Four-Project Series

Math for Machine Learning you own this product

prerequisites
intermediate Python • basic understanding of vectors and matrices from linear algebra • basic understanding of probability from statistics • basic understanding of derivatives from calculus
skills learned
manipulate NumPy arrays and pandas DataFrames • Eigenvalues and Eigenvectors • Singular Value Decomposition • principal component analysis • backpropagation • Bayes' theorem • fine-tune large language models
Earn a Certificate of Completion with this liveProject series
Nicole Königstein
4 weeks · 6-8 hours per week average · INTERMEDIATE
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includes 4 liveProjects
liveProject $35.99 $59.99 self-paced learning

Put on your data scientist hat for this series of liveProjects, where you’ll work at Finative, an analytics company that uses environmental, social, and governance (ESG) factors to measure companies’ sustainability, a brand new, eco-focused trend that's changing the way businesses think about investing. In each liveProject, you’ll focus on different machine learning (ML) and deep learning (DL) mathematical approaches—including Bayes' theorem, principal component analysis (PCA), cosine similarity, latent semantic analysis, and backpropagation—as you help Finative accomplish its goal of increasing its own sustainability.

You’ll develop a method to reduce the runtime of ML models, and you’ll save digital storage space by finding relevant keywords in order to determine whether documents should be discarded or saved. To increase efficiency, you’ll save training time by using a pre-trained language model to classify a sustainability report. Then, you’ll analyze the sentiment of tweets in order to detect greenwashing, the practice of spreading disinformation about a company’s sustainability. When you’re finished with these liveProjects, you’ll have a solid understanding of the mathematical basics of machine learning, strong programming and data science skills, and familiarity with sustainability.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.
How to get your FREE
Certificate of Completion
  • Finish all the projects in this liveProject series
  • Take a short online test
  • Answer questions from the liveProject mentor
That's it!

The takeaway from the author is very insightful. In general, I would say this project is very helpful and we can learn the theory and practice at the same time.

Zhiwei Cheng, Analyst, Allianz

here's what's included

Project 1 Principal Component Analysis

Step into the role of data scientist at Finative, an analytics company that uses environmental, social, and governance (ESG) factors to measure companies’ sustainability, a brand new, eco-focused trend that's changing the way businesses think about investing. To provide its clients with the valuable insights they need in order to develop their investment strategies, Finative analyzes a high volume of data using advanced natural language processing (NLP) techniques.

Recently, your CEO has decided that Finative should increase its own sustainability. Your task is to develop a method to optimize the runtime for the company’s machine learning models. You’ll apply principal component analysis (PCA) to the data in order to speed up the ML models. To classify handwritten digits and prove your theory that PCA speeds up ML algorithms, you’ll implement logistic regression with scikit-learn. You’ll use the explained variance ratio to gain an understanding of the trade-offs between speed and accuracy. When you’re done, you’ll be able to present your CEO with proof of PCA’s efficiency in optimizing runtime.

Project 2 Latent Semantic Analysis for NLP

At Finative, an ESG analytics company, you’re a data scientist who helps measure the sustainability of publicly traded companies by analyzing environmental, social, and governance (ESG) factors so Finative can report back to its clients. Recently, the CEO has decided that Finative should increase its own sustainability. You’ve been assigned the task of saving digital storage space by storing only relevant data. You’ll test different methods—including keyword retrieval with TD-IDF, computing cosine similarity, and latent semantic analysis—to find relevant keywords in documents and determine whether the documents should be discarded or saved for use in training your ML models.

Project 3 Analyze Reports with Hugging Face

You’re a data scientist at Finative, an environmental, social, and governance (ESG) analytics company that analyzes a high volume of data using advanced natural language processing (NLP) techniques in order to provide its clients insights for sustainable investing. Recently, your CEO has decided that Finative should increase its own financial sustainability. Your task is to classify sustainability reports of a publicly traded company in an efficient and sustainable way.

You’ll learn the fundamental mathematics—including backpropagation, matrix multiplication, and attention mechanisms—of Transformers, empowering you to optimize your model’s performance, improve its efficiency, and handle undesirable model predictions. You’ll use Python’s pdfplumber library to extract text from a sustainability report for quick delivery to your CEO. To further increase efficiency, you’ll save training time by using a language model that’s been pre-trained with ESG data to build a pipeline for the model and classify the sustainability report.

Project 4 Detect Sentiment with Transformers

Finative, the environmental, social, and governance (ESG) analytics company you work for, analyzes a high volume of data using advanced natural language processing (NLP) techniques to provide its clients with valuable insights about their sustainability. Your CEO has concerns that some of the companies Finative analyzes may be greenwashing: spreading disinformation about their sustainability in order to appear more environmentally conscious than they actually are.

As a data scientist for Finative, your task is to validate your sustainability reports by creating and analyzing them. You’ll compute conditional probability with Bayes’ Theorem, by hand, to better understand your model’s performance through metrics such as recall and precision. You’ll learn an efficient way to prepare your data from different sources and merge it into one dataset, which you’ll use to prepare tweets. To successfully classify the tweets, you’ll use a pre-trained large language model and fine-tune it using the Hugging Face ecosystem as well as hyperopt and Ray Tune. You’ll use TensorBoard and Weights & Biases to analyze and track your experiments, and you’ll analyze the tweets to determine whether enough negative sentiment exists to indicate that the company you analyzed has been greenwashing its data.

books resources

When you start each of the projects in this series, you'll get full access to the following books for 90 days.

The free project does not include full access to these Manning books. Purchase the full series to unlock this access in the free project, too!

project author

Nicole Königstein

Nicole Königstein currently works as data science and technology lead at impactvise, an ESG analytics company, and as a quantitative researcher and technology lead at Quantmate, an innovative FinTech startup that leverages alternative data as part of its predictive modeling strategy. She’s a regular speaker, sharing her expertise at conferences such as ODSC Europe. In addition, she teaches Python, machine learning, and deep learning, and holds workshops at conferences including the Women in Tech Global Conference.

Prerequisites

These liveProjects are for ML engineers, intermediate-level Python programmers, and early-stage data scientists. To begin these liveProjects you’ll need to be familiar with the following:

TOOLS
  • Python, particularly the pandas, NumPy, scikit-learn, Matplotlib, and seaborn libraries
TECHNIQUES
  • Intermediate linear algebra
  • Intermediate calculus
  • Intermediate statistics and probability

you will learn

In these liveProjects, you will learn progressively more complex mathematical techniques that will deepen your understanding of ML models and strengthen the skills you need for long-term success as a data scientist or machine learning engineer.

  • Manipulate NumPy arrays and pandas DataFrames
  • Eigenvalues and Eigenvectors
  • Principal Component Analysis
  • Singular Value Decomposition
  • Latent Semantic Analysis
  • TF-IDF
  • Backpropagation
  • Bayes' theorem
  • Attention mechanism
  • Fine-tune large language models

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
Certificate of Completion
Earn a certificate of completion, including a badge to display on your resume, LinkedIn page, and other social media, after you complete this series.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
How to get your FREE
Certificate of Completion
  • Finish all the projects in this liveProject series
  • Take a short online test
  • Answer questions from the liveProject mentor
That's it!

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  • Math for Machine Learning eBook for free

The series walks you through the basic math employed in different aspects of ML.

Maxim Volgin, Quantitative Marketing Manager, KLM
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