Lesson 27 of 31By Avijeet Biswal

Last updated on Mar 17, 202118209#### Machine Learning Tutorial: A Step-by-Step Guide for Beginners

Overview#### An Introduction To Machine Learning

Lesson - 1#### What is Machine Learning and How Does It Work?

Lesson - 2#### The Complete Guide to Understanding Machine Learning Steps

Lesson - 3#### Top 10 Machine Learning Applications in 2020

Lesson - 4#### An Introduction to the Types Of Machine Learning

Lesson - 5#### Supervised and Unsupervised Learning in Machine Learning

Lesson - 6#### Everything You Need to Know About Feature Selection

Lesson - 7#### Linear Regression in Python

Lesson - 8#### Everything You Need to Know About Classification in Machine Learning

Lesson - 9#### An Introduction to Logistic Regression in Python

Lesson - 10#### Understanding the Difference Between Linear vs. Logistic Regression

Lesson - 11#### The Best Guide On How To Implement Decision Tree In Python

Lesson - 12#### Random Forest Algorithm

Lesson - 13#### Understanding Naive Bayes Classifier

Lesson - 14#### The Best Guide to Confusion Matrix

Lesson - 15#### How to Leverage KNN Algorithm in Machine Learning?

Lesson - 16#### K-Means Clustering Algorithm: Applications, Types, Demos and Use Cases

Lesson - 17#### PCA in Machine Learning - Your Complete Guide to Principal Component Analysis

Lesson - 18#### What is Cost Function in Machine Learning

Lesson - 19#### The Ultimate Guide to Cross-Validation in Machine Learning

Lesson - 20#### An Easy Guide to Stock Price Prediction Using Machine Learning

Lesson - 21#### What Is Reinforcement Learning? The Best Guide To Reinforcement Learning

Lesson - 22#### What Is Q-Learning? The Best Guide to Understand Q-Learning

Lesson - 23#### The Best Guide to Regularization in Machine Learning

Lesson - 24#### Everything You Need to Know About Bias and Variance

Lesson - 25#### The Complete Guide on Overfitting and Underfitting in Machine Learning

Lesson - 26#### Mathematics for Machine Learning - Important Skills You Must Possess

Lesson - 27#### A One-Stop Guide to Statistics for Machine Learning

Lesson - 28#### Embarking on a Machine Learning Career? Here’s All You Need to Know

Lesson - 29#### How to Become a Machine Learning Engineer?

Lesson - 30#### Top 34 Machine Learning Interview Questions and Answers in 2021

Lesson - 31

Mathematics is one of the most crucial prerequisites for becoming an expert in Machine Learning. It is a foundational skill that you need to possess for working with machine learning algorithms. In this article, you will explore the importance of mathematics for machine learning and cover the skills you need to know in order to enhance your career in machine learning.

You will learn the topics mentioned below, in this article on Mathematics for Machine Learning:

- Importance of Mathematics for Machine Learning
- Statistics and Probability
- Linear Algebra
- Calculus

Expertise in mathematics is necessary to understand and apply algorithms in various applications. From choosing the right algorithm to selecting the correct parameter, it uses mathematical concepts in every step of a machine learning process. Other reasons include choosing an efficient training time, complexity, and bias in variance trade-off.

Now, let’s discuss the important skills that you need to know to master mathematics for machine learning.

Statistics and Probability form the core of data analytics. They are widely used in the field of machine learning to analyze, visualize, interpret data and discover insights. Statistics and probability have found a broad range of applications in several industries. It uses the techniques in statistics and probability theory while collecting, preprocessing, and manipulating data.

Below are some of the topics that you need to know:

- Descriptive Statistics
- Hypothesis Testing
- Regression Analysis
- Probability Distributions
- Conditional Probability
- Sampling and Central Limit Theorem
- Bayes Theorem

Click on the following link to learn more about Statistics and Probability for machine learning: Statistics & Probability for Machine Learning

In the field of machine learning, the concepts of linear algebra come up everywhere. It is another foundation skill required to become an expert in machine learning. To understand how each algorithm works, you need to know linear algebra. Concepts in linear algebra help implement machine learning algorithms from scratch. This includes working with vectors and matrix operations in n-dimensional space.

The following topics are really important:

- Vectors and Matrix Properties
- Matrix Transpose and Inverse
- Determinants
- Dot Product
- Eigenvalues and Eigenvectors
- Matrix Factorization
- Principal Component Analysis
- Orthogonality

Watch the following video to learn the above concepts of Linear Algebra: Linear Algebra for Machine Learning

Knowledge of calculus is very important to understand crucial machine learning applications. You might have to revisit high-school mathematics. Machine learning uses the concepts of calculus to formulate the functions that are used to train algorithms. Machine learning models are trained with datasets having multiple feature variables. Hence, getting familiar with multivariable calculus is important for building a suitable model.

Here are key topics that will help you:

- Differential and Integral Calculus
- Limit, Continuity and Partial derivatives
- Step, Sigmoid, Logit, and ReLU Function
- Maxima and Minima of a Function
- Product and Chain Rule

Here is a video link that will help you understand calculus better: Calculus for Machine Learning

Apart from the above mathematical skills, you should also have good expertise with algorithms and optimization methods. Knowledge of the working of gradient descent algorithms and familiarity with cost function and likelihood function is also critical. Other areas needing to focus on are data structures and discrete mathematics.

Mentioned below are some of the topics you should know:

- Arrays
- Linked List
- Stack
- Queue
- Sorting Algorithms
- Binary Search
- Heap Hashing
- Set and its Properties
- Graph Theory

Looking forward to becoming a Machine Learning Engineer? Check out the Machine Learning Certification Course and get certified today.

After reading this article, you would’ve understood why mathematics plays a key role in machine learning. Additionally, the skills you need to know, to master machine learning algorithms and build cost-effective models were also made clear. You also learned the different topics that are needed for a better understanding of machine learning.

Do you have questions related to this article on Mathematics For Machine Learning? If you do, then please put them in the comments section. Our team will be happy to help you.

To start your career in machine learning, click on the following link: PG Program in AI and Machine Learning

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Avijeet is a Senior Research Analyst at Simplilearn. Passionate about Data Analytics, Machine Learning, and Deep Learning, Avijeet is also interested in politics, cricket, and football.

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