Machine Learning Literacy - Practical Application

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Machine Learning Literacy - Practical Application

Author: Janani Ravi

Machine learning This path is focussed on Machine Learning in action. We have pulled a series of examples to demonstrate how machine learning is embedded in our day to day lives.... Read more

Pre-requisites

Understanding of algorithms used in the path. Though, it is desired, but not mandatory Understanding of Machine Learning Key concepts

Additional Role Titles: Data Analyst, Machine Learning Engineer, Software Engineers, and Business Analyst

Beginner

This path is designed to explore the application of Machine Learning in our day to day lives. This section focuses on a few industry examples of Machine Learning Practical applications. You will also explore approaches to Data Enabled Decision Making and Foundational Statistics required for understanding machine learning.

Key Concepts Machine Learning

by Janani Ravi

Nov 9, 2021 / 2h 6m

2h 6m

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Description

Machine Learning algorithms have the ability to adapt and learn from past experiences. Machine learning is important because of its wide range of applications and its incredible ability to adapt and provide solutions to complex problems.

In this course, Key Concepts Machine Learning, you will learn to identify use-cases where ML can provide an appropriate solution, and recognize common structures in ML-based solutions.

First, you will explore the limitations of rule-based approaches and how ML mitigates them. Then, you will discover the different types of ML models such as traditional models and representation models and see how you can develop the ML mindset to find solutions to meet your use case.

Next, you will explore common ML use cases such as regression, classification, clustering, and dimensionality reduction and learn the differences between supervised and unsupervised learning. You will also study specialized use cases such as recommendation systems, association rules learning, and reinforcement learning, as well as learning to apply ML to complex data such as text, images, and speech data.

Finally, you will learn how to formulate your use-case into one of these problem types so that it can then be solved with a commonly used ML-based approach.

When you are finished with this course, you will have the skills and knowledge of the conceptual underpinnings of Machine Learning needed to recognize use-cases for ML, and adopt common ML approaches.

Table of contents
  1. Course Overview
  2. Introducing Machine Learning Concept
  3. Identifying Problems Solved Using Machine Learning
  4. Applying Machine Learning to Complex Data
  5. Formulating a Simple Machine Learning Solution

Approaches to Data Enabled Decision Making

by Janani Ravi

Nov 12, 2021 / 1h 54m

1h 54m

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Description

Making an informed data-driven decision is important. In this course, Approaches to Data Enabled Decision Making, you’ll learn to structure decision-making in an enterprise setting to be grounded in data.

First, you will explore the different types of data-enabled decision-making such as data-inspired, data-informed, and data-driven decision making and understand the similarities and differences between these. You will also learn the basic steps involved in data-driven decision-making and how they can be applied in an organization.

Next, you will explore some common frameworks for data-enabled decision making such as the BADI framework, multiple-criteria decision making using goal programming, and the analytic hierarchy process. You will also learn how to relate to workflows in analytics, such as CRISP-DM, and the build-test-deploy lifecycle of an ML model. You will also study Porter’s five forces framework to analyze competitive forces in any industry.

Finally, you will explore real-world organizational case studies that use data to structure both tactical and strategic decisions. Case studies will cover the hospitality industry, a financial management firm, and a wedding and wine event management company.

When you’re finished with this course, you’ll have the skills and knowledge of data-driven decision-making needed to effectively structure and drive decision-making in your organization.

Table of contents
  1. Course Overview
  2. Introducing Data-driven Decision Making
  3. Exploring Decision Frameworks
  4. Applying Data-driven Decision Making

Foundations of Statistics and Probability for Machine Learning

by Janani Ravi

Nov 10, 2021 / 2h 12m

2h 12m

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Description

Learning the importance of p-values and test statistics and how these can be used to accept or reject the null hypothesis can lead you to explore the different types of t-tests and learn to choose the right one for your use case.

In this course, Foundations of Statistics and Probability for Machine Learning, you will learn to leverage statistics for exploratory data analysis and hypothesis testing.

First, you will explore measures of central tendency and dispersion including mean, mode, median, range, and standard deviation.

Then, you will explore the basics of probability and probability distributions and learn how skewness and kurtosis can give you important insights into your data.

Next, you will discover how you can perform hypothesis testing and interpret the results of these statistical tests.

Finally, you will learn how to perform and interpret regression models both simple regression with a single predictor and multiple regression with multiple predictors, and you will evaluate your regression models using R-squared and adjusted R-squared and understand the t-statistic and p-value associated with regression coefficients.

When you are finished with this course, you will have the skills and knowledge of statistics and data analysis needed to effectively explore and interpret your data as a precursor to applying machine learning techniques.

Table of contents
  1. Course Overview
  2. Understanding Descriptive Statistics and Probability Distributions
  3. Interpreting Data Using Statistical Test
  4. Performing Regression Analysis

Machine Learning for Financial Services

by Janani Ravi

Nov 24, 2021 / 1h 50m

1h 50m

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Description

Analytical and statistical models are already an integral part of the finance industry and the use of machine learning builds on a strong foundation in this industry. The financial services industry is uniquely positioned to leverage machine learning because of the vast quantities of high-quality data already available.

In this course, Machine Learning for Financial Services, you will explore machine learning techniques currently applied in the financial services industry. First, you will look at some examples and cases of where ML is already being used in financial services - for investment predictions, loan automation, process automation, and fraud detection. Then, you will develop an intuitive understanding of how recurrent neural networks

Next, you will explore two ML case studies from research papers - the first focusing on assessing and quantifying the return on investment and the second exploring how classification and clustering models can help detect money laundering.

Finally, you will get hands-on coding and see how you can use a classification model for fraud detection on a synthetically generated dataset.

When you are finished with this course, you will have the awareness of how machine learning can be applied in the financial services industry and hands-on experience working with financial data.

Table of contents
  1. Course Overview
  2. Exploring Applications of Machine Learning in Financial Services
  3. Case Study: Quantifying Risk and Return of Investment Opportunities
  4. Case Study: Extracting Insights for Fraud Detection
  5. Applying Machine Learning Techniques to Financial Data

Coming Soon

Machine Learning for Retail

Coming Soon

by Janani Ravi

Machine Learning for Healthcare

by Janani Ravi

Nov 24, 2021 / 1h 48m

1h 48m

Start Course
Description

The healthcare industry generates vast quantities of data, and so presents unique opportunities for applying machine learning. The use of machine learning in healthcare can prove transformative in the lives of people around the world.

In this course, Machine Learning for Healthcare, you’ll explore machine learning techniques currently applied in the healthcare industry. First, you’ll explore a few specific use cases such as the use of ML techniques for epidemic control, AI-assisted robotic surgery, patient diagnosis, and the automation of administrative tasks. You will also get an intuitive understanding of how convolutional neural networks work and how they are used in medical imaging.

Next, you will understand the steps involved in applying machine learning techniques to chronic disease prediction. You will study a case from a research paper that uses natural language processing and text extraction techniques on medical notes to diagnose chronic diseases for hospital patients. Another case study will discuss the use of medical imaging and image preprocessing techniques to detect leukemia from microscopic blood cell images.

Finally, you will get hands-on coding and see how you can use regression models to predict blood pressure and classification models to predict liver disease.

When you are finished with this course you will have the awareness of how machine learning can be applied in the healthcare industry and hands-on experience working with healthcare data.

Table of contents
  1. Course Overview
  2. Exploring Applications of Machine Learning in Healthcare
  3. Case Study: Disease Detection Using Machine Learning
  4. Case Study: Diagnosis Using Medical Imaging
  5. Applying Machine Learning Techniques to Healthcare Data

Coming Soon

Machine Learning for Marketing

Coming Soon

by Janani Ravi

Learning Paths

Machine Learning Literacy - Practical Application

  • Number of Courses7 courses
  • Duration10 hours
  • Expanded

Machine learning This path is focussed on Machine Learning in action. We have pulled a series of examples to demonstrate how machine learning is embedded in our day to day lives. These are just in time sort of courses that reflect the journey from problem to solution.

The path is curated for Data enthusiasts that are eager to learn about Machine learning and foray into Data centered roles like Machine learning experts. Though this path will contain workable solutions there is no requirement for the learner to any background into Machine Learning

Courses in this path

Beginner

This path is designed to explore the application of Machine Learning in our day to day lives. This section focuses on a few industry examples of Machine Learning Practical applications. You will also explore approaches to Data Enabled Decision Making and Foundational Statistics required for understanding machine learning.

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