Thermodynamics is a sub-branch of physics which deals with the processes of heat and energy in the universe. 

It is a fundamental aspect of our universe and has two rigid laws. Namely, the law of conservation of energy and the law of entropy. 

Law of conservation of energy:

Law of conservation of energy states that the energy in a closed system (Universe is a closed system) remains constant, in that it doesn’t increase or decrease but only transforms.  

This has a lot of implications for the real world, for instance, this law is the reason we can’t have perpetual motion machines because they’d have to be creating work without the input of energy (creating energy out of nothing)

But in the context of this article, it’s the law of conservation of mass that is important, it’s a direct translation of the law of conservation of energy because of relativistic physics. 

Relativistic Physics says that mass is simply a form of energy in a rest frame, meaning that as long as energy is not hitting the speed of light, it remains in the form of mass. 

So if mass is simply just another form of energy, that means, the law of conservation of energy also applies to mass and this is where the law of conservation of mass comes from. 

Law of Entropy:

The Law of Entropy, on the other hand, is the 2nd law of thermodynamics which says that the entropy of a closed system will only always increase and never decrease. 

But what really is entropy! It’s a fundamental property of the universe that determines the spontaneity of physical processes. 

Now the fact that it determines spontaneity and always increases in a closed system, means that it’s a vector property and this is what determines the arrow of time in the forward direction in our universe.  

How does thermodynamics relate to chemical reactions:

You might be wondering what on earth does thermodynamics have to do with chemical reactions. Well we’re here to tell you that it has to do a lot actually. 

First consider the fact that chemical reactions involve chemical compounds which have mass. This alone ensures that the first law of thermodynamics is involved in chemical reactions. 

Because the chemical compounds have mass, that means it needs to be conserved in a chemical reaction. This is where equation balancing comes in. 

You can’t design a chemical reaction if you do not balance the chemical equation because otherwise, that reaction would be useless as it wouldn’t be happening in nature. 

What does balancing mean exactly? Balancing a chemical equation means that you conserve the mass of reactants in the products they form. So what you do is you ensure there are the same number of coefficients (atoms) of chemical compounds on both sides of the equation even though configurations would obviously be different. 

Let’s take an example: 

S8 + F2 = SF6

This is an unbalanced chemical reaction. So how do we balance it? If we begin by balancing the fluorine, we know that just when we try to balance the sulfur, we’d have to balance the fluorine again. 

So instead, we begin with the sulfur. As you can see in the equation above, there are 8 sulfurs in the right hand side of the equation but only 1 on the left side, so we put an 8 with SF6.

S8 + F2 = 8SF6

And now we see that there are 48 fluorines on the right hand side, but only 2 on the left side. So what we do is we put the number 24 with F2 on the left side. 

Then we get, 

S8 + 24F2 = 8SF6

And now, the equation is perfectly balanced, there are equal numbers of sulfur and fluorine on both sides of the equation and the mass is conserved. This is how the reaction would have happened in nature and no other way.

Talking about the second law of thermodynamics now, and its effect on chemical reactions, you need to understand that the only reason the reaction is happening in a way that the reactants are producing the products rather than products producing reactants is because of entropy’s vector properties. 

Entropy causes things to move only forward in direction and not backwards. This is how entropy increases. A backward direction would decrease entropy which is forbidden by the 2nd law of thermodynamics.

This might sound obvious and for granted but the world would have been a weird place if not for entropy.

Many people are perplexed as to what the distinction between machine learning and statistics is. Machine learning and analytics have almost identical goals. However, there is a significant gap between the two in terms of data volume and human participation in the modeling process. The most common application of statistics and machine learning is to estimate a population region. Supervised learning, forecasts, and other aspects of machine learning are all covered. The knowledge of data collection, research, interpretation, statistics assignment help results, and design is referred to as statistics. We’ve gone through the differences between statistics and machine learning in detail in this report. Let’s start with an overview.

What is Machine Learning?

Machine learning, the subset of artificial intelligence in which you train the machine on itself and then use the results to make predictions. Machine learning is simply the process of using algorithms to train data. For most data analysts, it’s still a black box at times. You’re teaching the machine (computer or model) the rules you’ve developed (data points).

What is Statistics? 

Statistics, the branch of mathematics in which mathematical solutions are used to extract patterns in data. Statistics is purely mathematical. Some geometrical patterns can be defined and extracted using mathematical practices to extract insights or correlations between the data (statistics). Statistics play a role in identifying the trend.

Let’s go over Machine Learning and Statistics in greater depth.

It is essential to comprehend the data and recognize any similarities or trends before feeding it to the computer. If there is a correlation between two or more data points, the prediction is more likely to be right. The majority of companies in the artificial intelligence field are now pushing toward automation and robotics. Statistics, linear algebra, probability, and geometry are the foundations for leading such domains. This is since every data-related problem can be solved using mathematics.

In terms of machine learning and statistics, the statistician constructs descriptive statistics or statistical modeling. On the other hand, machine learning is concerned with a hypothesis, a classification that necessitates knowledge of basic programming, data structures, and algorithms.

Machine Learning and Statistics: What’s The Distinction?

1. Machine learning, the subset of data science or analytics that contributes to artificial intelligence and automation. Statistics,the branch of mathematics in which these solutions are applied to data, resulting in predictive modeling and other applications.

2. Statistics is the foundation for every machine learning model. Machine learning is one of the fields of data science. EDA (exploratory data analysis) is needed to construct the model, with statistics playing a key role.

3. To create a model, the first step is to do feature engineering, which entails deciding which attributes to use and attributes have the best results in terms of maximum probability. A link between the independent variables or data points must be established in order to extract the appropriate features.

4. The terms “machine learning” and “statistics” are not interchangeable. They are both connected to one another. You can’t construct a model without statistics, and there’s no point in doing statistical analysis on the data if you can’t build a model. It contributes to the development of the model.

5. After the model has been developed, statistics are used to assess the output and evaluate the outcomes. Many assessment metrics is built-in data science to assess the results. Confusion matrix algebra, for example, is used to derive true positives, false negatives, true negatives, and false positives.

6. Machine learning and statistics are linked so that one leads to the other in terms of applications.

7. Statistical analysis and machine learning have teamed up to apply data science to a data problem or extract information from data, resulting in a greater effect on sales, industry, and marketing.

8. Machine learning is a subset of data science or analytics that contributes to artificial intelligence and automation. Statistics is a branch of mathematics in which these solutions are applied to data, resulting in predictive modeling and other applications.

Machine learning vs statistics 

Conclusion 

In this, We’ve gone through the main differences between machine learning and statistics, as well as where these two can be used. Machine learning and statistics both contribute to Data Science, but they have different goals and contribute in different ways. Statistics vs Machine Learning knowledge necessitates a deeper understanding and explanation. Though the tactics and arguments can be similar, the goals are rarely the same.