KICK START AS A NEWBIE
Almost every one of us was introduced to this field of automation, with jargon like Artificial Intelligence(AI), Machine Learning (ML), Deep Learning (DL), Data Science/Analytics, etc., which seemed like magic at first sight. I’m no stranger to it, my baby steps too.
The backbone of the course ‘Data Science’ and those magical terms rests on these below topics:
Programming in Python:
Note: The coursework I did in Machine Learning & Deep Learning was based on python, but there are alternatives like R, MATLAB, etc. but python is the most popular choice.
Some key places to concentrate on are:
- Data types and their usage in Python
- Method definition in Python and its usage
- Arithmetic operators and number systems
- Typecasting in Python
- Bit manipulation in Python
Additionally, problem-solving or standard coding for questions is something students are familiar with throughout academia, but scripting the code in a refined, readable manner is very important when you are writing code for projects especially the ones in production. It’s because when you are collaborating on a project, your team members need to be comfortable with your code at first sight. This helps them to improve your code and highly increases the efficiency of your project work.
Libraries for Data Analysis:
Though this isn’t a necessary prerequisite, being familiar with standard basic data analysis libraries would be of great help. Some of the notable ones are:
- Numpy
- Pandas
- Matplotlib
- Scikit learn (Helps for ML concepts)
I made sure that I played around with some of these libraries to know their syntaxes, use cases, and get an overview of them. Trust me, spending quality time with the documentation of these libraries is worthwhile and is the hard but best way to learn them.
[Tip]: Skipping Medicine while you are suffering from any abnormality and skipping the Math part in any field, especially when you are trying to learn new technologies are not so different. So never ignore the maths on which the AI, ML & DL are built.
Despite the fact it seems boring and hard(except for math enthusiasts), because personally, I will say math is boring, it is very crucial in understanding the concepts behind Machine Learning and Deep Learning at a greater depth.
The most important concepts to focus on are:
Make sure you are at least familiar with computing derivatives, graph transformation, matrices, and understand vectorization. Cheat sheets and subject refresher or revision lectures can help you get through the concepts required enough to understand Deep Learning but having said that, subject expertise is advisable.
Probability and Statistics:
It’s obvious math too but deserves to have a separate mention. If you can’t do statistics, properly, then you most probably can’t do Machine Learning or Deep Learning as well properly. These two subjects are the very necessary and most important fundamentals required to excel in this program. Despite the fact most Machine Learning models use bootstrapping to get statistics, you must understand some key concepts like:
- Hypothesis tests in statistics
- Probability & Bayesian inference
- Measures of dispersion, central tendency in statistics
- Frequently used probability distribution curves
- Activation functions
- Types of dependence between numerical variables
Reality will hit us : With all these said, if I or anyone else claims to have mastered all these in one month, it is completely false. All I did was to get familiar by playing around with them so that we understand the Machine Learning and Deep learning concepts from scratch at a greater depth. Consistency is the key and there’s no substitute to practice or shortcuts to become so-called Data Scientists in months.
The message here is that no matter what phase you are in a beginner or an expert you always have something new to learn in this field, so start wherever you are, make use of the resources and do your level best.
“Learning Data Science is like going to the gym, you earn your muscles(skills) only if you work out day in and out and irregularity leads you back to day zero”.
Data Science, Machine Learning, and Deep Learning are not subjects to be studied but are tools that are applied in real-time to solve problems across various domains like healthcare, finance, marketing, policy, and decision-making, etc. So I would love to mention some key points that would help you navigate through your data science journey:
- Do remember that every concept you study was once a research paper, so don’t think that this is beyond my knowledge and I can’t do this. Websites like “paperswithcode” help you to implement and understand the concept in depth. Once you understand how these works, optimistically you can create the next disruptive research paper !!!
- Courses give you theoretical knowledge but projects give you both practical and theoretical knowledge. Having already claimed data science to be a tool, practical knowledge is required to use the tool efficiently than just knowing the facts about it. So always make quality projects to illustrate your learning.
- Open source your projects, even the best Deep Learning models need to be reiterated, similarly, there’s always room for improvement in any work you do, and open-sourcing it would enable anyone interested to work on it. Write medium blogs on your work and give back to the community by teaching your learning as the best way to learn something is by doing it practically and teaching it intuitively.
- Always connect and network with passionate and enthusiastic people who add value to the community in professional platforms like LinkedIn, Github, Stackoverflow, etc.
I’ve given what I can and What I learnt through my journey, I really hope this helps you a lot guys. All the best for you career ahead.
And Guys don’t forget to give it a clap!