Machine Learning Literacy

Paths

Machine Learning Literacy

Author: Janani Ravi

Machine Learning is the application of algorithms and mathematical models by software system to progressively improve their performance on a specific task. This skill covers the... Read more

What you will learn:

  • The machine learning workflow (data sourcing -> data cleaning -> data preparing -> data modeling and training -> model evaluation -> deployment -> monitoring & maintenance )
  • Commonly employed data models
  • Common techniques employed in machine learning (reinforcement learning, model validation strategies, etc)

Pre-requisites

  • Data Literacy
  • Data Analytics Literacy
  • Statistics

Beginner

Learn how feature engineering fits into the machine learning workflow, and build your first features from numerical data

Preparing Data for Machine Learning

by Janani Ravi

Oct 29, 2019 / 3h 24m

3h 24m

Start Course
Description

As Machine Learning explodes in popularity, it is becoming ever more important to know precisely how to prepare the data going into the model in a manner appropriate to the problem we are trying to solve. In this course, Preparing Data for Machine Learning* you will gain the ability to explore, clean, and structure your data in ways that get the best out of your machine learning model. First, you will learn why data cleaning and data preparation are so important, and how missing data, outliers, and other data-related problems can be solved. Next, you will discover how models that read too much into data suffer from a problem called overfitting, in which models perform well under test conditions but struggle in live deployments. You will also understand how models that are trained with insufficient or unrepresentative data suffer from a different set of problems, and how these problems can be mitigated. Finally, you will round out your knowledge by applying different methods for feature selection, dealing with missing data using imputation, and building your models using the most relevant features. When you’re finished with this course, you will have the skills and knowledge to identify the right data procedures for data cleaning and data preparation to set your model up for success.

Table of contents
  1. Course Overview
  2. Understanding the Need for Data Preparation
  3. Implementing Data Cleaning and Transformation
  4. Transforming Continuous and Categorical Data
  5. Understanding Feature Selection
  6. Implementing Feature Selection

Intermediate

Transform nominal data, such as names or categories, into features appropriate for machine learning, and apply techniques for simplifying large data sets

Designing a Machine Learning Model

by Janani Ravi

Aug 13, 2019 / 3h 25m

3h 25m

Start Course
Description

As Machine Learning explodes in popularity, it is becoming ever more important to know precisely how to frame a machine learning model in a manner appropriate to the problem we are trying to solve, and the data that we have available. In this course, Designing a Machine Learning Model you will gain the ability to appropriately frame your use-case and then choose the right solution technique to model it. First, you will learn how rule-based systems and ML systems differ and how traditional and deep learning models work. Next, you will discover how supervised, unsupervised, and reinforcement learning techniques differ from each other. You will learn how classic supervised learning techniques such as regression and classification complement classic unsupervised techniques such as clustering and dimensionality reduction. You will then understand the assumptions and outcomes of these four classes of techniques and how solutions can be evaluated. Finally, you will round out your knowledge by designing end-to-end ML workflows for canonical ML problems, ensemble learning, and neural networks. When you’re finished with this course, you will have the skills and knowledge to identify the correct machine learning problem setup, and the appropriate solution technique for your use-case.

Table of contents
  1. Course Overview
  2. Exploring Approaches to Machine Learning
  3. Choosing the Right Machine Learning Problem
  4. Choosing the Right Machine Learning Solution
  5. Building Simple Machine Learning Solutions
  6. Designing Machine Learning Workflows
  7. Building Ensemble Solutions and Neural Network Solutions

Creating Machine Learning Models

by Janani Ravi

Oct 29, 2019 / 2h 44m

2h 44m

Start Course
Description

As Machine Learning explodes in popularity, it is becoming ever more important to know precisely how to frame a machine learning model in a manner appropriate to the problem we are trying to solve, and the data that we have available. In this course, Creating Machine Learning Models you will gain the ability to choose the right type of model for your problem, then build that model, and evaluate its performance. First, you will learn how rule-based and ML-based systems differ and their strengths and weaknesses and how supervised and unsupervised learning models differ from each other. Next, you will discover how to implement a range of techniques to solve the supervised learning problems of classification and regression. You will gain an intuitive understanding of the the model algorithms you can use for classification and regression. Finally, you will round out your knowledge by building clustering models using a couple of different algorithms, and validating the results. When you’re finished with this course, you will have the skills and knowledge to identify the correct machine learning problem setup, and the appropriate solution and evaluation techniques for your use-case.

Table of contents
  1. Course Overview
  2. Understanding Approaches to Machine Learning
  3. Understanding and Implementing Regression Models
  4. Understanding and Implementing Classification Models
  5. Understanding and Implementing Clustering Model

Advanced

Extract features from text documents and images

Deploying Machine Learning Solutions

by Janani Ravi

Oct 30, 2019 / 3h 4m

3h 4m

Start Course
Description

Machine Learning is exploding in popularity, but serious early warning signs are emerging around the performance of ML models in production. In this course, Deploying Machine Learning Solutions you will gain the ability to identify reasons why models might be under-performing in production after doing just fine in training and testing, and ways to mitigate this worrying phenomenon. First, you will learn how training-serving skew, concept drift, and overfitting are different causes of model underperformance, and how they can be mitigated by post-deployment monitoring. Next, you will discover how ML models can be deployed, that is made available on HTTP endpoints, using Flask, the popular Python web-serving framework. You will also see how you can deploy models to serverless environments such as Google Cloud Functions Finally, you will work with platform-specific machine learning services such as Google AI Platform and Amazon SageMaker for model deployment. When you’re finished with this course, you will have the skills and knowledge to identify issues with models that have been deployed but are not performing to expectations, as well as how to implement deployment using both on-prem and cloud infrastructure.

Table of contents
  1. Course Overview
  2. Understanding Factors that Impact Deployed Models
  3. Deploying Machine Learning Models to Flask
  4. Deploying Machine Learning Models to Serverless Cloud Environments
  5. Deploying Machine Learning Models to Google AI Platform
  6. Deploying Deep Learning Models to AWS SageMaker
Offer Code *
Email * First name * Last name *
Company
Title
Phone
Country *

* Required field

Opt in for the latest promotions and events. You may unsubscribe at any time. Privacy Policy

By providing my phone number to Pluralsight and toggling this feature on, I agree and acknowledge that Pluralsight may use that number to contact me for marketing purposes, including using autodialed or pre-recorded calls and text messages. I understand that consent is not required as a condition of purchase from Pluralsight.

By activating this benefit, you agree to abide by Pluralsight's terms of use and privacy policy.

I agree, activate benefit