Tag: machinelearning

  • ML/AI Regression for Stock Prediction – AAPL Use Case

    1. Install Yahoo finance library 2. Call all dependencies that we will use for this exercise  3. Define the ticker you will use 4. Let’s look at the data table 5. Data Exploration Phase 6. Data Preparation, Pre-Processing & Manipulation 7. Apply Linear Regression 8. Perform ML QC Analysis 9. Final Output

  • HealthTech ML/AI Use-Cases

    About Supervised ML/AI Breast Cancer Diagnostics Python Use-Case Supervised ML/AI in Breast Cancer (BC) Classification Heart Failure Prediction using Supervised ML/AI Technique Diabetes Prediction using ML/AI in Python

  • Keras LSTM Stock Prediction

    Despite the vast amount of historical stock data, the high noise to signal ratio and varied market conditions cause inadequate stock price predictions. A solution to these challenges is Long Short Term Memory (LSTM). LSTMs offer a range of adjustable parameters without the necessity for fine-tuning, and efficiently handle sequential data.

  • Heart Failure Prediction using Supervised ML/AI Technique

    Heart failure is a worldwide healthy problem affecting more than 550,000 people every year. The heterogeneity of the syndrome is reflected by its many causes and classification systems. Machine learning has demonstrated significant potential in various medical research fields and has the potential to uncover intricate associations and the ability to identify subtypes of heart…

  • Supervised ML/AI Breast Cancer Diagnostics (BCD) – The Power of HealthTech

    Pilots Related to HealthTech Infographic These plots illustrate the most basic application of ML/AI in BCD as the binary classification problem. Classification usually refers to any kind of problem where a specific type of class label is the result to be predicted from the given input field of data. This is a task which assigns a label value…

  • About MLOps

    About MLOps

    Machine Learning (ML), a subset of Artificial Intelligence, enables computers to learn from experience, improving tasks through performance measures. Deployed by businesses across sectors, ML powers various applications such as chatbots, decision support tools, fraud detection, etc. ML uses data analytics concepts like predictive and prescriptive algorithms, and techniques such as supervised, unsupervised, and deep…