Technology is the proverbial third eye. It enables us to interpret and understand the world & events around us in ways that only a few can achieve. Modern technologies like AI & machine learning are reflections of the sheer brilliance of such brilliant thinkers & inventors.
Scientists & technologists have long dreamed of crafting machines that can think independently. Long the stuff of science fiction, the 1950s and 60s witnessed a dynamic generation of scientists, mathematicians, and polymaths conceive the mathematical representations of learning theory and the mathematical possibility of artificial intelligence. Today, AI & ML systems are integral to all kinds of businesses, from manufacturing industries to online information technology assignment help websites.
It uses mathematical & statistical techniques to quantize the abstract, enabling machines to identify & interpret patterns in data and learn spontaneously from experience without human intervention. Alongside programming, they are the chief prerequisites in ML, making it a challenging domain to master. However, if you aspire to succeed in it, a solid foundation in the prerequisites is a must.
Get a good head start with this article.
What Do You Need To Know To Master Machine Learning?
Want to design your machine learning model? Then you need to be good at your numbers.
Both pure and applied mathematics is central to AI and machine learning. And it is mathematics, the language of nature, that provides the concepts & techniques for representing & quantifying abstract ideas, interpretations & logic.
Unlike generic computational systems, It’s systems do not work with hardwired instructions. This is because machine learning focuses on problems with intuitive solution processes but cannot be defined in distinct formal steps.
To devise effective solutions & construe working models, aspiring machine learning engineers need solid foundations in the following:
A major applied mathematics field, statistics provides the tools & techniques we need to dissect & uncover information from data. In addition, both descriptive and inferential statistics are heavily employed in it for visualization & making predictions.
Statistics is a foundation of data science and machine learning with regression. Regression, statistical distributions, hypothesis testing, random variables, Bayes theorem, etc., are employed for better representation, identifying & extracting features and developing appropriate models.
Crucial statistics fundamentals used in it are:
- Mean, Variance, & Standard Deviations
- Data visualization techniques such as histograms, charts, graphs, etc.
- Probability, random variables, and distributions
- Hypothesis testing
- Bayes theorem
- Set theory
The concepts and methods in linear algebra allow ML engineers to represent data and carry out operations in abstract geometric space. For example, in formulating multivariate equations, creating vectors, matrices & tensors, mapping & transforming datasets, etc. —linear algebra is key to all these operations and much more.
Essential linear algebra concepts that are heavily employed in it modelling include:
- Systems of linear equations
- Methods of solving systems of linear equations
- Vectors, matrices and tensors
- Vector spaces (Euclidean & Non-Euclidean)
- Norms, Determinants, Inverse
- Eigenvalues & Eigen vectors
- Basis and Rank
- Affine Space
- Linear Independence
Linear algebra is used in it concepts such as principal component analysis, loss functions, covariance matrices, support vector machines, singular value decomposition, etc.
Uncertainty is intrinsic to the natural world. Nothing can be predicted with absolute certainty from natural phenomena to the human thinking process. Probability allows us to quantify and tackle such uncertainties with substantial accuracy.
Probability is a vital mathematical concept. It is key to designing ML algorithms that can generate predictions with high accuracy. With the power of probability, models can make predictions even with incomplete information. It utilizes probability holistically, from the fundamental postulates to applied Bayes Theorem, random distributions, mass & density functions, and so on. Statistics and probability are used in tandem in numerous application scenarios.
Vital probability concepts used in it include:
- Probability density estimations
- Likelihood estimations
- Bayes theorem & conditional probability
- Discrete & continuous probability distributions
- Construction of probability spaces
- Sum and product rules in probability
- Central Limit Theorems
Another integral aspect of machine learning, Calculus, comes in handy during the performance optimization of machine learning models. Numerous algorithms in machine learning optimize a particular objective function that tunes model parameters & enables systems to explain some data better.
Univariate and multivariate calculus is utilized in solving optimization problems in ML models. Crucial concepts of calculus employed in machine learning are:
- Differentiation & Integration
- Partial Differentiation
- Calculating the gradients of matrices
- Higher order derivatives
- Multivariate calculus
- Taylor’ series
- Calculus in higher dimensions
And those were the most vital mathematical topics & concepts that form the basis of machine learning. Mastering them all at once is not possible. So, take them on one by one, put in some serious effort, use your grey cells, and, if need be, seek machine learning homework help to develop crack skills.
If you are looking for a good reference, this book, aptly named Mathematics for Machine Learning, is one of the best.
Besides maths and stats, computer programming is another central pillar of it. So, before we round up, here’s a look at the most prominent programming languages used to design machine learning systems.
Programming Prerequisites For Machine Learning
You must learn to code if you wish to craft & implement your machine learning algorithm. The following are currently popular programming languages used to design modern machine learning systems.
Python is currently considered the top programming language for designing machine learning models. It has a very shallow learning curve, effortless & straightforward syntax, and a vast array of libraries & frameworks. NumPy, SciPy, Keras, Natural Language Tool Kit, TensorFlow, MatPlotLib, Open CV, PyTorch and Pandas are potent libraries for different machines, deep learning and natural language processing use cases.
R is one of the chief languages in data science, thanks to its robust data sampling, evaluation, & visualization techniques. R is designed for statistical analysis of large volumes of data and can help streamline the entire engineering process.
Other prominent approaches to machine learning engineers include designing algorithms in languages like C++, Golang, & Shell Scripting in Linux.
As you develop a solid base in mathematics & sharpen your coding skills, it is also essential to build your ideas & understanding of data science & machine learning concepts. Understand the theoretical concepts of machine learning and data science with these handy resources:
- Machine Learning Basic Concepts
- Concepts in Machine Learning
- Notes on Data Mining & Machine Learning
- Introduction To Machine Learning(Department of Computer Science, Stanford University)
- Pattern Recognition & Machine Learning
- Machine Learning: A Probabilistic Perspective
Don’t forget to practice solving associated mathematical & programming problems while mulling over these essential concepts.
And that’s about it for this write-up. Hope it helps you kickstart your machine learning studies effectively & efficiently. This is a demanding domain, and you might need professional assistance often. Seek assistance from services that offer quality machine learning homework help & information technology assignment help.
All the best!