Let’s cook up a feast of machine learning delights today! To create a great dish, we always need the key ingredients to make it truly flavorful. To make a data science recipe (i.e., project), you will need the key ingredient called mathematics.
Anyway, we don’t use the math directly when we make projects, but it is underlying. All the algorithms that we use are explicitly based on mathematics. By knowing the required math, we can understand all the nuts and bolts of the algorithms. We will discuss in detail why we have to learn mathematics to master the algorithms in another section (Why is mathematics essential for data science?).
MISCONCEPTION ABOUT MATHEMATICS:
Many people find math either boring or horrifying; yes, I can resonate with that because I used to feel the same way. But not until I started to learn it in the right way. Yes, math can be boring when we study it for no reason. But when we study math with an objective and know that we are going to apply it to solve real-world problems, math becomes really interesting and exciting.
LEARN IT IN THE RIGHT WAY. I personally know many people who do differentiation and don’t even know what a derivative is. Don’t just skim through the formulas and methods; learn it properly from the core. That is when math really gets interesting.
This way of learning will change your whole perspective on mathematics. I know it is a tedious process to learn and process all the math in our brains, but eventually, you will fall in love with the process and start enjoying it. Let us see what topics need to be covered.
TOPICS TO BE COVERED :
Well here is a good news for you! there is no need of mastering all the math in the world only specific topics are sufficient to master data science. So the topics that are needed to be covered are.
Linear Algebra
Multivariate Calculus
Statistics
Probability
Master these areas in mathematics and you are good to go. Now let us see why are the important and how do they play a significant role in data science.
WHY IS MATHEMATICS ESSENTIAL FOR DATA SCIENCE?
Ok, let me be honest we don’t need any mathematics to do any machine learning or deep learning, yess true all we do is nothing but importing a package we require use it to clean the data process the data and we simply train the data, sounds soo simple right. You can ask me so why bro what is this fuss all about? To become a good engineer or data scientist or anything related to the domain, you need to understand all the concepts underlying.
Let us see what are the significance of the topics given above:
Linear Algebra: Linear algebra is the backbone of data science, providing the mathematical framework for understanding and manipulating datasets, particularly in operations like matrix transformations and vector spaces. It’s essential for developing algorithms in machine learning, data modeling, and optimization.
Multivariate calculus: Multivariate calculus plays a crucial role in data science by enabling the optimization of machine learning models, particularly through techniques like gradient descent. It helps in understanding how changes in multiple variables simultaneously affect a function, which is essential for model training and fine-tuning.
Statistics: Statistics is fundamental in data science for making sense of data, allowing for the collection, analysis, interpretation, and presentation of data. It provides the tools for hypothesis testing, probability estimation, and drawing meaningful inferences from datasets, which are critical for making data-driven decisions.
Probability: Probability is key in data science for quantifying uncertainty and predicting outcomes. It underpins algorithms like Bayesian networks and is essential for modeling random processes, evaluating risks, and making informed decisions based on data.
RIGHT RESOURCES TO LEARN FROM:
In this section, I will provide you with the free resources that I personally followed to master all the math required. Anyway, I’m not a math expert in the first place, so you can give them a try and see if they work for you. If they do, great; if not, explore more and figure out your own way that works for you.
LINEAR ALGEBRA:
The Essence of Linear Algebra by the channel 3blue1brown, this playlist provides you in depth intuition of linear algebra to its core. This helps you to understand all the other advanced topics required.
Then go for M4ML — Linear Algebra by imperial collage longdon, this playlist help us to understand how the linear algebra is used specifically in data science.
MULTIVARIATE CALCULUS:
The Essence of Calculus by the same channel 3blue1brown, this playlist also helps you build strong fundamentals in calculus. (Fun fact: Every animation on this channel is crafted entirely with code!)
Continue it with Multivariate Calculus — Full Online Specialism, they explain some of the core concepts to understand the underlying mechanism in deep learning.
STATISTICS:
For learning statistics I suggest you to visit this playlist STATISTICS FOR MACHINE LEARINING by Krish Naik this playlist contains all the required statistics required for machine learning.
PROBABILITY:
You can learn the probability also from the same channel KrishNaik.
After completing all these playlists follow it up with these books
1.Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : This is one of the best books for learning all the algorithms and this books provides immense knowledge about the libraries scikit-learn and tensorflow in depth.
2.MATHEMATICS FOR MACHINE LEARNING : I prefer this book for the people who have a good intuition in mathematics because this book contains a lot of technical terms which are pretty hard to understand for a new learner.
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