David Rossitter’s Post

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴? 🤖   (𝗗𝗶𝗱 𝘆𝗼𝘂 𝗸𝗻𝗼𝘄 𝗶𝘁’𝘀 𝗮 𝗴𝗮𝗺𝗲-𝗰𝗵𝗮𝗻𝗴𝗲𝗿 𝗮𝗰𝗿𝗼𝘀𝘀 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀?)  • Machine Learning (ML) is a subset of AI that empowers systems to learn from data, identify patterns, and make predictions without explicit programming.  • It drives innovation across industries, powering solutions like recommendation systems, fraud detection, and predictive analytics.  𝗣.𝗦. 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗲𝗱 𝗶𝗻 𝘀𝘁𝗮𝘆𝗶𝗻𝗴 𝘂𝗽-𝘁𝗼-𝗱𝗮𝘁𝗲 𝘄𝗶𝘁𝗵 𝗠𝗟? 𝗗𝗼 𝗳𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝗳𝘂𝗹 𝗽𝗼𝘀𝘁𝘀!  📊 𝗔𝗹𝘀𝗼, 𝗱𝗼𝗻’𝘁 𝗺𝗶𝘀𝘀 𝗺𝘆 𝗽𝗿𝗲𝘃𝗶𝗼𝘂𝘀 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗦𝗲𝗿𝗶𝗲𝘀 – 𝗮 𝗴𝗼𝗹𝗱𝗺𝗶𝗻𝗲 𝗼𝗳 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗳𝗼𝗿 𝗮𝗻𝘆𝗼𝗻𝗲 𝗱𝗲𝗹𝘃𝗶𝗻𝗴 𝗶𝗻𝘁𝗼 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲.  #MachineLearning #ArtificialIntelligence #DataScience #Technology

Forecasting can be useful when it comes to helping a business plan for the future. One of the best technologies to help your organisation make reliable forecasts is Machine Learning (ML), a form of Artificial Intelligence. Note that it can analyse vast amounts of data quickly to help make forecasts. What are your thoughts on ML in predictive analytics? Please let me know in the comment section below. Discover more here: https://heyor.ca/NRwq2Y #Forecasting #MachineLearning #Data #PredictiveAnalytics

FForecasting can be useful when it comes to helping a business plan for the future. One of the best technologies to help your organisation make reliable forecasts is Machine Learning (ML), a form of Artificial Intelligence. Note that it can analyse vast amounts of data quickly to help make forecasts. What are your thoughts on ML in predictive analytics? Please let me know in the comment section below. Discover more here: https://heyor.ca/NRwq2Y #Forecasting #MachineLearning #Data #PredictiveAnalytics

Forecasting can be useful when it comes to helping a business plan for the future. One of the best technologies to help your organisation make reliable forecasts is Machine Learning (ML), a form of Artificial Intelligence. Note that it can analyse vast amounts of data quickly to help make forecasts. What are your thoughts on ML in predictive analytics? Please let me know in the comment section below. Discover more here: https://heyor.ca/NRwq2Y #Forecasting #MachineLearning #Data #PredictiveAnalytics

🔍 Unraveling Feature Importance in Machine Learning 🚀 In the world of Machine Learning, understanding which features hold the most weight in your predictions can be a game changer. This comprehensive guide dives deep into Feature Importance, providing practical strategies to: 📊 Rank and prioritize features for better model accuracy 🧠 Enhance interpretability of your models with actionable insights 🔧 Boost performance through effective feature selection and engineering Discover how to fine-tune your models and achieve better results with a focus on the most impactful data variables. 🔗 Read the full article here: https://lnkd.in/dDNqEEYP #MachineLearning #DataScience #AI #FeatureImportance #ModelOptimization #AIInsights #TechInnovation #FeatureEngineering #MLInterpretability

When building an artificial intelligence model I have a habit of first building what I term a "Benchmark" model. It's always a shallow network of one or two layers with no regularizations. Oftentimes time some of my closest friends also in the field asked why I always take my time to build a "Shallow" model and my response has always been that it helps me understand exactly what I'm working against. Building an AI model is just a means to an end and that end is the most important thing therefore, the intention of building an AI model isn't to tell or more about what it has been exposed to during training and that's because it already has sufficient information and could easily map a prediction but what is more important is how a model performs when it's exposed to entirely new information. Building a model that generalizes well on this new dataset involves several optimizations and this process becomes easier when you begin to optimize from the least model accuracy or performance. Even though my experiences can dictate some assumptions such as the number of layers, networks, learning rate, optimizer, etc that can give optimal performance however I always unconsciously build a "Shallow Model" for any project I am about to undertake. It helps. #ArtificialIntelligence #Machinelearning

Project 2) Hi everyone! I recently developed a simple yet effective machine learning model using Logistic Regression to help branded car companies predict whether potential customers are likely to purchase a car based on their age and salary. 🔍 Here's what I did: 1) Used a real-world dataset with 400 observations to train the model. 2) Preprocessed the data and performed feature scaling. 3) Split the data into 75% training and 25% testing sets. 4) Achieved an 89% accuracy on the test set! 🎯 5) Use Streamlit to create an interactive user interface. I'm looking forward to taking on more challenging projects as I continue exploring the field of Machine Learning. Feedback and suggestions are always welcome! 🙌 #MachineLearning #LogisticRegression #Streamlit #DataScience #AI #Technology #Innovation #LearningJourney #CarPurchasingModel

Unlocking the Power of Feature Importance in Machine Learning Understanding which features matter most in your ML model can significantly boost accuracy and transparency. This article dives deep into feature importance, a critical aspect of building robust machine learning systems. Key Highlights: Explains the concept of feature importance and its relevance in interpreting ML models. Covers popular methods like SHAP values, Permutation Importance, and Feature Selection Techniques. Demonstrates the balance between performance optimization and maintaining model explainability. Explores real-world applications, from healthcare diagnostics to financial risk assessment. 🔗 Read the full article: https://lnkd.in/dDNqEEYP Additional Resources: Hands-on Tutorials: https://www.kaggle.com/ Practical Guide: https://scikit-learn.org/ #MachineLearning #AI #FeatureImportance #DataScience #ModelInterpretability #TechInnovation #ExplainableAI

𝐉𝐮𝐬𝐭 𝐥𝐢𝐤𝐞 𝐰𝐚𝐭𝐞𝐫 𝐜𝐫𝐚𝐬𝐡𝐢𝐧𝐠 𝐚𝐠𝐚𝐢𝐧𝐬𝐭 𝐫𝐨𝐜𝐤𝐬 𝐬𝐡𝐚𝐩𝐞𝐬 𝐭𝐡𝐞𝐦 𝐨𝐯𝐞𝐫 𝐭𝐢𝐦𝐞, 𝐝𝐚𝐭𝐚 𝐫𝐞𝐬𝐡𝐚𝐩𝐞𝐬 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥𝐬. The power isn’t in the single wave, but in the persistence. How are you shaping your AI models with data? If you're not consistently refining, you're missing opportunities for growth. 𝐇𝐞𝐫𝐞’𝐬 𝐡𝐨𝐰 𝐭𝐨 𝐡𝐚𝐫𝐧𝐞𝐬𝐬 𝐝𝐚𝐭𝐚’𝐬 𝐭𝐫𝐮𝐞 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐢𝐧 𝐌𝐋: 𝐒𝐭𝐚𝐫𝐭 𝐬𝐦𝐚𝐥𝐥, 𝐢𝐭𝐞𝐫𝐚𝐭𝐞 𝐟𝐚𝐬𝐭:   ↳ Test hypotheses quickly    ↳ Learn from each iteration    ↳ Adapt based on feedback 𝐔𝐬𝐞 𝐝𝐢𝐯𝐞𝐫𝐬𝐞 𝐝𝐚𝐭𝐚 𝐬𝐨𝐮𝐫𝐜𝐞𝐬:   ↳ Broaden your dataset variety    ↳ Capture multiple perspectives    ↳ Boost model generalization 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞 𝐭𝐡𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐜𝐲𝐜𝐥𝐞:    ↳ Reduce manual intervention    ↳ Scale efficiently    ↳ Ensure continuous improvement 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐦𝐨𝐝𝐞𝐥 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞:    ↳ Track relevant metrics    ↳ Monitor over time    ↳ Learn from changes 𝐀𝐝𝐝𝐫𝐞𝐬𝐬 𝐝𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐞𝐚𝐫𝐥𝐲:    ↳ Clean and preprocess    ↳ Handle missing data    ↳ Reduce bias upfront Ready to see the power of persistence in action? Start with a small project. Experiment, iterate, and let the data do the work. The results will speak for themselves. What’s your first experiment going to be? ♻️ Repost to your LinkedIn followers and follow Timothy Goebel for more actionable insights on AI and innovation. #MachineLearning #DataDrivenAI #AIInnovation #ContinuousImprovement #AIExperimentation