1-Year Anniversary of LearnAIWithMe

Thanks for being with us!

When I first encountered AI in Deep Learning, I was fascinated by the possibilities. Back then, some of the products we see today could only be imagined.

But after the release of ChatGPT, everything changed. Thanks to this pioneer, many similar products—like Claude, Llama, and Gemini—have since been released, and these technologies have impacted all industries.

However, how can you adapt to these changes? How can you learn AI from scratch? How can you stay updated with AI news on time? How can you apply these AI technologies to different industries?

Weekly AI Pulse

There are 51 AI Weekly Newsletter has been published. Here you’ll see ;

• 3 News - Deep Explanation + Option to listen this news, check below;

  

  
  

• 3 Recommended Articles + Option to listen their Summaries, check below

  

• Option to Listen Entire Newsletter;

  

  LearnAIWithMe GPT

  

  You can kickstart your AI journey with LearnAIWithMeGPT, exclusively available to our paid subscribers.

  This series shows you step-by-step how to use LearnAIWithMeGPT to master AI concepts and skills. Ready to dive in? Here’s what you can expect:

  

  Next Gen Data Projects

When you subscribe, you’ll get access to next-gen data projects packed with the latest techniques and insights. These projects include cutting-edge approaches, from the newest methodologies to practical instructions on how to work with large language models (LLMs).

Whether you’re leveling up your skills or looking to apply fresh solutions, this is where the future of data science is taking shape. Let’s dive into some of the exciting content waiting for you

Heart Failure Mortality Prediction

In this data project, we’ll explore heart failure mortality prediction using six different classification algorithms

• Random Forest

• Logistic Regression

• K-Nearest Neighbors (KNN)

• Decision Tree

• Support Vector Machine (SVM),

• Naive Bayes

We aim to find the most effective algorithm based on various performance metrics. To do that, we compared the algorithms;

Data Source: The dataset originates from the UCI Machine Learning Repository.

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Analysis Approach:

1. Data Preparation: Initial checks and cleaning ensure the dataset's readiness for analysis.

2. Algorithm Testing: The performance of each algorithm is assessed using metrics like Accuracy and F1 Score.

3. Feature Engineering: Dimensionality reduction through PCA and redundancy checks via correlation analysis streamline the feature set.

4. Model Selection: Cross-validation is employed to select the most robust model against overfitting.

5. Results: Key findings highlight the superior performance of Random Forest and Logistic Regression in precision and accuracy.

Check the data projects available for our paid subscribers in the gDrive link we sent to you after becoming a paid subscriber.

Final Thoughts

Thanks for being a subscriber. We hope that in the coming year, you’ll take even more advantage of AI! See you!