Practical Data Science and Machine Learning

Course Overview

Data is the residue of every action that takes place in a company, with customers, and in the marketplace. It is created when customers buy products, users interact with services, and colleagues collaborate.

In an increasingly connected world, our ability to capture and leverage data has increased exponentially; but data in the wrong hands is useless, if not dangerous. In the right hands, data can drive new insights and powerfully informed decisions.

This course teaches the fundamentals of Machine Learning using hands-on coding exercises in Python. Basic Python is reviewed and taught as needed throughout the course so no prior Python experience is required though some basic experience with programming general is assumed.

5  days
    • Learn the concepts that are key to Machine Learning using Python exercises.
    • Query, and visualize data using open source tools: NumPy, Pandas, Matplotlib.
    • Discover the categories of machine learning and their business applications.
    • Understand the steps in the machine Learning process.
    • Create machine learning models using powerful algorithms.
    • Know the options for deploying models as part of larger systems.
    • See what is on the horizon for machine learning.
    • Techniques for deploying models as part of larger systems
  • Day 1


    • Introductions
    • ML Overview, What will be covered in this course
    • Distinguish AI/ML and Data Science
    • Brief History
    • S.E.M.N. framework


    • Hands On Intro to Kaggle
    • Interactive Python and Advanced Features Review
    • Your First Kaggle Project
    • Math We Will Use, and Introduction of Linear Algebra

    Day 2 – Process and Tools


    • Data Science Process Review
    • Data Manipulation Tools and Techniques
    • NumPy
    • PANDAS
    • MatPlotLib


    • Data Cleansing Exercise
    • Data Exploration Exercise
    • Individual Data Exploration Projects

    Day 3 – Supervised Learning Algorithms

    Morning – Supervised Learning Algorithms Pt 1

    • Linear Regression
    • Nearest Neighbor

    Afternoon – Supervised Learning Algorithms Pt 2

    • Decision Tree
    • Random Forest
    • Nearest Neighbor

    Day 4 Unsupervised Learning Algorithms


    • Dendrogram
    • Dimensionality Reduction
    • K-Means


    • SVD
    • PCA

    Day 5 – Deep Learning


    • Overview of Deep Learning
    • Anatomy of simple Neural Network
    • Hand on Neural Network Exercises – Perceptron


    • Deep Learning with Advanced Neural Network Architectures
    • Hand on project – Computer Vision
    • GANs (Generative adversarial networks)
    • Word2Vec
    • Next Steps
    • Final Q and A
  • Participants should have a working knowledge of Python and be familiar with core statistical concepts (variance, correlation, etc.).