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Applied Machine Learning Online 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 gives students the skills they need to harness the power of data.

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Experienced teachers

Learn from The Data Incubator's experienced data science instructors dedicated to teaching data analytics.

Jumpstart your career

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

Designed for working professionals

This course was designed for the busy lives of working professionals with a part-time schedule and recorded lectures.

Build a series of miniprojects

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

Live lectures

Live lectures allow you to ask your instructor questions and interact with your classmates.

Small class sizes

Small class sizes ensure you have plenty of access to your instructor and can receive personalized feedback on your progress.

Curriculum

Prerequisites:

Intermediate Python
Familiarity with linear algebra
Familiarity with statistical modeling

Target Audience:

This class is for you if:

You have taken our Data Science Foundations course and want to deepen your understanding of machine learning
You have experience with modeling or have a background in data science, and would like to learn the theoretical principles and practical applications of machine learning
You want to gain hands-on experience applying machine learning techniques to real-world data sets

Learning Outcomes:

Upon completion of this course, you will:

Understand and be able to deploy the steps required to complete an end-to-end machine learning project
Have learned about various machine learning models, how they work, pros and cons of each, and how they compare
Know how to best use machine learning to solve business problems and analyze problems using a machine learning perspective
Understand how to effectively use Python's machine learning library

Course Outline:

Introduction to Machine Learning

Module description Learning outcomes Prerequisites Suggested notebook order Additional resources

Introduction

Statistics vs Machine Learning Data as a Matrix Models as Functions Types of Machine Learning Parameters and Learning

Regression

Linear Regression Regression Metrics Optimization Stochastic Gradient Descent Adding Features Regularization Example: California Housing Data Set Reference: Statistical Motivation

Scikit-Learn API

Classes vs Objects Estimators Transformers Pipelines

Classification

Precision and Recall Other Classification Metrics Probabilistic Models Logistic Regression Multiclass classification problems

Bias, Variance, and Overfitting

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

Scikit-learn Workflow

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

Transformers and Preprocessing

Feature Scaling Encoding Categorical Variables Imputation Dimension Reduction Natural Language Processing Custom Transformers Answers to Questions

K Nearest Neighbors

Tuning k and other hyperparameters Normalizing features

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 Boltzmann Machines

Support Vector Machines (SVM)

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

Decision Trees and Random Forests

Decision Trees Ensemble Methods Determining feature importance

Overview of Machine Learning

Decision-making flowchart Comparing ML Algorithms Overfitting Scaling, Normalization, and Standardization Ensemble methods Modeling for purposes other than predictive power

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

Sentiment Analysis

Bag of Words Model Interpreting the Model Grammar and Other Tools

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

Naive Bayes

Predictive modeling using Naive Bayes Classifying mushrooms (an example)

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 Surpriselib References

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 imbalanced-learn package

Digital Signals

Sampling Noise & Filters Audio files Filters Frequency domain

Choosing the Correct Algorithm

Few Features Many Features Few Observations Many Observations Underfitting Overfitting Explicability Prediction Speed Parallelization Online Learning Feature Scaling

Advanced Machine Learning

Module description Learning outcomes Prerequisites Suggested notebooks Additional resources

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Sign up for the Applied Machine Learning Online Course

The next Applied Machine Learning Online Course will run from 2019-04-15 to 2019-06-05. Classes are generally held on Mondays and Wednesdays from 6:30-9:30 PM ET / 3:30-6:30 PM PT, with some exceptions for holidays. The deadline for registration is 2019-04-05. The course tuition is $3495 with early-bird discounts available.

The exact dates for the next session will be: 4/15, 4/17, 4/22, 4/24, 4/29, 5/1, 5/6, 5/8, 5/13, 5/15, 5/20, 5/22, 5/27, 5/29, 6/3, 6/5

Learn More

Interested in the course but need help with financing? We've partnered with Climb Credit to help. Climb offers 3-year financing with fixed interest rates and 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.

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TDI + MRINetwork

The Data Incubator recently teamed up with MRINetwork to increase its access to hiring partnerships worldwide. MRINetwork is comprised of over 1,500 search professionals who specialize in hundreds of industries, many of whom came from the industries in which they now recruit. The addition of MRINetwork, and its network of existing clients, will add thousands of hiring partners on top of TDI's existing 300+ hiring partnerships. As the need for data scientists has increased exponentially over the past few years, MRI provides TDI students with immediate access to new data science positions in geographies worldwide, as well as greater access to companies with a fundamental need for the data science talent required to harness the power of their data.

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."