Machine Learning
What is Machine Learning?
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence that uses algorithms to learn from data and make predictions or decisions without explicit instructions. Machine learning can be used for a variety of tasks, such as image and speech recognition, natural language processing, and decision making. It can be divided into three main categories: supervised learning, unsupervised learning and reinforcement learning. It is widely used across various industries, such as finance, healthcare, marketing, biotechnology, and logistics.
Steven's Machine Learning Support
Real-World Applications
Real-World Applications
Real-World Applications
Personalized shopping using machine learning is a popular application in the e-commerce industry. Here's an example of how it can work:
A retail company uses machine learning to create a personalized shopping experience for its customers. The company gathers data on customer browsing and purchase history, as well as demographic information. This data is used to train a machine learning model that can predict which products a customer is likely to be interested in.
When a customer visits the company's website, the model is used to recommend products that are tailored to the customer's individual preferences. For example, if a customer has previously shown an interest in outdoor gear, the model might recommend camping equipment.
The company also uses machine learning to personalize the layout of the website, such as recommending products in the order that is most likely to lead to a purchase. Additionally, the company can use machine learning to predict the likelihood of a customer returning and proactively reach out to the customer with personalized offers and discounts.
This type of personalized shopping experience can lead to increased customer satisfaction and loyalty, as well as increased sales for the company. Additionally, machine learning models can be continuously trained on new data, allowing the company to improve recommendations over time.
Discover how Steven utilized online transactions' data to gain insights on product pairings. Steven's work resulted in an increase in e-commerce sales for a retail client.
Click below to learn more.
Hands on Experience
Real-World Applications
Real-World Applications
Steven has extensive experience collaborating with clients to develop and deploy machine learning models to solve real-world problems. He recently worked with education officials in Texas to create a series of models to predict student performance across various subject areas. Using a combination of ensemble techniques, Steven delivered a highly versatile model that could predict each students' end-of-the-year state exam scores using historic subject area performance and environmental factors.
Steven also helped a major sports apparel and footwear retailer gain valuable insights into how terms searched on their e-commerce website translated into products ultimately purchased by customers. To achieve this, Steven developed and trained a natural language processing (NLP) model to identify the association between customer search terms and product names, descriptions, and characteristics. This insight helped the client's e-commerce team align their product descriptions with customer search terms.
Additionally, Steven supports all facets of machine learning projects, from data preparation and feature engineering, to model development, evaluation, training, and deployment.
Steven has developed and implemented the following models:
Association Rules, Random Forests, Gradient Boosting, Support Vector Machines (SVMs), Neural Networks, K-Means Clustering, Recurrent Neural Networks, and XGBoost models.
Click below to discover how Steven utilized machine learning models to identify academic gaps and struggling students, resulting in improved performance on state tests.
Steven's Proprietary Machine Learning Models
Steven's Bidirectional Extended Transactions Model
Discover how Steven's Association Rules Model can uncover hidden product insights in customer buying patterns from single item transactions.
Technical Proficiency
Steven is proficient in various machine learning tools, including Python, Data Bricks, Data Iku, Alteryx, as well as Python libraries, Scikit Learn and Tensorflow.
Expand Your Knowledge
There are many resources available for learning machine learning. Here are a few popular options:
- Coursera: Coursera offers a wide range of online courses on machine learning from top universities and institutions such as Stanford, University of Washington, and Imperial College London.
- edX: edX also offers a variety of online machine learning courses from universities such as MIT, Harvard and Berkeley.
- Udemy: Udemy has a large selection of machine learning courses that are taught by industry experts.
- fast.ai: fast.ai is a non-profit that offers a free, open-source education in artificial intelligence and machine learning.
- Kaggle: Kaggle is a platform that offers a variety of machine learning competitions, tutorials and datasets to help you learn and practice machine learning.
- DataCamp: DataCamp provides interactive machine learning courses and tutorials in Python and R.
- Machine Learning Mastery by Jason Brownlee: This website offers a variety of tutorials and articles on machine learning using the Python programming language.
These are just some of the many resources available for learning machine learning, you can also check out books, tutorials, and research papers, depending on your preference and learning style.