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Introduction to Machine Learning

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.

Learn various Machine Learning algorithms to evaluate and productize models.
Developer teams who are new to Machine Learning and would like to understand its power and potential.
Data Engineer - Data Scientist - Software Developer - Technical Manager
Skill Level
Fast Track - Hack-a-thon - Learning Spikes - Workshops
2 Days
Related Technologies


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

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
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”


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