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Model vs Algorithm in ML

Machine Learning works with “models” and “algorithms”, and both play an important role in machine learning where the algorithm tells about the process and model is built by following those rules.

Algorithms have derived by the statistician or mathematician very long ago and those algorithms are studies and applied by the individuals for their business purposes.

A model in machine learning nothing but a function that is used to take some certain input, perform a certain operation which is told by algorithms to its best on the given input, and gives a suitable output.

Some of the machine learning algorithms are:

  1. Linear regression
  2. Logistic regression
  3. Decision tree
  4. Random forest
  5. K-nearest neighbor
  6. K-means learning

What is an algorithm in Machine learning?

An algorithm is a step by step approach powered by statistics that guides the machine learning in its learning process. An algorithm is nothing but one of the several components that constitute a model.

There are several characteristics of machine learning algorithms:

  1. Machine learning algorithms can be represented by the use of mathematics and pseudo code.
  2. The effectiveness of machine learning algorithms can be measured and represented.
  3. With any of the popular programming languages, machine learning algorithms can be implemented.

What is the Model in Machine learning?

The model is dependent on factors such as features selection, tuning parameters, cost functions along with the algorithm the model just not fully dependent on algorithms.

Model is the result of an algorithm when we implement the algorithm with the code when we train the algorithms with the real data. A model is something that tells what your program learned from the data by following the rules of those algorithms. The model is used to predict the future result that is observed by the algorithm implementation of small data.

                Model = Data + Algorithm 

A model contains four major steps that are:

  1. Data preprocessing
  2. Feature engineering
  3. Data management
  4. performance measurement.

How the model and algorithms work together in machine learning?

For example:

y = mx+c is an equation for a line where m is the slope of the line and c is the y-intercept, this is nothing but linear regression with only one variable.
similarly, the decision tree and random forest have something like the Gini index and K-nearest having Euclidean distance formula.

So take the linear regression algorithm:

  1. Start with a training set with x1, x2,…, and y.
  2. Find out the parameters c0, c1, c2 with the random variables.
  3. Find out the learning rate alpha
  4. Then repeat the following updates such as c0 = co-alpha +h(x)-y and for c1, c2 also.
  5. Repeat these processes till converged.

when you employing this algorithm, you are employing these exact 5 steps in your model without changing the steps, your model initiated by the algorithm and also treat all the dataset same.

If you want to apply that algorithm to the model, the model finds out the value of m and c that we don’t know, then how will you find out?
suppose you have 3 variables that are having values of x and y now your model will find the value of m1, m2, m3, and c1, c2, c3 for three variables.
The model will work with three slopes and three intercepts to find out the result of the dataset to predict the future.

The “algorithm” might be treating all the data the same but it is the “model” that actually solves the problems. An algorithm is something that you use to train the model on the data.

After building a model, a data science enthusiasts test it to get the accuracy of that model and fine-tuning to improve the results.

This article may help you yo understand about the algorithm and model in Machine learning, In summary, an algorithm is a process or a technique that we follow to get the result or to find the solution of a problem.
And a model is a computation or a formula that formed as an output of an algorithm that takes some input, so you can say that you are building a model using a given algorithm.

 

Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor Flow, IBM Watson, Google Cloud platform, Tableau, Hadoop, time series, R, and Python. With authentic real-time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science roles. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python, Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.

Introduction to Python Programming

What exactly is Python?

Python is a general-purpose, interpreted, and dynamic programming language that belongs to high-level programming language divisions. Python is commonly used for application development because it supports object-oriented programming approach.

Why is Python so well-likely?

The one-line response is ‘widely accessible’ and has a ‘simple syntax’. Yes, not a single programming language has the same level of accessibility and ease of use as Python. Python’s syntaxes give natural language a lot of weight, making it easier to understand and work with, and making it sound more like human communication language. As a result, it has stayed in the top five programming languages preferred by software engineers, application developers, and other techies for the past few decades.
But, since the last few years, crossing the circumference of popularity, Python has become a global craze irrespective of demographic, professional, and generation-related limitations.

Why should you learn Python in 2021?

Python programming is now the fifth fundamental need to live in this real world, after food, water, air, and shelter.
It may sound diplomatic or even crazy, but it’s the fact. Let’s have an inside look at why it’s so important to learn Python?

    • Use of Python is industry-independent:-Every industry needs Python. Most industries like BFSI, healthcare, sales and marketing, even education and research industries are becoming highly dependent on python programming.The example I will give here will certainly make you realize how powerful Python programming industrial reaches.
      Skilled growth marketers, who once relied solely on simple analytical skills and advanced Excel expertise, now have little value in the marketing industry unless they know Python.
      Yes, nowadays, python-driven researches are used to make the majority of growth marketing decisions. As a result, Python is clearly embedded in every area of the industrial sector, regardless of domain.
      You can visit python.org’s Success story page to the ultimate power of Python across multiple industries.
      But why the sudden popularity?
    • Python is a foundational language of data science:-Data science- the major cause of Python’s cross-industrial demand surge.
      Initially, R used to be the backbone of data science. Still, with the advancement of data science and its applicability, the complexity of R became a sound barrier for the data science field. Simplicity and a wider range of accessibilities promoted Python as the successful replacement of R in the world of data science.
      With the advancement of AI and Machine Learning, Python achieved more credibility within the last few years. The most widely used software and application packages like ‘pandas’, ‘NumPy’, ‘Matplotlib’, ‘pyspark’, ‘Keras’, ‘Scikit-learn’, ‘PyTorch’, etc., all are developed using python programming.
      Such applications and libraries are very efficient for handling larger amounts of data, even by non programmers. As these are inbuilt libraries within Python, other than the application usage knowledge, no core programming or coding proficiency is needed. These applications have strong algorithmic abilities, making a person with only basic knowledge of statistics and complex mathematics impressively eligible to do a data analytics job. This is why Python is called ‘A programming language built for everyone’ and is making the future of data science.
  • It’s going to be most demanding skills in the future job market:-While data science has become the hottest topic in the global job market, the demand for python programmers are increasing silently.
    Why so?
    Well, everybody is now focusing on the data science career switch. Professionals without technical backgrounds target the basics of python programming and jump into the data science tools and technologies. But from where are these tools technologies coming?
    Yes, these are the output of so many python programmer’s hardships. Hence, while the key focus is now on data scientist and data scientist courses, the hidden job opportunities are very high for python programmers. On LinkedIn.com, at present, almost 528,242 python jobs are available worldwide. The number is increasing by at least 10% every day.

    So, suppose you hold a technical background or a student. In that case, it’s best for you that you focus on earning experience (for working professionals) or degrees in core python programming (for students).

  • Python programmers are well compensated.
    Python developers earn around 7 to 18 lakhs/year (6 t0 ten years of experience) as base salary with additional compensation of almost the same amounts in India. This is quite high in comparison to other programming developers like PHP, Java, C++, etc. Even SMEs are offering an average of 2 to 3 lakhs salary package to freshers.

What are the uses of python Software?

Python software can be used in a variety of ways, as previously mentioned.The fields and tasks for which python software is widely used are listed below.

  • Web development
  • 3D CAD application
  • Machine learning
  • Scripting
  • Analysis data
  • Processing of image
  • Artificial Intelligence
  • Speech Recognition
  • Development of Software
  • Data mining
  • Creation of Desktop and mobile application
  • Development of Games

Features of Python

  • It’s simple to understand, read, and write.
    Syntaxes used in python programming are more like natural language/ human language (like English). For example, to print ‘Learnbay- The Data Science & AI Institute, ‘we have to just type print(“Learnbay- The Data Science & AI Institute”) in python editor or any IDLE.
  • Smart in memory management
    Python offers you a stress-free programming experience concerning memory. This programming software comes with efficient auto-memory management features that periodically clean the memory by itself.
  • Free to use and open source
    No purchasing cost or subscription cost is required for downloading and using Python.you can use Python at zero cost for a lifetime. And due to its open-source licensing features, you can share this software, among others. Even, can modify the source code as per your project requirement.
  • Cross-platform performance
    Python has compatibility with most operating systems like Windows, Linux, Mac, Unix, etc.
    Until now, you have learned plenty of basic information about python programming. So, you might have a question in mind when you are aware of the ease of use.

Can I learn Python on my own?

Yes, of course. Plenty of python learning videos are now available over the internet. A few of the reliable options are Codecademy, Learnpython.org, LinkedIn python learning courses, etc. you can download Python for free here .
These are only for gathering a basic stage of knowledge. If you have data science career transition planning, you must choose a creditable course featured with real-time industrial projects.

You can check our sample python programming class for AI video.

Win the COVID-19

If you slightly change your perspective towards the lock-down situation you can find hope of this pandemic to end and can hope of a brighter than ever future. Go for Data Science, it will be worth it.

text stemming in NLP

Human language is an unsolved problem that there are more than 6500 languages worldwide. The tons of data are generating every day as we speak, we text, we tweet, from voice to text on every social application and o get the insights of these text data we need technology as NLP. If you know there are two types of data are there one is structured and unstructured data. Structured data used for Machine learning models and unstructured data is used for Natural language processing. There are only 21% of structured data is available, so now you can estimate how much NLP is required to handle unstructured data. 

To get the insights of the dataset of unstructured data to take out the important information from it. The important technique to analyze the text data is text mining. Text mining is the technique to extract useful information from the unstructured data by identifying and exploring a large amount of text data. Or we can say that text mining is used to convert the unstructured data to the structured dataset.

Normalization, lemmatization, stemming, tokenization is the technique in NLP to get out the insights from the data.

Now we will see how text stemming works?

Stemming is the process of reducing inflection in words to their “root” forms such as mapping a group of words to the same stem. Stem words mean the suffix and prefix that have added to the root word. It is the process to produce grammatically variants of root words.  A stemming is provided by the NLP algorithms that are stemming algorithms or stemmers. The stemming algorithm removes the stem from the word. For example, eats, eating, eatery, they are made from the root word “eat“. so here the stemmer removes s, ing, very from the above words to take out meaning that the sentence is about eating something. The words are nothing but different tenses forms of verbs.

This is the general idea to reduce the different forms of the word to their root word.
Words that are derived from one another can be mapped to a base word or symbol, especially if they have the same meaning.

As we can not sure that it will give us a 100% result so we have two types of error in stemming they are: over stemming and under stemming.

Over stemming occurs when there are too many words have cut out.
This could be known as non-sensical items, where the meaning of the word has lost, or it can not be able to distinguish between two stems or resolve the same stem where they should differ from each other.

For example, take out the four words university, universities, universal, and universe. A stemmer that resolves these four stems to “Univers” that is over stemming. It should be the universe stemmer that stemmed together and university, universities stemmed together they all four are not fit for the single stem.

Under stemming: Under-stemming is the opposite of stemming. It comes from when we have different words that actually are forms of one another. It would be nice for them to all resolve to the same stem, but unfortunately, they do not.

This can be seen if we have a stemming algorithm that stems from the words data and datum to “dat” and “datu.” And you might be thinking, well, just resolve these both to “dat.” However, then what do we do with the date? And is there a good general rule? So there under stemming occurs.

Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor Flow, IBM Watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real-time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science roles. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python, Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.

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Note : Our programs are suitable for working professionals(any domain). Fresh graduates are not eligible.