You want to become a data scientist. How can you learn to use machine learning?

Data Science problems don't always require Machine Learning. That's a very first thing to keep mind. With that being said, you can leverage your ML skills for solving Data Science Problems. For example: The difference between a data scientist who knows ML vs the one who doesn't know, can clearly be visible in the solution architecture. ML lets you approach the problem very differently from the classical Stats perspective.

To learn machine learning, start with a strong foundation in mathematics, statistics, and Python programming. Focus on understanding machine learning basics through online courses and reading resources. Dive deep into different types of machine learning algorithms and use libraries like Scikit-learn, TensorFlow, and PyTorch. Gain practical experience by working on projects and participating in online competitions. Stay updated with the latest trends and research, and network within the machine learning community. It is a journey of continuous learning and practice. Showcase your projects on platforms like GitHub. A strong portfolio can be as valuable as formal education. Start small and always be patient with your progress.

It’s important to learn the basics, which includes programming languages, libraries. It’s essential to understand the mathematics behind a machine learning model. After being through with the basics, practice, practice, practice! Find datasets on Kaggle and try implementing models along with validations and graphs.

Online Courses and Tutorials: Explore online resources for structured learning paths, topics, and projects. Practice with Datasets: Utilize platforms like Kaggle and UCI to work with datasets, focusing on data cleaning and exploratory data analysis (EDA). Join Data Science Communities: Engage with communities on platforms like Kaggle, KDNuggets, TowardsDataScience, and LinkedIn to stay updated on Machine Learning trends. Build and Deploy Projects: Start with small projects to learn about production-level architecture, including Machine Learning model deployment and monitoring.

When anyone starts learning ML. The best way is to start with regression and then classification algorithms. You can choose a problem to test your skills. Some of the famous ones are: - House price regression - Email classification - Titanic survival analysis

To learn machine learning as a data scientist, choose a problem that interests you, has a dataset available, matches your current skill level, and aligns with your learning goals. Start with simpler problems and gradually move to more complex ones as you gain experience.

Choosing a problem is critical to start the machine learning journey. How to select a machine learning problem requires few questions to answer - is the problem statement is demanding a prediction for future? - If so, how much data available ? 500-1000 data points? ( for practice) - is the expected outcome is a grouping (cluster) of items or continuous number like 1.1, 22.2,5.0 Or probability of an event happens either 0 or 1. - select the ML algorithm that is suited to your problem based on answers to above questions.

To learn machine learning, you can start from the most basic problem, such as classifying iris species or predicting house price, etc. Although these simple problem are not suitable to be one of your portfolio, but by learning from simple dataset, you can learn how to implement every single algorithms and every trick such as feature engineering, hyperparameter tuning, model stacking, model pipelining, and so on. After you confident enough with your skill, you can step up the game playing with bigger data and harder problems. If you want to find one, just check kaggle, there are a lot of real-world data to play with, you can even share what you've done to public as portfolio.

- Choose a use case and associated dataset from UCI Machine Learning (ML) repository - Start with numerical datasets with no missing values - Start from either classification or regression (using the appropriate filter on the UCI ML repo) - Have a look at scikit-learn's documentation and choose relevant predictive performance metrics based on 1) what matters the most for the chosen use case (what needs to maximised or minimised, and what is its impact?) and 2) the type of problem being solved (e.g., regression or classification) - Learn, practise, iterate, enjoy ??

Building machine learning is just 20% of your time. 80% of it you spend on exploring the data. Usually, before machine learning modeling, I do these 3 steps: 1. Data Inspecting: to inspect the structure of the data and any abnormalities inside the data such as outlier, empty data, and so on 2. Data Preprocessing: after inspecting the data, I usually split the data to train and test set, then do the preprocessing on both dataset. 3. Data Exploration: check any pattern you can find inside the train set such as distribution, trend, correlation, etc. Everything you find on data exploration step may help you on build machine learning model.

Engaging with real-world data is crucial for aspiring data scientists. It's a messy, unpredictable affair that contrasts sharply with the neat datasets often used in academic settings. This hands-on experience is invaluable; it teaches resilience and adaptability, skills that are essential when facing the complexities of real-world data challenges. It's in this unstructured environment that the true art of data science is mastered.

Dive into the data related to your chosen problem. Explore its structure, quality, and relationships between different variables. Visualization tools like matplotlib or seaborn can help you gain insights into the data. Look for patterns, outliers, and any missing values that need to be addressed. Understanding your data thoroughly is crucial for building accurate machine learning models.

Building a ML model is about 20% effort. The major portion of your time will be spent preproccsing and analysing th data. Th first step before anything is conducting and exploratory data analysis or EDA. This a crucial step to any data science project. Never skip this. This will give you insights and a deeper understanding of the dataset you are working with which will eventually help with model building. After your EDA the majority of the time is spent cleaning and preprocessing the data to make it viable for the model to consume. This includes normalization, missing value treatment, encoding, etc. After all this we can start building the actual model.

Based on your problem and data exploration, choose appropriate machine learning algorithms to solve it. Start with simpler models like linear regression or decision trees and gradually move to more complex ones like neural networks if needed. Consider factors like the size of your dataset, the type of features, and the interpretability of the model. Experiment with different algorithms and techniques to find the one that performs best for your specific problem.

Training and testing an ML model is where theory meets practice, a nuanced understanding of the problem. This phase is pivotal because no matter how sophisticated a model is, its true test lies in its performance on unseen data. This stage embodies the iterative essence of ML, underscoring the importance of experimenting with different approaches, parameters, and even models to achieve the best outcome. Furthermore, it highlights the crucial role of evaluation metrics that align with the project's goals, ensuring that the model's performance is measured in a meaningful and relevant way to the problem. This process not only tests our models but also our assumptions and understanding, making it a rich learning experience in itself.

With model complexity increase, it can be daunting to choose the hyperparameters that give you descent results. Should you add more layers or increase number of CNN filters or change SVM kernel: manual hyperparameter tuning can be difficult . So, using libraries like hyperOPT for Bayesian optimization can be tried. If multi-objective optimization is desired, e.g , in CGM sensor data, reducing Mean error between blood glucose and sensor glucose and maximizing sensor longevity are competing objectives, so optimizing them using Genetic algorithm or Particle Swarm optimization can do the trick. While choosing such algorithm, choose the one that requires less parameter tuning, not to add up to the complexity of model hyperparameter tuning

Training and testing models is really what gets many people hooked in data science. Seeing the accuracy of your model improve or the classification algorithm work effectively is a great feeling. With that in mind, creating a functioning model is iterative, and there are considerations, such as parameter tuning and reflecting on project goals that should be considered for the granularity in training and testing.

When deploying your ML model, focus on smooth integration and real-world performance. Use containerization for easy deployment and scalability. Monitoring is crucial—set up systems to track performance metrics and watch for data drift, which can degrade your model over time. Regular updates based on real-world feedback and performance data are key to keeping your model relevant and effective. Remember, deployment is just the beginning of a model's lifecycle in production!

Deploying and monitoring ML models bridges theory with real-world application, ensuring performance in dynamic environments. Deployment marks a model's entry into its operational phase, serving its purpose. Monitoring, akin to ongoing education, maintains its relevance over time. This step addresses scalability, security, and maintenance, showcasing a model's resilience. Tools like AWS SageMaker, Google Cloud AI, Azure ML, TensorFlow Serving, and Flask enable deployment and monitoring, Dashboards such as Grafana visualize performance, ensuring models adapt and thrive. These technologies demand a blend of technical skill and foresight, vital for navigating real-world challenges and ensuring the longevity and impact of ML models.

Remember, learning machine learning is a journey that requires continuous practice and experimentation.Challenges are opportunities to grow, and sharing insights with others helps us navigate towards mastery. With each step, we uncover new possibilities and refine our skills along the way.

becoming a data scientist and learning to use machine learning requires a combination of education, practical experience, experimentation, collaboration, specialization, and continuous learning. By following these steps and embracing a growth mindset, anyone can embark on a fulfilling and rewarding career in the dynamic and rapidly evolving field of data science. ??

Like machine learning models, humans also require training on quality data. They must actively seek reliable sources, such as arXiv, GitHub, Hugging Face,domain specific use cases, etc., to improve their knowledge and skills.

Aprendizado de Máquina é uma área de conhecimento como qualquer outra e para aprender os seus tópicos e conceitos é preciso buscar suas referências na fonte básica. Ent?o, nesse sentido é interessante primeiro saber os seus fundamentos para depois aprender as ferramentas de implementa??o dos algoritmos da área. Os livros s?o um bom come?o para o aprendizado e depois deles é possível partir para artigos surveys que compilam como está o estado da arte da área. Após isto, é interessante implementar os algoritmos da área de forma manual para depois utilizar as ferramentas prontas. é sempre bom manter um estudo constante, pois sempre há evolu??es na área.

We are seeing the ethics of data science become more and more important each day. Responsible development AI is trending now, and will continue to be at the forefront of work as AI continues to develop. Along with the ethics of AI there are ethics in data, including its sourcing, known biases, sampling methodology, and implications. There are also ethics in visualizations and analysis. It is easy to create misleading and potentially harmful analytics, whether intentional or not. These ethical considerations are difficult and involve nuance. I have found these to be the hardest transitions between academic and work settings, as data is certainly less curated.