Introduction to Machine Learning Course

Course Summary

The Introduction to Machine Learning training course is designed to demonstrate the fundamentals of Data Science and Machine Learning in Python.

The course begins with describing the Python tools such as NumPy, SciPy, Pandas, and Scikit-learn to gain an understanding of supervised vs. unsupervised learning, clustering, and feature engineering. Next, it explores the oft-used Machine Learning algorithms such as linear regression, logistic regression, decision trees, random forest, and k-nearest neighbors. The course concludes with exploring how to evaluate models and productize them.

Prerequisites: A working background of Python is expected.

Purpose

Learn various Machine Learning algorithms to evaluate and productize models.

Audience

Developer teams who are new to Machine Learning and would like to understand its power and potential.

Role

Data Engineer - Data Scientist - Software Developer - Technical Manager

Skill Level

Introduction

Style

Fast Track - Hack-a-thon - Learning Spikes - Workshops

Duration

2 Days

Related Technologies

Python

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Productivity Objectives:

Describe the basics of Data Science and Machine Learning
- Including techniques for Data Analytics, Feature Extraction/Engineering, Clustering, Regression, and Classification
Utilize Python Data Science/Machine Learning ecosystem–NumPy, SciPy, Pandas, and scikit-learn
Demonstrate how to “productize” Machine Learning models

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about our training

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
Expert Practitioners

Industry experts with 15+ years of industry experience that bring their battle scars into the classroom.
Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
Fully Customized

One-size-fits-all doesn't apply to training teams. That's where we come in!

What You'll Learn

In the Introduction to Machine Learning training course, you'll learn:

High Level Overview of Data Science and ML Concepts
- Supervised versus Unsupervised Learning
- Basic ML Vocabulary
- Tools and Libraries in Various Languages
- Machine Learning on Big Data / Scalable Machine Learning
Introducing Data Science and Data Analytics in Python
- NumPy and SciPy
- Pandas
- Data Exploration
Introducing scikit-learn Library in Python
- scikit-learn Capabilities
- scikit-learn Structure / Base Classes (Estimators / Transformers)
- Concepts
Feature Extraction and Feature Engineering
- Feature Selection
- Dealing with Categorical Variables
- Scaling and Normalization
- Dimensionality Reduction / PCA
Clustering and Unsupervised Learning
- Clustering Intro
- How k-Means Works
- k-Means in scikit-learn
- Interpreting Clusters
- Clustering and Outlier Detection for Fraud Detection
Supervised Learning: Regression and Classification
- Introducing Regression and Classification
- Survey of Regression and Classification Methods
- Fitting Linear Models
- Logistic Regression
- Decision Tree Learning
- Random Forests
- Evaluating Models: Accuracy, Precision/Recall/F1, ROC Curve
Recommendations
- User/Items Matrix
- Ratings: Implicit vs. Explicit
- Cluster-Based Recommendation
- k-Nearest-Neighbors (KNN) and Recommendations
- Similarity Matrices
- Matrix Decomposition: SVD vs. ALS
Productizing ML
Using Prediction.IO stack