The fourth step is to become an expert in time series models and solve real forecasting projects. The fifth step involves solving industry projects with real data and complexities, similar to an on-the-job Data Scientist. The sixth step is to master new age computer vision and natural language processing and build your own applications. The final step is to learn how to prepare data for ML model building and drive actionable business insights from the data using extensive EDA.
Key takeaways:
- The first step to becoming a machine learning expert is to become proficient in data science coding and practice real use cases.
- Mastering machine learning concepts, algorithms, and applications is the second step.
- The third step involves learning how to deploy models into actual applications and building your own AI product.
- The final steps involve becoming an expert in time series forecasting, solving industry projects with real data, mastering deep learning, and learning how to prepare data for ML model building and driving actionable business insights.