Introduction to Machine Learning


Course Number: PYTH-200

Duration: 3 days (19.5 hours)

Format: Live, hands-on

Machine Learning Training Overview

Accelebrate's Introduction to Machine Learning training class teaches experienced developers, architects, and team leaders the foundations of building machine learning solutions. Starting with data handling, going through data exploration, and finishing with an algorithms overview, this training provides students with the knowledge of modern tools and the appropriate way of thinking that are crucial for being successful with machine learning.

Note: we offer a free 1-hr pre-recorded Machine Learning with Python Webinar.

Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

Objectives

  • Understand what is Machine Learning and which problems does it tackle.
  • Understand the Data Science pipeline.
  • Become familiar with the various tools in the Python ecosystem to handle data (clean and transform).
  • Understand the basics of descriptive statistics and probability for data exploration.
  • Using visualization libraries for gaining insight into the data.
  • Become familiar with using the wide variety of machine-learning algorithms.

Prerequisites

No prior knowledge of machine learning, math or data science is required. However, all attendees should know the fundamentals of the Python programming language and feel confident with its syntax.

Outline

Expand All | Collapse All

Introduction
The Concepts
  • What is Data Science?
  • The Role of Machine Learning
  • Use-cases
  • Quality of Data
Data Handling
  • NumPy Essentials
  • Introduction to Pandas
  • Working with DataFrames
  • Filtering
  • Vectorized Operations
Descriptive Statistics
  • Measures of Center
  • Measures of Dispersity
  • Correlations
  • Z-Test
  • Pandas and Descriptive Statistics
Visualization
  • Introduction to matplotlib
  • Creating Charts
  • The Seaborn Library
  • Complex Figures
Data Science
  • Introduction to Data Exploration
  • Scaling
  • Feature Selection
  • Feature Engineering
Algorithms Overview
  • Introduction to SKLearn
  • Supervised Learning
  • Regression vs. Classification
  • Overfitting vs. Underfitting
  • Algorithms
    • Linear Regression
    • Logistic Regression
    • Decision Tree (Bagging/RandomForest/GradientBoost)
    • K-Nearest Neighbors
    • Support Vector Machines (SVC/SVR)
  • Introduction to Unsupervised Learning
    • PCA
    • K-Means
Testing and Scoring
  • Overview of Scoring Methods
  • Cross-Validation
Conclusion

Training Materials:

All Machine Learning training students receive comprehensive courseware.

Software Requirements:

  • Windows, Mac, or Linux with at least 8 GB RAM
  • Anaconda for Python 3.x
  • Related lab files that Accelebrate will provide


Learn faster

Our live, instructor-led lectures are far more effective than pre-recorded classes

Satisfaction guarantee

If your team is not 100% satisfied with your training, we do what's necessary to make it right

Learn online from anywhere

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Multiple Payment Options

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