitations

This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions, labs, and project work.

OUTLINE

Virtual Classroom Live Outline

Machine Learning (ML) Overview

Machine Learning landscape
Machine Learning applications
Understanding ML algorithms and models (supervised and unsupervised)

Machine Learning Environment

Introduction to Jupyter notebooks/R-Studio

Machine Learning Concepts

Statistics Primer
Covariance, Correlation, and Covariance Matrix
Errors, Residuals
Overfitting/Underfitting
Cross validation and bootstrapping
Confusion Matrix
ROC curve and Area Under Curve (AUC)

Feature Engineering (FE)

Preparing data for ML
Extracting features and enhancing data
Data cleanup
Visualizing Data
Exercise: data cleanup
Exercise: visualizing data
Linear regression
Simple Linear Regression
Multiple Linear Regression
Running LR
Evaluating LR model performance

Logistic Regression

Understanding Logistic Regression
Calculating Logistic Regression
Evaluating model performance

Classification: SVM (Supervised Vector Machines)

SVM concepts and theory
SVM with kernel

Classification: Decision Trees and Random Forests

Theory behind trees
Classification and Regression Trees (CART)
Random Forest concepts

Classification: Naive Bayes

Theory behind Naive Bayes
Running NB algorithm
Evaluating NB model

Clustering (K-Means)

Theory behind K-Means
Running K-Means algorithm
Estimating the performance

Principal Component Analysis (PCA)

Understanding PCA concepts
PCA applications
Running a PCA algorithm
Evaluating results

Recommendation (collaborative filtering)

Recommender systems overview
Collaborative Filtering concepts

Time Permitting: Capstone Project

Hands-on guided workshop utilizing skills learned throughout the course

PREREQUISITES

Before attending this course, you should have:

Basic Python skills
Good foundational mathematics in linear algebra and probability
Basic Linux skills
Familiarity with command line options such as ls, cd, cp, and su

This course is for intermediate skilled professional. This is not a basic class.

WHO SHOULD ATTEND

Experienced Developers, Data Analysts, and others interested in learning about machine learning algorithms and core concepts leveraging Python. This course is also offered in R or Scala – please inquire for details.