Here's how you can learn data mining algorithms through practical applications.

Start with the fundamentals. Understand different data types, preprocessing steps, and core concepts like classification, clustering, and regression. Python and R are essential tools.

Learning Basics is the most important step which is often missed. Being strong with fundamentals always helps you learn the new concepts quicker and solve the problems better. Implementing thorough EDA, fitting the appropriate Algorithm, Continuous Evaluation of Model Performance, these are the basic concepts one needs to get proficient to make a career in AI and Data Science

Typically, I have found learning through the process of iteratively alternating between theory and practical application, the most useful in cementing an understanding of ML algorithms. I start by reading theory from some books or online courses. My personal favorites are: 1. 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Geron Aurelien 2. 'The Hundred-page Machine Learning Book' by Andriy Burkov Then, I pick a few problems either from work or from online forums like Kaggle, and attempt to solve them using the knowledge I have acquired. This helps me know where gaps in my understanding exist, and I can revisit my books more surgically. I continue this process iteratively till I get better and better.

Study fundamental concepts of data mining, including data preprocessing, classification, clustering, and association rules. Start with smaller, manageable datasets and gradually move to more complex ones.Write code from scratch to understand the inner workings of algorithms.Compare different algorithms on the same dataset to understand their strengths and weaknesses.Join data science forums and communities like Kaggle or Stack Overflow.Attend virtual meetups or webinars to learn from experts and peers.Regularly review your projects and identify areas for improvement.Set new learning goals based on your experiences and interests

1)Data mining is playing a very crucial underhood role in the current AI trend , For any AI model to perform in the bestway it should be trained with lots and lots of structured and Unstructured data. 2)It is worth less to learn and create data mining algorithms which aren't designed for a specific goal or use case . If you try to create a data mining algorithm without any goal , what happens is you dont know how will it be useful to you and seems like waste of time 3) So implementing projects with data mining algorithm and having a good goal really paves your way to a better place 4)A tip on which you can rely if you really want to make a career on Data mining is focus on Synthetic Data generation . Keep Coding ???

Für den Einstieg ins Data Mining sind Projekte wie die Vorhersage von Aktienkursen, Wetterdaten, Energieverbrauch, Verkehrsfluss oder Verkaufszahlen im Einzelhandel besonders geeignet. Diese Projekte bieten eine praxisnahe Anwendung der Zeitreihenanalyse und grundlegender Techniken des maschinellen Lernens.

Selecting the right projects accelerates learning in data mining. Start with projects that match your skill level to build confidence and expertise. Beginners might begin with dataset analysis using simple classification algorithms. Advanced learners could tackle recommendation systems or complex time series forecasts. The key is to apply algorithms to datasets that interest you, making the process both educational and engaging. By working on real-world problems, you not only understand theoretical concepts but also gain practical experience. This approach helps you develop a portfolio showcasing your skills, making you a more attractive candidate for future opportunities.

A good program in this field is usually full of practical exercises, from the simplest to the most complex. A good part of them require developing several final activities that can be directed, or completely free in terms of topic selection, data set, and choice of algorithms. In those cases, the key is not to bet on the most complicated, or what we know the most. We must get out of our comfort zone, and choose projects that excite us. It doesn't matter if it is through a simple logistic regression, or a complicated neural network. The important thing is that we feel proud of the selected work. Only then will we have enough courage to carry it out.

Choosing the right projects can significantly enhance your learning experience. Start with projects that align with your current skill level to build confidence. For example, beginners might begin with a dataset analysis using basic classification algorithms, while those more advanced could work on a recommendation system or a complex time series forecast. The essential part is to apply the algorithms you’re learning to datasets that interest you, making the process both educational and engaging. This approach ensures that you not only grasp the theoretical aspects but also gain practical, hands-on experience.

Write algorithms from scratch to understand their workings. Begin with easier ones like k-means clustering or decision trees, then progress to complex models like neural networks or support vector machines.

Implementing data mining algorithms from scratch is a crucial step in mastering their intricacies and practical applications. Start with foundational algorithms like k-means clustering or decision trees, using familiar programming languages to construct them using basic constructs like loops and conditionals. This hands-on approach demystifies the algorithms and solidifies your understanding of their mechanisms. As you advance, tackle more sophisticated models such as neural networks or support vector machines. This progression not only sharpens your technical skills but also enhances your ability to optimize and innovate, positioning you as a proficient and insightful data mining professional.

Right, to really understand what we're doing, it's really helpful to write the algorithm. However, and taking into account that a good percentage of the people who work in this field are not programmers, once the algorithm is written, and proven that we obtain the same results as with scikit learn, caret, etc... my recommendation is use the libraries. They tend to be more optimized. Obviously, if your ability as a programmer is good, and you like that part of the vast world of data science, nothing prevents you from developing your own algorithms, or from contributing to improving existing ones. Personally, I prefer the analysis and modeling part to the programming part, but that is a matter of taste.

I would highly recommend testing on ready examples (those are everywhere right now) and then gradually start testing with more examples / datasets. This will really help you practically see what an algorithm does and where it can be used elsewhere

Implementing an algorithm from scratch immediately reveals the various tradeoffs and sub-decisions that needs to be taken in fitting a model. For example, in case of decision tree, how to find the split, slitting criteria, when to stop splitting etc. Once you implement the algorithm, you understand the motivation and importance of each of the criteria and can give you lot of insight into fitting models.

You need to learn how to do model training, testing and evaluation, along with bias-variance and cross validation concepts really well, to be able to train your models effectively.

In regression problems, do not forget to review the residuals of the model. You may have obtained a lower mean squared error, but if the residuals are not white, and depend on the predicted values, or on the independent variables, we will have a problem. Betas will be biased. In cases of binary classification, the ROC curve, and the area under the curve, is very useful to know how good the model is. In cases of multiclass classification, review the confusion matrix, and look at the values outside the main diagonal. Especially at the highest ones. This is where the algorithm fails the most.

Analyzing results is a cornerstone of mastering data mining algorithms. After implementation, evaluate performance using metrics like accuracy, precision, recall, or F1 score for classification tasks, and mean squared error for regression. This critical step not only measures success but also deepens your understanding of model behavior in different contexts. Grasping these metrics enables refinement of models, highlighting nuances in algorithm performance. Additionally, this analysis is pivotal in identifying overfitting and enhancing model generalization. Through meticulous evaluation, you gain insights into improving predictive power and robustness, thus advancing your expertise in data mining.

Understanding how an algorithm works with a set of data is important. There are metrics and diagnostic tests that "go with" most algorithms. However, don't start here - visualization of model results, metrics, and other diagnostic quantities will give even deeper understanding of the model vis-a-vis the dataset.

Interpreta??o dos resultados, com analise e interpreta??o dos resultados dos algoritmos aplicados aos dados. Valida??o dos modelos e das técnicas de valida??o cruzada e métricas de desempenho para avaliar a qualidade dos modelos.

Engage in online forums and platforms. Share your projects, get feedback, and learn from others. Collaboration exposes you to diverse methods and enhances your skills.

Engaging in online communities is pivotal for mastering data mining algorithms through practical applications. By collaborating on forums and platforms, you can exchange valuable insights, share your projects, and receive constructive feedback. This collaborative environment fosters innovation and exposes you to a variety of methods and techniques. Rather than copying others, aim to draw inspiration from their approaches to enhance your own solutions. Active participation in these communities not only broadens your knowledge but also sharpens your problem-solving skills, keeping you updated with the latest trends and best practices in the field of data mining.

One thing I've found incredibly helpful is joining various online communities focused on data mining. By participating in forums and platforms like Kaggle, Stack Overflow, and Reddit’s data science subreddits, you can share your projects and get valuable feedback. Seeing how others approach similar problems exposes you to diverse techniques and methods. Collaboration in these spaces often leads to new learning opportunities and innovative solutions. Remember, the goal is to draw inspiration and continuously refine your methods. This approach has significantly enhanced my understanding and application of data mining algorithms.

Una excelente forma de seguir aprendiendo y aplicar el conocimiento en la colaboración en línea, ser parte de proyectos te hace encontrar la experiencia y compartir tus habilidades en pro de proyectos que pueden beneficiar a la humanidad.

Collaboration leads to two types of learning, learning for others with more experience in testing algorithms and explaining their meaning, and also learning from teaching (neurologically, our brain learns more when we have to make an effort to organize and understand our own ideas, and better grasp the ‘why’ of things, in order to be able to explain them). Also, open source models available online may solve part of your starting point challenges. In short, it is imperative to collaborate.

Retrospect! Do a SWOT analysis Focus on strengths , work on weaknesses, look for opportunities and beware of the threats ! Its okay to fail ! Review what you do and apply that knowledge and learning to achieve bigger results next time.

Reflecting on my progress was a habit I cultivated throughout this journey. I maintained a journal where I documented my learning experiences, challenges, and breakthroughs. Regular reflection helped me identify gaps in my knowledge and areas for improvement. I also reviewed my old projects periodically, which provided a sense of accomplishment and motivated me to tackle more complex challenges.

Valuable Insights: Helpful Insight: Online communities are rich sources of collective knowledge. Participating in these forums allows you to gain insights from a diverse group of people with varying levels of expertise. Example: Platforms like Kaggle, Stack Overflow, and specialized forums like Data Science Central provide opportunities to ask questions, share solutions, and learn from others' experiences. Collaborating online is a powerful way to enhance your data mining and machine learning skills. It provides a platform for learning, sharing, and innovating. Engaging with a community not only helps solve immediate problems but also inspires long-term growth and development.

Using structured frameworks such as the Cross-Industry Standard Process for Data Mining (CRISP-DM) helps guide your projects systematically. This approach ensures comprehensive coverage of all aspects of data mining, from understanding the business problem to deploying the model. Many organizations, including IBM and SAS, utilize CRISP-DM to structure their data mining projects, ensuring a thorough and methodical approach to extracting insights from data

Após concluir a minera??o de dados, reflita sobre cada passo que você realizou até aqui e proponha mais uma ou duas vers?es do que foi construído. No mundo executivo, mesmo que a sua solu??o funcione perfeitamente em seu computador ou servidor, é possível que ocorram erros durante a execu??o ao mudar de rede, servidor ou ao usar o computador do seu cliente, seja ele interno ou externo. Esse exercício é fundamental para que você adquira maleabilidade no processo, e fa?a você passar mais rapidamente pelos novos desafios que terá.

Stay updated and keep practicing: -Online Courses and Tutorials: Platforms like Coursera, edX, and Udacity offer courses on data mining and machine learning. -Books and Research Papers: Read books like “Data Mining: Practical Machine Learning Tools and Techniques” by Witten, Frank, and Hall, and “Pattern Recognition and Machine Learning” by Bishop. Follow recent research papers for advanced techniques. -Community Engagement: Join data science and machine learning communities, participate in forums like Stack Overflow, and attend related meetups and conferences.

Great feedback from the many experts. At the beginning of any data science class I teach, I point to two obstacles my students will face in becoming data science practitioners. (1) Algorithms, statistics, Python, and the like are tools that help you listen to the story the data tells. (2) You are your worst enemy as you will start an analytical project with a preconceived notion of what to expect. Tools are important as you need tools to build the results, but never mistake the tools for the objective of your project. Yes, using deep learning or deploying a serverless ML architecture using MLFlow is sexy, but if you cannot answer the problem and clearly explain the story the data tells ... what is the point?

Learning algorithms by using them assumes that the algorithm actually "works" when we use it. Too often it doesn't. So what have we learned when we use an algorithm and are not aware that it didn't work, we used it incorrectly, or the algorithm is for a special case. Usually we can ask an AI app that's good at writing programs to provide assistance (and I recommend doing that) but AI occasionally does not understand what we're asking for and solves a totally different problem. Learning algorithms by using them can be like learning to be a car mechanic by driving to the store. To avoid this, we need to "repeatedly, take the engine apart and put it back together". We need to know when it fails/works.

It is necessary to have good metrics regarding the problem you are trying to solve, let’s say regression, classification, segmentation or whatever it is. But metrics are not sufficient. Take into for example in ANN the loss can be different of the metric used to evaluate the model. What is sufficient is to have a calibrated model (or at least near sufficient). For example, for a classification task, a model is said to be calibrated if its predicted probabilities accurately reflect the true likelihood of outcomes. In other words, if a model predicts a probability of 0.7 for a certain class, that class should occur approximately 70% of the time when the prediction is made. Always take this into account at the end.

To learn data mining algorithms through practical applications I would adopt a different set of steps than the one listed: Start with Basics: Begin by understanding core algorithms like decision trees, clustering, and regression analysis through online courses or textbooks. Hands-On Projects: Apply these algorithms to real-world datasets available on platforms like Kaggle or UCI Machine Learning Repository. Use Tools: Familiarize yourself with data mining tools like R, Python (using libraries like scikit-learn), and WEKA. Analyze Results: Interpret the outcomes to understand the effectiveness and limitations of each algorithm. Iterate: Refine your approach based on feedback and results to deepen your understanding and improve your skills.