Introduction :

Three key steps of career growth are learning foundational skills, working on projects (to deepen your skills, build a portfolio, and create impact), and finding a job.

Learning: beyond the foundations, keeping up-to-date with changing technology is more important in AI than fields that are more mature.

Job: Many companies are still trying to figure out which AI skills they need, and how to hire people who have them.

Learning :

More research papers have been published on AI than anyone can read in a lifetime.

For a good career you will need :

• Beyond specific models, it’s even more important to understand the core concepts behind how and why machine learning works, such as bias/variance, cost functions, regularization, optimization algorithms, and error analysis.
• Deep Learning has become such a large fraction of machine learning that it’s hard to excel in the field without some understanding of it! It’s valuable to know the basics of neural networks, practical skills for making them work (such as hyperparameter tuning), convolutional networks, sequence models, and transformers.
• For math, Key areas include linear algebra (vectors, matrices, and various manipulations of them) as well as probability and statistics (including discrete and continuous probability, standard probability distributions, basic rules such as independence and Bayes’ rule, and hypothesis testing). In addition, exploratory data analysis (EDA) — using visualizations and other methods to systematically explore a dataset — is an underrated skill. I’ve found EDA particularly useful in data-centric AI development, where analyzing errors and gaining insights can really help drive progress!
• You will also need some software development skills, even though is not essential, it still really useful in the job market, these skills include programming fundamentals, data structures (especially those that relate to machine learning, such as data frames), algorithms (including those related to databases and data manipulation), software design, familiarity with Python, and familiarity with key libraries such as TensorFlow or PyTorch, and scikit-learn*.*

How to learn: A good course — in which a body of material has been organized into a coherent and logical form — is often the most time-efficient way to master a meaningful body of knowledge.

How much math do you need to succede in machine learning ?

Understanding the math behind algorithms you use is often helpful, since it enables you to debug them. But the depth of knowledge that’s useful changes over time. As machine learning techniques mature and become more reliable and turnkey, they require less debugging, and a shallower understanding of the math involved may be sufficient to make them work.

Projects :