You’re considering a Machine Learning career path. What are the most important things to keep in mind?

Have a solid foundation in core subjects like math, statistics, programming, and computer science. Machine learning relies heavily on quantitative skills and coding ability. Build expertise through relevant coursework and projects. Gain practical experience by applying machine learning techniques to real-world datasets and problems. Theory alone is not enough - hands-on practice is crucial for developing intuition. Develop skills beyond just modeling. Data cleaning, feature engineering, model deployment, and model monitoring are equally important parts of the workflow. Build a portfolio of completed projects to showcase during interviews. Projects demonstrate relevant capabilities better than courses or certifications alone.

Feature engineering is the process of using domain knowledge to extract and select the most relevant features from raw data to improve the performance of ML models. This step is crucial because the quality and informativeness of the features significantly influence the model's ability to learn and make accurate predictions. Despite the rise of deep learning, where models can automatically extract features from raw data, understanding the underlying principles of feature engineering remains essential. It exemplifies the blend of mathematical, statistical, and programming skills needed in ML.

Anyone considering a career path in MACHINE LEARNING should consider the following Skills and Personality Traits: Important Skills: Programming Proficiency Mathematics and Statistics Data Handling and Processing Machine Learning Algorithms Data Visualization Domain Knowledge in Application Area Model Evaluation and Optimization Development and Integration into production systems Personality Traits: Curiosity Analytical Thinking Adaptability Persistence Communication Skills Team Collaboration Ethical Considerations Problem-Solving Attitude Continuous Learning I hope it helps, thanks.

The intersection of art and technology, demonstrating how ML can be applied creatively beyond traditional applications. For those interested in computer vision, mastering neural style transfer not only requires knowledge of image processing and CNNs but also offers a unique way to explore artistic expression through technology. This example highlights the breadth of possibilities within each ML specialization, encouraging learners to delve deep into their interests and discover innovative ways to apply ML techniques.

Start with Kaggle or DrivenData and look for open projects with datasets -> Build your project and keep committing your changes and finally upload on Github public repository -> Write a detailed insights and methods on Medium -> Share a short post about it on LinkedIn for reaching a broader and relevant professionals. You can always get creative in any of these steps but make sure to share it on Linkedin

Hands-on learning is the best way to fuel your ideas and be more innovative in your area of expertise. Actively engage in practical projects, tackle real-world problems, and experiment with different tools and techniques. The insights gained from hands-on experience are invaluable in solidifying your understanding and enhancing your problem-solving skills

Keep in mind that the field is ever-evolving. Choosing a career in ML means committing to continuous learning and staying current on the latest developments. You can do this by reading the latest research papers, leveraging platforms like LinkedIn, and following blogs to stay informed about the newest trends. There are also a lot of tutorials and short lectures that discuss recent updates in the field. While staying on top can be challenging at times, if you enjoy what you do, it will be a fun and rewarding journey!