AI, data science, machine, and deep learning – these have become buzzwords. They have begun to appear across websites, online blogs, advertisements, product descriptions, user manuals ..you name it! Found in smartphones & across myriad applications, artificial intelligence is finally no longer the stuff of science fiction but a part of our day-to-day lives. Consequentially, there’s such a huge spurt in the demand for skilled data scientists and engineers.
Advanced mathematical concepts lie at the heart of both data science and machine learning. Every DS & ML assignment comes with significantly challenging math problems, compelling many to seek professional math assignment help from post-grad experts. The purpose of this write-up is to provide some help for students so with a thorough recap of fundamental DS & ML math concepts.
Let’s dive in.
Essential Math Topics for Data Science & AI
There’s a lot of math involved in DS and ML. Mathematics is how we humans quantify, dissect, and analyze any kind of information. So, if you are an aspiring data scientist and/or machine learning engineer, then get ready to dive feet-first into the following topics.
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Linear Algebra
This is one of the most heavily employed topics in data analysis and AI. Concepts and techniques from linear algebra are employed all over, right from the classic machine learning algorithms all the way to state-of-the-art large-language models and computer vision systems. Regression, classification, support vector machine, the k-nearest neighbour clustering algorithm – all of these have vector and matrix operations.
Two key concepts to look out for are →
- Vectors, Matrices, & Tensors
1-D vectors, 2-D matrices, and N-D tensors are the most effective ways to store feature-rich data. You will need to work with these three data structures throughout, right from basic machine learning all the way to deep learning and neural networks.
If you frequently find yourself struggling with vectors & tensors, click now on this link right here and place a “Please do my assignment for me” request to connect with leading math experts.
- Eigenvectors & Eigenvalues
Eigenvalues and vectors are techniques to reduce data dimensionality. Major dimensionality techniques such as Singular Value Decomposition and Principal Component Analysis have eigenvalues and vectors to determine the most important features & attributes in a dataset. In other words, you are extracting the most valuable information from a given corpus.
Check out this link to find out more about how linear algebra runs almost every ML algorithm out there.
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Probability
Probability is another central cog in DS and ML. Predictions made by machine learning models are all based on probabilistic calculations that help tackle all the noise & uncertainty latent in information.
Crucial concepts to work on are →
- Conditional Probability & Bayes Theorem
As the name suggests, conditional probability determines the chances of the occurrence of something, given the fact/information that some other dependent or independent event has already occurred. Baye’s theorem allows us to invert conditional probabilities and find out the causes behind an effect. There’s an entire machine learning classifier based on Baye’s theorem called the Naive Baye’s Classifier.
- Random Probability Distributions & Variables
Continuous and discrete probability distributions & random variables are essential to tackle all the randomness and uncertainty intrinsic to real-world events and information.
Here’s a great resource for learning more about the use of probability in data science and AI.
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Statistics
The third central pillar of data science, analysis, and AI is statistics. Statistical techniques are utilised for exploring datasets in-depth as well as inferring more information from available data. The importance of stat concepts is evident from their ubiquity across classical or non-neural network-based algorithms.
Statistics, in itself, is vast in scope. Here are the most crucial concepts to master →
- Measures of Central Tendency
- Variance, Co-variance, Correlation, & Standard Deviation
- Sampling, Errors, & Expectations
- Baye’s Theorem, Conditional Probability,& Random Statistical Distributions,
- Hypothesis Testing, A/B Testing, Confidence Intervals, p-values, ANOVA, & t-tests
And those were three of the most vital topics that are fundamental to almost all aspects of data science and artificial intelligence. They are vast in scope and genuinely challenging to master. So, if you think you could do with some help with your math assignments & assessments in DS & AI, connect with a reputed website online assessment help service today.
We wrap things up with a look at ten of the most popular AI/DS algorithms founded on the above three math topics & their use cases.
Top Ten ML Algorithms & Their Applications
| Linear Regression | Forecasting & Prediction Trends |
| Classification | Categorizing Any Data |
| Decision Trees | Risk Assessment, Medical Diagnosis, Customer Segmentation |
| K-Means Clustering | Segmentation, Anomaly Detection, Data Compression etc. |
| Principal Component Analysis | Feature Extraction from Data, Noise Reduction |
| Logistic Regression | Spam Detection, Fraud Detection |
| Random Forests | Fraud Detection, Recommendation Systems, Image Processing |
| Support Vector Machines | Image Classification, Text Classification, Bio-informatics |
| Naive Baye’s Theorem | Spam Filtering, Sentiment Analysis |
| Gradient Boosting | Fraud Detection, Customer Sentiment Analysis |
And that’s all the space we have for today. Hope this was an interesting and informative read for everyone.
Mastery of data science and machine learning requires a thorough mastery of the math topics mentioned herein and more. Check out this excellent book on mathematics for machine learning that can help you out in your journey toward mastery. And connect with a reputed academic service in case you want some expert help.
