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Various playbooks, roadmaps, and career paths boast of helping you land your first job in ai or transition into the field. However, the automation that comes with ai advances is also putting many jobs at risk.
So how do you make a career in ai, especially in today's generative era?
First of all, it is important to note that the fundamentals of ai are still very necessary to understand how the algorithms work, what are the assumptions of the algorithms, how to debug them if the expected behavior deviates from the actual behavior, the difference between sample and . population, what is the need to collect a sample and the different ways to collect it, perform the hypothesis test, and more.
It is time to act
Great, with this understanding of the fundamentals of ai and its importance even in the GenAI era, let's quickly cover the roadmap to learning ai.
Starting with the fundamental pillars of learning algorithms, i.e. linear algebra, calculus, statistics and probability, you will be equipped with understanding concepts such as what, why and how of derivatives, where they are used and what is forward and backward. approve. You will also solidify your understanding of data distribution and probability distributions, such as Gaussian, Poisson, etc.
Most of this knowledge is available for free; Recommended resources are:
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Now we are ready to learn machine learning concepts that would cover key algorithms including linear regression, logistic regression, decision trees, clustering, and more.
Before we continue, it is important to note that learning ai has become much easier today due to the democratization of education. For example, all of the suggested readings in this roadmap are available for free.
In addition to developing the intuition behind algorithms, it is also important to learn concepts such as cost functions, regularization, optimization algorithms, and error analysis.
At this time, let's also start to get familiar with software programming. Learning how to code and implement the solution will allow you to practice it without problems. The 4-hour Python video course (as shown in the roadmap image) covers the fundamentals to get started from the beginning. Now, we are ready to learn the ins and outs of deep learning by focusing on fundamental concepts including layers, nodes, activation functions, backpropagation, hyperparameter tuning, etc.
Great, having learned enough, we have reached the final stage, which I usually refer to as the playground. This is where you put all your knowledge into practice. A great way to do this is by practicing and participating in Kaggle competitions. One can also find winning solutions and develop an approach to handle various business problems.
ai Workflows
This is a typical path to learning ai, all while one internalizes ai workflows that begin with data exploration, that is, dissecting data to understand underlying patterns. It is during this phase that data scientists learn about data transformations to prepare them for modeling purposes.
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Feature selection and engineering are the most powerful skills of distinguished data scientists. This step, if done correctly, can speed up the model learning process.
Now is the moment every data scientist waits for: build models and select the one that works best. The definition of “best performance” is done through evaluation metrics, which are of two types: scientific precision, recall and mean square error, and the other includes business metrics such as increase in clicks, conversions or impact of the Value in dollars.
Getting to this stage while reading an article seems like an easy process, but in practice it is an extensive process.
differentiator
So far, we've discussed the conventional path, learning what everyone is doing. But where is the differentiator here to stand out in the GenAI era?
A predominant notion among students is to continue consuming learning content. While studying the fundamentals is important, it is equally important to start practicing and experimenting to develop an intuitive understanding of the concepts learned.
Additionally, the crucial component of creating ai solutions is knowing if ai is the right choice, which includes the ability to map the business problem to the right technical solution. If the initial step itself is performed poorly, then the implemented solution cannot be expected to meet business objectives in any meaningful way.
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Furthermore, data science is considered more of a technical function, but in fact, its success quotient depends largely on the often underrated ability to collaborate with stakeholders. Ensuring the incorporation of stakeholders from diverse backgrounds and experience plays a key role.
Even if the model is showing good results, it may not be adopted due to lack of clarity and ability to link it to business results. This gap can be addressed through effective communication skills.
Finally, prioritize data in your approach to ai. The success of any ai model depends on data. Additionally, find ai advocates who believe in the capabilities and possibilities of ai while understanding the associated risks.
With these skills on your side, I wish you a stellar career in ai.
Vidhi Chugh is an ai strategist and digital transformation leader working at the intersection of product, science, and engineering to build scalable machine learning systems. She is an award-winning innovation leader, author and international speaker. Her mission is to democratize machine learning and break down the jargon so everyone can be a part of this transformation.
