Machine Learning Bootcamp
Empower your team with state-of-the-art skills to discover hidden patterns in your data
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An innovative curriculum provides your team with state-of-the-art machine learning skills actually used in practice
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Build hands-on machine learning skills via in person classroom or live virtual experience
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Jumpstart your team's advanced analytics journey using Python - no previous Python experience required
Looking for individual live training? Check out Dave’s Machine Learning Jumpstart.
Answering the call for advanced analytics
Is machine learning shaping the future of your organization?
While data has always been used in business, things have changed. Functions like HR, Product Management, Customer Service, etc., are embracing advanced analytics to drive better business outcomes.
Do you want your team to be a part of this data-driven future?
It’s hard to avoid all the social media posts, magazine articles, or news clips trumpeting how machine learning is permanently changing the way organizations operate – and changing the expectations of organizations.
Machine learning for ANY team - regardless of role/background
Imagine a team of Product Managers that could answer the following question with data, "What feature usage(s) are highly predictive of a sticky customer?" How much value would they bring to their organization?
Training from a top-rated instructor
Machine Learning Bootcamp
The Machine Learning Bootcamp empowers your team with skills like random forests and k-means clustering to discover new insights.
If your team is new to Python, Dave’s free Python Crash Course will teach them everything they need to know before the bootcamp.
This training focuses on a practical subset of machine learning skills, so your team can hit the ground running and deliver insights ASAP.
The bootcamp is often bundled with additional courses (see below) to increase your team’s capabilities.
The outcome?
Your team will have the knowledge and hands-on skills to use machine learning to find hidden patterns in your data, including crafting predictive models and performing cluster analyses.
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A well-defined set of skills for real-world machine learning insights
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Your team will build real-world skills via 12 hands-on labs
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Courses can be taught using Python in Excel if that works better for your team
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Courses can be taught in-person or virtually. Choose what works best.
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Taught by Dave Langer, globally recognized data science instructor
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Bundle additional courses to expand your teams capabilities
What professionals have to say
Bring the same high-quality training experiences of national conferences to your team.
Training delivered in person or live virtual - whichever works best.
“Very good course as an intro to machine learning. I feel that with what I learned today I can put these skills into practice at work.”
— David Green, EMWD
“Fantastic intro ML course that’s presented in an engaging way. The content was easy to understand and the labs were easy to follow along. I’ve left the course wanting to dive deeper into the topic.”
— Jessica Liu, O-I Glass
“MIND BLOWN…not by the difficulty of the class, but by how EASY Dave makes machine learning within the reach of aspiring Data Scientists.
Easily the highlight of this year’s conference for me. I feel empowered to bring this material back to the job, put it to use, and teach it to others.”
— Chet Phelps, Health Solutions
“Best training and instructor I’ve had. Organized, clear, good pace, helpful examples, and an engaging and fun instructor.”
— Alex Kurtz, Sourceability
“I am so glad to have started the conference in Dave’s class. He set a wonderful tone for what is yet to come. I hope my other courses measure up!”
— Christina Mitchell, Naphcare
“Great class! Engaging instructor. Wish I would have had more time this week to attend his other sessions.”
— Matthew Royalt, Southern Star Central Gas Pipeline
Machine Learning Bootcamp Curriculum
3 days. 12 hands-on labs.
Bootcamp can be taught with Python in Excel.
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What is Machine Learning?
Data Analyst, Teacher
Why Decision Trees?
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Course Datasets
Exploratory Data Analysis (EDA)
Hands-on Lab #1
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Classification Tree Intuition
Overfitting Intuition
Gini Impurity
Split Quality
Splitting Categorical Data
Splitting Numeric Data
Classification Trees with Python
Hands-On Lab #2
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Under/Overfitting
The Bias-Variance Tradeoff
Supervising the Data
Model Tuning
Classification Tree Pruning
Measuring Awesomeness
Splitting the Dataset
Modeling Tuning with Python
Hands-On Lab #3
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Feature Engineering Intuition
Data Leakage
Decision Boundaries
Engineering Numeric Features
Engineering Categorical Features
Engineering Date-Time Features
Missing Data
Hands-On Lab #4
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Regression Tree Fundamentals
Numeric Feature MSE
Categorical Feature MSE
Feature Evaluation
Tuning Regression Trees
Imputation with Regression Trees
Hands-On Lab #5
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Bad, Tree! Bad!
Ensembles
Bagging
Feature Randomization
Random Forests with Python
Hands-On Lab #6
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Feature Importance
Tuning Random Forests
Model Testing
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Additional Resources
Wanna Kaggle?
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The following is the 3-day curriculum. The curriculum can be expanded by bundling additional courses (see below).
Dave’s free Python Crash Course is available for teams new to Python.
Introduction to Machine Learning - Days 1 & 2
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Course Expectations
What is Cluster Analysis?
Cluster Analysis Use Cases
The Challenge of Clustering Data
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The Iris Dataset
The Hand-Written Digits Dataset
The Heart Dataset
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Hierarchical, Partitional, and Overlapping Clustering
Prototype Clusters
Density-Based Clusters
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Introducing K-Means
The K-Means Algorithm
Euclidian Distance
The Problem with Outliers
Data Standardization
K-Means Caveats
Hands-On Lab #1
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Evaluating Clusters
Cluster Cohesion
Evaluating Cohesion with the Elbow Method
The Silhouette Coefficient
Evaluating Clusters using the Silhouette Score
Hands-on Lab #2
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Introducing DBSCAN
The DBSCAN Algorithm
DBSCAN Caveats
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Considerations for Optimizing DBSCAN
Calculating min_samples
Choosing the eps Value
Introducing Nearest Neighbors
Evaluating eps with the Elbow Method
DBSCAN vs K-Means
Hands-On Lab #3
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Introducing Dimensionality Reduction
Principal Component Analysis (PCA)
PCA Concepts
Hands-On Lab #4
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The Problem with Categories
Encoding Categorical Data
Factor Analysis of Mixed Data (FAMD)
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Supervised Learning Resources
Cluster Analysis Resources
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Cluster Analysis - Day 3
Course Add-Ons
Expand your team’s capabilities by bundling additional courses into your bootcamp.
All courses can be taught using Python in Excel.
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Visual Data Analysis
This 1-day hands-on course teaches how to use data visualizations the way Data Analysts/Scientists do - to get to the “why” of what’s happening. This course focuses on topics useful to any team, including Distribution Analysis, Correlation Analysis, Multivariate Analysis, and Time Series Analysis..
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Data Wrangling for Machine Learning
This 1-day hands-on course focuses on techniques for producing the best quality data for use in crafting valuable machine learning models. Topics include data profiling, wrangling string data, and engineering date-time features. This course expands upon the topics covered in the Introduction to Machine Learning course.
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Text Analytics
This 1-day hands-on course is an introduction to the tools an techniques of transforming text data into a form suitable for analytics. Examples include clustering documents and sentiment analysis. Topics include tokenization, stemming, lemmatization, TF-IDF, and cosine similarity.
FAQs
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Yes! The bootcamp can be delivered virtually or in-person with your team.
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While the courses do include mathematics, it is at a level accessible to a broad audience. For example, no knowledge of calculus or statistics is required.
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All the courses use Python as the programming language. Dave's free Python Crash Course is available for teams new to Python.
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Everything taught in the bootcamp is 100% compatible with Python in Excel. Courses can be taught using Python in Excel for the hands-on labs.
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The Introduction to Machine Learning online course is available and covers the first two days of the bootcamp.
At this time, there is no online course for cluster analysis.