Top 10 mistakes to avoid while learning Artificial Intelligence – Learnxt

Top 10 mistakes to avoid while learning Artificial Intelligence

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Top 10 mistakes to avoid while learning Artificial Intelligence

It is quite generic to make mistakes while learning new skills or getting into a new field. The same goes for machine learning algorithms required to build specific models. Most professionals often get bounded and wonder what to do next and commit common mistakes, especially budding ones.
This blog will discuss some common mistakes that most working professionals commit while working with Artificial Intelligence and machine learning algorithms. Hopefully, after reading this, you will pay more attention to your assigned tasks and not repeat common mistakes. 
Let’s get started!
Spending too much time learning concepts instead of practical Application:
Since your childhood, you must have heard that playing is as essential as studies; the same holds in for Artificial Intelligence and Machine Learning. Learning too much theory and not applying it to master your skillset is not good. The theory is designed as keeping in mind the conditions, but these do not hold practices with real-time problems. For instance, you learn to apply a specific algorithm to solve a problem, and the algorithms require some practical based experience along with learning theories. But in real life, practical learning is missing, and then this would be a challenge for a professional to solve a problem at hand. So your learning process should be a blend of both theory and practice. Whenever you try to learn something, try to find the data set and apply it right over there. 
Directly diving into machine learning and some fancy Algorithms:
Let’s just clear a common myth that machine learning is not what data science offers. It is a process that starts with understanding a particular problem and collecting relevant data to deliver solutions for it. In this, machine learning is a small part that helps make predictions or significant decisions with data. But many people dive into the machine without knowing its importance that should not be the case. If you are willing to make a career in Machine learning, basic mathematics knowledge is fundamental. Secondly, one should pay proper attention to solving problems rather than applying fancy algorithms.
Trying to grab everything quickly:
This is one of the common mistakes that often every data scientist makes. As there is a saying slow and steady wins the race; focus on the current scenario and do not puzzle everything, or this will end up in utter confusion. Try to find an area that gives you joy within the data science domain and try to gain practical working experience. 
Jumping to conclusions without proper Validation:
We have seen data scientists’ straight coming to conclusions without validating proper prediction or analysis. Perform proper hypotheses before coming to any conclusion. 
Neglecting Data Cleansing and EDA, Visualizations:
Most Data Scientists skip the concept of data cleaning, EDA and Visualisations, and directly moves to data modelling. Understanding the data and making it usable for modelling is a perfect approach; thus, a lot of attention should be given to this to be a successful data scientist. 
Practice for Accuracy:
Accuracy is not what every business or client desires. However, the thought process and data behind creating a particular model should be at least 95% accurate; else the client rejects the analysis. Accuracy is paramount, but interpretability has its value. Just try to understand how accuracy fits in a particular domain of a business problem and whether it is more interested in results or understanding that problem and factor. 
More attention is given to tools instead of problems in hand:
Tools in data science play a very crucial role but solving a problem is much more critical. Tools make life more comfortable and enable one to quickly perform the task; hence, one should not pay much attention to tools’ usage. For instance, one should not try to show their machine learning skills everywhere. Having a good knowledge of tools and libraries is excellent, but this will not take you so far until it is combined with solving the real-business problem in a particular domain. This is the fundamental role of a real Data Scientist. Search for datasets in a specific industry and try to work on them; this will create an immense impact on your resume.
 Wondering Communication Skills are not required:
Communication skills are one of the most underrated and least talked about when becoming a data scientist. You can learn all the data modelling and statistics and make the best graphs, but you will fail as a Data Scientist if you cannot convince a particular client. If you are working in an organisation, then you need to coordinate with fellow team members. Ensure you closely work on your communication skills and practice with a person in your sales or marketing who handles the client; this will help you in the long run. 
Insufficient research on the problem at hand:
Many problems take much more time than they should because of a lack of research. People tend to jump on solving without researching about it well before. Consult your seniors and understand the process flow first. 
 Giving too much time to Coding only:
Being a data scientist is more about solving problems than practicing coding. So coding is essential in data science but is not a mandatory skill. 
ML and AI Algorithms with SRM Group and LEARNXT will take you a long way
Considering the momentum gained by machine learning and artificial intelligence in the current scenario, SRM, in collaboration with LEARNXT, offers various degree and PG Diploma courses in AI and ML. These courses’ primary focus will be on equipping the principle concepts of MLAI through latest Data Science curriculum aligned with industry needs & experiential learning.
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