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Machine Learning Course

Eight-week Online ML Bootcamp

The Data Incubator is a Cornell-funded data science training organization. We run an introductory 8-week part-time online program geared towards giving working professionals an immersive hands-on experience with Machine Learning. Developed based on feedback from our hundreds of industry partners and using the same rigorous methodology as our fellowship, the curriculum transforms ML amateurs into ML professionals.

Experienced teachers

Learn from experienced industry professionals dedicated to teaching data analytics.

Designed for working professionals

The flexible, part-time, online course is perfect for the working professional.

Jumpstart your career

Learn the most sought-after tools and techniques in the industry to help jumpstart your data analyst career.

Build a series of miniprojects

Gain hands-on experience applying the tools employers value to real-world datasets. All powered by a 100-node cluster.

Curriculum

Introduction to Machine Learning

In a world with abundant data, leveraging machines to learn valuable patterns from structured data can be extremely powerful. We explore the basics of machine learning, discussing concepts like regression, classification, model evaluation metrics, overfitting, variance versus bias, linear regression, ensemble methods, model selection, and hyperparameter optimization. Through powerful packages such as scikit-learn, students come away with a strong understanding of core concepts in machine learning as well as the ability to efficiently train and benchmark accurate predictive models. They gain hands-on experience building complex ETL pipelines to handle data in a variety of formats, developing models with tools like feature unions and pipelines to reduce duplicate work, and practicing tricks like parallelization to speed up prototyping and development.

Prerequisites:

Basic Python
Basic to intermediate statistics
Basic linear algebra

Course outline:

Bias, Variance, and Overfitting

Decision Trees In-sample error Out-of-sample error Variance-bias tradeoff Cross-validation strategies Grid search for tuning hyperparameters

Comparing ML Algorithms

Decision-making flowchart Ensemble methods Modeling for purposes other than predictive power

Decision Trees and Random Forests

Decision Trees Ensemble Methods Determining feature importance

Deep Leanring and TensorFlow

TensorFlow What is deep learning? The perceptron Multilayer Perceptron Example: Classifying handwritten digits

Digital Signals

Sampling Noise & Filters Audio files Filters Frequency domain

K Nearest Neighbors

Tuning k and other hyperparameters Normalizing features

Learning and Metrics

Supervised machine learning Metrics for regression Metrics for classification Metrics for probabilistic predictions Precision-recall tradeoff Feature importance Nonparametric vs. parametric analysis Asymptotic approximation property

Linear and Logistic Regression

Predictive modeling framework Linear Regression Regularization Lasso Regularization Combining signals with linear models Logistic Regression Multiclass classification problems Stochastic Gradient Descent Quantile Regression

Natural Language Processing

Text as a "bag of words" Word importance: Term frequency–inverse document frequency (TF-IDF) Document similarity metrics Engineering your features Building the Classifier Additional NLP topics and resources

Outlier Detection

Motivation Concepts Scikit-learn Implementation One-class SVM Isolation Forest Case Study: Anomaly Detection in Time Series Modeling the background Detecting seasonality with Fourier Transforms Detrending z-Score Moving-Window Averages Including windowed data in model Bayesian Changepoints Online Learning References

Recommendation Engine

Problem definition and data format Feature engineering Nearest Neighbors Tag Data Dimensional Reduction Recommendation for a User Cooperative Learning Regression of Ratings Baseline Model Overfitting and Cross-validation Modeling Interaction References

Scikit-learn Workflow

Writing custom estimators and transformers Pipelines Feature Unions Data Types Validating Your Implementations

Support Vector Machines (SVM)

Maximal Margin Classifier Linear SVM Non-linear SVM Multi-class SVM Approximating kernels Support Vector Regression Outlier detection using SVM

Time Series

Trends in time series data Cross-validation for time series Modeling drift Modeling seasonality Modeling "noise" Using external data sources as features More advanced time series modeling frameworks

Unbalanced Classes

Introduction: cancer detection case study Definition and common scenarios for unbalanced data Simple techniques to deal with unbalanced data Undersampling Oversampling Synthetic data augmentation Additional approaches Train/Test split with unbalanced data Probabilities with unbalanced data The python Unbalanced Data package

Unsupervised Learning

Metrics for clustering K-Means clustering Gaussian mixture models Dimension Reduction Random projections Matrix factorization Principal Components Analysis (PCA) Non-negative Matrix Factorization (NMF) Restricted Boltzman Machines

To get more information, including the dates and to gt a detailed syllabus, enter your information below. The course tuition is $3,495.00 with early-bird discounts available.

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Interested in the course but need help with financing? We've partnered with Climb Credit to help. Climb offers affordable, 3-year financing with fixed interest rates and low monthly payments. Apply online and get a decision same-day with no impact on your credit. Have questions about how it works? Check out their FAQ.

Curriculum built with feedback from over 250 industry partners:

Testimonials

"The data science skills I sharpened at The Data Incubator helped me analyze diversity in STEM education, model SaaS stock prices, and compare industry growth rates. The instructor's background in quantitative finance made for relevant use cases that benefit not only data scientists, but also data-driven finance people like myself."

Christian Templeton, Google

Formerly Salesforce, Symantec. Accepted to The Data Incubator Fellowship.

"From day one I was getting my hands dirty working with data using industry-relevant tools. Having completed the program I'm now better equipped to manage engineering and product teams, and able to conduct sophisticated ad-hoc analysis. Everyone at TDI blew me away; truly exceptional."

Michelle Miller, Hack Reactor

Formerly Berkeley, Smithsonian

"Their heavy focus on applied learning meant that I was working on real data and solving real problems right from the start. While lectures were a valuable component of their courses, the real learning took place while working on projects. These projects were practical, engaging, and instructive, and generally great opportunities to get hands-on experience with what I had learned in the lectures."

David Kwon, Yelp

Formerly University of California, Berkeley

With loads of data you will find relationships that aren’t real.
Big data isn’t about bits,
it’s about talent.

Forbes Magazine

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To get more information, including the dates and to gt a detailed syllabus, enter your information below. The course tuition is $3,495.00 with early-bird discounts available.

Learn More