Why Machine Learning is Important for Our daily life

Techshanvia @Techshanvia · Jan 22, 2025

Introduction of Machine learning

NOW IS THE TIME FOR "big data." Companies used to be the only ones with data. In the past, that data was processed and stored in computer centres. We all became data producers, first with the introduction of personal computers and then with the broad use of wireless communications. We are creating data every time we purchase a product, rent a movie, browse the internet, write a blog, post on social media, or even just go for a stroll or drive. Everybody is both a producer and a consumer of data. We want services and goods that are tailored to our needs. We want our interests to be anticipated and our needs to be recognised.

Consider a grocery chain, for instance, that sells thousands of items to millions of consumers through hundreds of physical locations around the nation or an online site. Each transaction's details are saved, including the date, customer ID, items purchased and their prices, total amount paid, and so on. Every day, this usually adds up to a significant amount of data. To increase sales and profit, the grocery chain wants to be able to forecast which customers are most likely to purchase certain products. In a similar vein, every consumer seeks to identify the product set that best suits their requirements.

This work is not clear. We have no idea who is most likely to purchase this flavour of ice cream, read this author's next book, watch this new film, go to this place, or click on this link. Consumer behaviour varies throughout time and by region. However, we are aware that it is not entirely arbitrary. Individuals do not visit supermarkets and make haphazard purchases. They purchase chips when they purchase beer, ice cream during the summer, and Glühwein spices that are protected by copyright in the winter. The data exhibits specific trends.

An algorithm is required in order to solve a computer problem. An algorithm is a set of steps that must be followed in order to convert input into output. For instance, an algorithm for sorting can be created. The output is an ordered list of the input, which is a collection of numbers. There may be different algorithms for the same task, and we would want to identify the most effective one that uses the fewest instructions. either memory or both.

However, we lack an algorithm for some tasks. It's one thing to predict customer behaviour; it's another to distinguish between spam and legitimate communications. We are aware that the input is an email document, which is essentially a character file in the most common scenario. We are aware of the desired result: a yes/no response that indicates if the message is spam.However, how to convert the input to the output is beyond our knowledge. What constitutes spam varies over time and among individuals.We compensate for our knowledge gaps with data. Thousands of sample messages, some of which we know are spam and some of which are not, can be readily compiled. Our goal is to "learn" what spam is from these examples. Stated otherwise, we want the algorithm for this task to be automatically extracted by the computer (machine). Since we currently have algorithms for sorting numbers, there is no need to learn how to do it. However, there are several applications for which we lack methods but have large amounts of data.

Timeline for machine learning

1943: Mathematical brain network modelling using logic Scientists Warren McCulloch and Walter Pitts demonstrated the first mathematical modelling of a neural network in history in order to create algorithms that mimic human cognitive processes.
1950: saw the invention of the Turing Test by Alan Turing, which states that a computer is considered "intelligent" if it can persuade a human that it is indeed a human.
1952: saw the creation of Arthur Samuel's Checkers program, which was designed to play checkers on an IBM computer and get better every time.
1956: Dartmouth Summer Artificial Intelligence Research Project: John McCarthy brought a number of prominent mathematicians, scientists, and researchers to Dartmouth College, where they spent six to eight weeks coming up with concepts for thinking machines. Artificial intelligence is thought to have originated from this occurrence.
1957: saw the creation of Frank Rosenblatt's "perceptron," the first computer neural network designed to process visual inputs like images and produce outputs like labels and classifications in response.
1963: A program named MENACE (Matchbox Educable Noughts and Crosses Engine), created by Donald Michie, was capable of learning how to play a flawless game of tic tac toe (known as Noughts and Crosses XO in the UK).
1967: The closest neighbour method, which was used to design routes for travelling salesmen, gave computers the ability to recognise simple patterns.

From the beginning of neural network modelling in 1943 to ground-breaking innovations like Google's AlphaGo in 2016, a number of significant events have influenced the area of machine learning.

Why is Machine Learning important ?

The availability of high-speed Internet, the availability and affordability of computational power, and the ever-increasing amounts and diversity of data are all contributing factors to the expanding significance of machine learning. One can easily and automatically create models that can analyse incredibly vast and complicated data sets with accuracy because to these digital transformation aspects.

Machine learning can be used for many purposes, such as recommending goods and services, identifying cybersecurity breaches, and enabling self-driving automobiles, to save expenses, reduce risks, and enhance general quality of life. Machine learning is spreading daily and will soon be incorporated into many aspects of human life due to increased availability to data and processing capacity.

What are the different types of machine learning ?

Machine learning is a subset of AI, which enables the machine to automatically learn from data, improve performance from past experiences, and make predictions. Machine learning contains a set of algorithms that work on a huge amount of data. Data is fed to these algorithms to train them, and on the basis of training, they build the model & perform a specific task.

These ML algorithms help to solve different business problems like Regression, Classification, Forecasting, Clustering, and Associations, etc.

Based on the methods and way of learning, machine learning is divided into mainly four types, which are:

Supervised Machine Learning.
Unsupervised Machine Learning.
Semi-Supervised Machine Learning.
Reinforcement Learning.

How does the Supervised Machine Learning work ?

Supervised learning, a key ML technique, is more important than ever as the worldwide machine learning market is predicted to grow at a 42% compound annual growth rate (CAGR) before 2024. An expanding variety of businesses benefit from its capacity to transform data into actionable insights in order to achieve the desired outcomes for the target variable.

Similar to supervised machine learning, supervised learning relies on the cultivation of data and the production of an output from prior experiences (labelled data).

In other words, each data point is a pair of data examples (input objects) and target labels (intended predictions), indicating that the input data is made up of labelled examples.

This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias). This statistical quality of an algorithm is measured through the so-called generalization error. The goal of testing data is to estimate generalization error on unlabeled datasets.

Of course, all that is possible when the machine learning model is provided with quality training data. The latter can lead to drastic improvements in model performance, giving you a considerable edge over your competitors.

As supervised learning model’s ability to accumulate training data and utilize performance criteria derives from previous experiences, the same data gets employed to forecast future events and refine present training data. This process ends up saving lots of time and effort, not to mention how helpful it gets in solving many real-world computation problems.

In a sense, the supervised learning process starts with the collection and preparation of labeled training data, and once that data is accumulated, the labeled data gets categorized into different groups/versions.

How does Unsupervised Machine Learning work ?

Unsupervised learning works by analyzing the data without its labels for the hidden structures within it, and through determining the correlations, and for features that actually correlate two data items. It is being used for clustering, dimensionality reduction, feature learning, density estimation, etc.

The hidden structure sometimes called feature vector, represents the input data such a way that if the same feature vector is being use to reconstruct the input, then one can do that with some acceptable loss. The variance in two feature vectors of two inputs directly proportional to the variance in the inputs itself. Thus this hidden structure or feature vector only represents features in the data that actually give distinction to it.

RBM, autoencoders are the two simple form of unsupervised neural networks. Moreover a CNN network without a FC network can be used as a encoder for the images. The training for such encoder networks are done by using a decoder network, and optimizing by reducing the reconstruction loss.Because there is no external teacher in unsupervised learning, it is crucial to increase the entropy which can be done by redundancies in the data. Redundancy provides knowledge for unsupervised learning. Unsupervised learning works with the mechanism that compare the coming data with the datas seen before.

What unsupervised learning models actually do is to measure the familiarity of coming datas with the past seen datas, and make inferences with that comparison like clustering.Unsupervised learning often tries to take advantage of statistical patterns that reoccur in data. Since unsupervised learning generally does not have labels to work with, the algorithms have to do the next best thing which is try to figure out what commonly (i.e. often repeatedly) happens in data and compare that against what uncommonly happens in data.

How does Semi-supervisied Machine Learning work ?

Semi-supervised learning (SSL) is a machine learning type that falls between supervised and unsupervised. The central concept is to use the labeled data as a guide for your learning process and similarly extract information from these unlabeled sources of the training set.

When labeling data might be expensive or time-consuming, SSL helps obtain useful regularities from labeled and unlabeled examples. Semi-supervised learning is in the middle of these two types: supervised learning indicates that we have fully labeled datasets, and unsupervised learning means no labels.

A natural question arises: is semi-supervised learning meaningful? More precisely: in comparison with a supervised algorithm that uses only labeled data, can one hope to have a more accurate prediction by taking into account the unlabeled points? As you may have guessed from the size of the book in your hands, in
principle the answer is “yes.” However, there is an important prerequisite: that the distribution of examples, which the unlabeled data will help elucidate, be relevant for the classification problem.
In a more mathematical formulation, one could say that the knowledge on p(x) that one gains through the unlabeled data has to carry information that is useful in the inference of p(y|x). If this is not the case, semi-supervised learning will not yield an improvement over supervised learning. It might even happen that using
the unlabeled data degrades the prediction accuracy by misguiding the inference Semi-supervised learning will be most useful whenever there are far more unlabeled data than labeled. This is likely to occur if obtaining data points is cheap, but obtaining the labels costs a lot of time, effort, or money. This is the case in many
application areas of machine learning, for example: In speech recognition, it costs almost nothing to record huge amounts of speech, but labeling it requires some human to listen to it and type a transcript.
Billions of webpages are directly available for automated processing, but to classify them reliably, humans have to read them.
Protein sequences are nowadays acquired at industrial speed (by genome sequencing, computational gene finding, and automatic translation), but to resolve a threedimensional (3D) structure or to determine the functions of a single protein may
require years of scientific work.

How does Reinforcement Learning works ?

Reinforcement machine learning models are taught to make a series of judgments via learning. In an unpredictable and potentially complex environment, the agent must learn to attain a goal. Artificial intelligence is put in a game-like environment when it learns reinforcement. To find a solution to the problem, the computer employs trial and error.

Artificial intelligence is given either rewards or penalties for the acts it takes in order to get it to accomplish what the programmer desires. Its purpose is to increase the total prize as much as possible.

Despite the fact that the designer establishes the reward policy–that is, the game’s rules–he provides the model with no tips or ideas for how to solve the game.

Starting with completely random trials and progressing to sophisticated tactics and superhuman skills, it’s up to the model to find out how to do the task in order to maximize the reward. Reinforcement learning is currently the most effective technique to hint at machine creativity by utilizing the power of search and many trials.

Despite the fact that the designer establishes the reward policy–that is, the game’s rules–he provides the model with no tips or ideas for how to solve the game.

How to choose and build the right Machine Learning model ?

Machine learning models have wide applications in many fields. Some of the widely used applications are Email spam filtering, Web search results, pattern and image recognition, Video recommendation, and so on. Machine Learning becoming a more widely accepted and adapted technology in many fields. The applications of Machine learning models are widespread in the fields of health care, banking, e-commerce, and so on. Most social media and content-delivering networks use Machine Learning algorithms to provide a more personalized and enjoyable experience.

To build any Machine Learning model, there are 8 steps to follow. It is not necessary to build a model in machine learning with all those steps, you can also skip one or two steps according to your model. But for better accuracy, follow all these steps. The steps in machine learning model building are as follows.

Understand the problem,
Collect and Process the data,
Split the data,
Choose appropriate model,
Train the model,
Evaluate the model,
Hyperparameter Tuning,
Prediction,

Understand the problem :
Before starting to build any machine learning model, first try to analyze and understand the purpose for which you are building the model. It would help to choose the appropriate algorithm for our model and also gives better results. If you understand the problem clearly, you can able to list some potential solutions to test in order to generate the best model. Understand that you have to try out a few solutions before you land on a good working model.

To understand the steps more clearly, let us consider the example of identifying fruits by their color, shape, and size. This basic example is to understand the process in a simple way. For our model, we have different parameters to classify a fruit. We can add more features to get better results. For the sake of simplicity, we have taken three different parameters to identify the fruit. The first feature is the color of the fruit, the second one is the shape of the fruit and the last one is the size of the fruit. Using these features our model will identify the name of the fruit.

It is good to have basic knowledge of the field in which you are developing the model. For example, if you are developing a model for credit card fraud detection, you should learn to understand how the industry operates and analyze the problem completely to build a better model.
Data Collection and Data Preprocessing:
The collection of data is the foundation of the Machine Learning process. The better the collection of data, the better will be the model. Choosing incorrect features or Choosing limited features for the dataset may reduce the efficiency of the model. So it is very important to concentrate on the dataset before starting to build the model. If you want to build a model with a sample dataset, there are a lot of datasets already available at https://www.kaggle.com/. So, you can use the dataset required for your model from there.

Color Shape Size Fruit Name

Red Conical Small Strawberry

Orange Round Medium Orange

Green Ellipsoidal Medium Guava

Red Round Medium Apple

Green Oval Large Watermelon
The dataset used in the Machine Learning model is simply an M x N matrix, where M is the columns (features) and N is the rows (samples). It can be further broken into X (independent variables) and Y (dependent variables).
Exploratory Data Analysis:
Exploratory Data Analysis (EDA) is done in order to gain an understanding of the dataset. Some of the common approaches for EDA include:

Data visualization – Scatter plot, heat map, box plot, etc.
Descriptive statistics – Mean, median, Standard deviation, etc.
Once we have analyzed data with suitable features, the next step is to preprocess the data for further steps. The quality of data will have a huge impact on the quality of the model. Data preprocessing is a technique to convert the data collected into a clean dataset. It is one of the vital and important steps in building a machine-learning model. It is also known as data wrangling or data cleaning.

Some datasets contain missing values, duplicate values, or incorrect data. In those cases, remove a specific row that has a null value for a feature or a particular column where the values are missing. Or calculate the mean of a particular row that contains a missing value and replace the result for the missing value. Most datasets have a large number of features. Which increases planes, it is difficult to model and visualize. The volume of data is reduced by methods like Principal Component Analysis (PCA) and SVD.
Feature Scaling:
It is the final step in the data preprocessing phase. The learning algorithms perform much better when the features are on the same scale. A well-prepared data always gives better results. So It is important to fine-tune the data at each and every step of building the model. The most common techniques used in feature scaling are:

Normalization – It is a method to rescale the features of the data within a specific range mostly [0,1]. To normalize the data min-max scaling method is applied to each feature column.
Xchanged = ( X – Xmin )/ ( Xmax – Xmin )

Standardization – It centers the feature columns at mean 0 and standard deviation 1 so that the feature columns have the same scale. It keeps the information about outliers and makes the model less sensitive to them.
Z = ( X-μ ) where, μ – Mean, σ – Standard deviation.
Split the data:
The next important step is to explore the dataset and divide the dataset into training and testing data. The dataset for the Machine Learning model must be split into two separate sets – training and test set.

The training data denotes the subset of a dataset used for training the machine learning model. Here we already know the output. The testing data is the subset of the dataset, used for testing the machine learning model. The Machine Learning model predicts the outcomes of the test dataset. The breaking of data should be 80:20 or 70:30 ratio approximately. The larger part is for training purposes and the smaller part is for testing purposes. This is more important because using the same data for training and testing would not produce good results.

One more common approach is to split the data into 3 portions training data, validation data, and testing data. As explained before, the training set is used to train the model, the validation set is used for evaluation where model tuning (like hyperparameter tuning) is done. The testing set can be used to further test the model.
Cross-Validation:
After segregating the data, our next work is to find a good algorithm suited for our model. This is one of the most important steps in machine learning. There are various existing models and algorithms are there to use. Our job is to find an appropriate algorithm from the variety of options over there. There are three types of Machine Learning Models – Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

Supervised Learning – deals with training the model with labeled data. The outcome is known, so the model is continuously refined to get the accuracy. Our sample data with fruits fall under this category since we already know the outcome. Linear Regression Algorithm can be used to build a model with our data. Some of the common algorithms in Supervised Learning are Linear regression, Logistic regression, Polynomial regression, Random forest, Decision tree, K-nearest neighbors, and Naive Bayes. We have the data with predefined output to train the model in the case of Supervised Learning.

Unsupervised Learning – If the label data is not available and the outcome is unknown it falls under the category of Unsupervised Learning. This algorithms clusters and groups the data into categories. Some of the algorithms used in Unsupervised Learning are Principal component analysis, K-means clustering, Apriori Algorithm, Partial least squares, Fizzy means, Hidden Markov models, and Hierarchical clustering.

Reinforcement Learning(click here to read in detail) – learns and makes decisions based on trial and error method. A common example of the Reinforcement Learning algorithm is Markov’s decision process.

There are various algorithms available for various purposes of the model. Some algorithms are suited for dealing with text, some for images, and much more. Choose an appropriate algorithm to build your model after analyzing the dataset and aim to build the model. We can implement our model to distinguish fruits with Linear Regression since we are dealing with 3 independent variables to predict the outcome.
Train the model:
The main process of building our model starts with the training phase. Here we use the split part of the data set allocated for training to make our model learn. Our model learns to identify fruits by analyzing their characteristics. Our 3 features have a coefficient called the weight of features. And the constant is known as the bias of the model. First, we pick random values and compare them with actual output, and then the difference can be reduced by trying different biases and values. Repeat the iteration until the model reaches a decent amount of accuracy.

You should spend a quality amount of time during the training phase. The more you tune and prepare the model during training, the better will be the results. This phase requires a lot of patience and experimentation. If you succeed in training your model well, then you can expect good results from your model.

The most important problems considered during the training of models are optimization and generalization.

Optimization – is defined as the process of adjusting the model to get the best performance possible on training data i.e. the learning process.
Generalization – is said to be how well the model performs on unseen data. The main goal is to get the best generalization possible.
Let’s understand two important terms before moving further steps in the Machine Learning model.

Bias – They are the assumptions made by the model to make a function easy to learn
Variance – After training data and obtaining low error. Upon changing the data, then training the same model and experiencing a high error, is known as a variance.

Overfitting and Underfitting
A model is said to be under-fitted when it cannot capture the underlying data. It denotes that our algorithm or model does not fit the data well. It happens when we have less data to build the model. Overfitting simply means high bias and low variance. Underfitting reduces the accuracy of the model.

Underfitting can be reduced by

Increasing the model complexity
Increasing the number of features
Removing the noise from the data
Increasing the duration of training
A model is said to be over-fitted when we train with a lot of data. This happens when the model gets trained with so many inaccurate data entries and noise. Overfitting simply means high variance and low bias.

Overfitting can be reduced by

Reducing model complexity
Increasing the training data
Ridge Regularization and Lasso Regularization
Using dropout for neural networks to tackle overfitting.
Evaluate the model
After training the model with trained data, the model has to be tested. The purpose of testing is to evaluate how the model will work in real-world scenarios. We can evaluate the accuracy of the model during this phase. In our case, the model tries to identify the type of fruit with the learning done in the previous phase. The evaluation phase is very important and we can check whether the model achieves the goal we planned. If the model does not perform well up to mark during the testing phase then the previous steps have to be re-iterated until we attain the required accuracy. As I mentioned earlier we should not use the same data used during the training phase. The separate data splitter from our dataset should be used for evaluation.

Regression metrics
regression models deal with a continuous range of values instead of classes. Mean squared error in the model is calculated by taking the average of squared differences between the predicted output and the actual output. Learning curves are plotted with training data and validation data. If our model gives high bias, we can come to the conclusion of having errors in the validation and training datasets. If the model suffers from high bias, training is to be done more to improve the model.

Classification metrics
Classification models are concerned only with whether the outcome is correct or not. When performing classification predictions like our model, there are four possible outcomes that could be expected. They are true positives, true negatives, false positives, and false negatives. These four outcomes are plotted on a confusion matrix. You can generate the matrix after predictions on the test data and categorize each prediction as one of the possible outcomes. The accuracy of the model is the percentage of correct predictions made by the test data. The accuracy of the model can be evaluated by dividing the number of correct predictions by the number of total predictions. There are other metrics like precision and recall that are also used to evaluate Classification models.
Hyperparameter tuning:
Once the evaluation is successful, proceed to the next phase Parameter tuning. This step in the machine learning model improves the results gained during the evaluation step. A Hyperparameter of the model is a configuration that is external to the model and whose value cannot be estimated from the data provided. They are used in processes to help estimate model parameters, they are tuned for a given predictive modeling problem and they can often be set using heuristics.

Some of the examples in model hyperparameters include the C and sigma hyperparameters for support vector machines, K in K-nearest neighbors, learning rate for training a neural network, and the penalty in Logistic Regression Classifier. The model parameters are estimated from data automatically and model hyperparameters are set manually and are used in processes to help estimate model parameters.

Hyperparameter tuning is choosing a set of optimal hyperparameters for a learning algorithm. Model hyperparameters are often referred to as parameters because they are the parts of machine learning that must be set manually and tuned.

In our case, we can make our model recognize fruits better by hyperparameter tuning. There are multiple ways we can improve and tune the model. You can revisit the training phase and use multiple sweeps of data to train the model. This can lead to better accuracy and also the long duration of training provides better accuracy and results. You can also refine the initial values given to the model. There are many other parameters we can tune and achieve desired results.
Prediction:
The final step in machine learning model building is prediction. This is the phase where our model can be considered to be ready for applications. Our fruit model should be able to identify the name of the fruit. The model is given independence from human interference and it exposes its conclusion on the basis of the datasets and the training given. We succeed in the Machine Learning model If it can able to perform well in different scenarios.

This step is executed by the end-users when they use the particular model in the respective domain. Machine Learning models can process large amounts of data and make decisions. The model we build to find fruits is very simple yet the steps can be easily understood with the help of that.

Machine Learning application for enterprise :

According to the IDC (2018), spending on cognitive and AI systems will reach $77.6 billion in 2022, which is more than three times the $24 billion forecast for 2018. The banking and retail industries made the largest investments in cognitive and AI systems in 2018. A recent O’Reilly Media (2018) survey indicates that about half of the world's 11,000 data specialists work for enterprises in the early stages of exploring machine learning, while the rest have moderate or extensive experience in.

Machine Learning Examples by Company

Companies of all sorts have used machine learning to expand their offerings and streamline their processes. After completing a data boot camp, you will have the skills to start implementing machine learning in your company — or qualify for a new one. Below is just a small selection of companies that have used these tools to great advantage.

Yelp’s image curation
In 2015, Yelp built a photo classifier that helps classify user-uploaded photos of businesses. For example, if a user went to the local pub, ordered a burger, and uploaded a photo of it to Yelp, the image classifier would be able to properly identify the burger. To build this classifier, Yelp collected the information through photo captions, photo attributes, and crowdsourcing, and then used machine learning to classify future photos. They also allow users to report incorrectly classified photos — a great example of feedback that helps improve ML-built products.

Facebook’s chatbot
In 2020, Facebook introduced a chatbot that was able to converse on a wide array of topics — not just a prescribed set of topics like many customer service chatbots do. According to the MIT Technology Review, this bot, called Blender, was “first trained on 1.5 billion publicly available Reddit conversations.” It was then further developed to focus on conversations containing emotion, information-dense conversations, and conversations between users with distinct and differing personalities.

Amazon’s product recommendations
Once you begin browsing and shopping on Amazon, you’ll start to see suggestions like “Customers Who Bought This Product Also Bought”. These are clear results of machine learning, as they categorize both products and shoppers and use that information to suggest items to users.

What are advantage and disadvantage of Machine Learning ?

The Advantages and Disadvantages of Machine Learning: balancing its benefits with its real-world limitations

Machine learning or ML is a revolutionary technology that has changed many fields by letting computers learn and change on their own. However, it has pros and cons just like any other tool.
Learning about the advantages and disadvantages of machine learning is important for anyone who wants to use it effectively in their business or projects. There are advantages and disadvantages of machine learning that are talked about in this article. It shows what machine learning can and can't do.

Advantages of Machine Learning
Machine learning is a great instrument in many different fields since it presents several convincing benefits:

1. Efficiency and automation
The capacity of machine learning to automate advantages of machine learning is its main benefit. ML algorithms may accomplish highly efficient jobs including data entry, email filtering and also customer assistance by means of pattern and data analysis. Automation helps to liberate human resources for more difficult jobs.

2. Improved Choice of Decision-Making
Processing enormous volumes of data at amazing rates, ML models can offer insights missed by people. These models are absolutely essential for companies that depend on data-driven decisions since they can forecast results and also advise activities by learning from past data.

3. Enhanced Personalism
One major advantage of machine learning especially in sectors like e-commerce and entertainment—is personalizing. By means of analysis of user preferences and behaviors, ML techniques can generate custom suggestions, hence improving user experience and happiness.

4. Flexibility and Ongoing Education
As ML models encounter additional data, they grow over time unlike conventional systems. One of the advantages of machine learning is its adaptability since it guarantees that models stay relevant even in changing circumstances.

5. Use in Various Domains
From marketing to finance and industry, benefits of machine learning in many disciplines including healthcare. While in finance it supports fraud detection and algorithmic trading, in healthcare ML assists with disease prediction and drug discovery. Its adaptability emphasizes its importance in current uses.

Disadvantages of Machine Learning
Even though machine learning has many benefits, it disadvantage of machine learning that should not be ignored. These problems show where extra care needs to be taken when using ML systems.

1. Dependence on Data
One big problem with limitation of machine learning is that it needs a lot of data to work. For machine learning models to work well, they need a lot of high-quality data. Data that is missing, skewed, or wrong can make a model not work well and give bad results.

2. Difficulty in Putting It into Action

It's not easy to make and use machine learning models; you usually need to have specific skills and knowledge. Small businesses and groups that don't have a lot of time or money may find it hard to deal with this level of difficulty.

3. Not being able to be understood
A lot of deep learning models and other machine learning models work in a way that is hard to understand. It's not always easy to figure out how they got the exact data they give. The disadvantage of machine learning is that it is not always clear what it is doing. This is very important in fields like healthcare and business where you need to be able to explain things.

4. A lot of money spent on computers

It takes a lot of computing power to train machine learning models especially ones that use big datasets. This means that hardware, energy and cloud services are expensive, which can make them harder to get.

5. The risk of being too tight or too loose
ML models can either become too specific to the data they were trained on (this is called "overfitting") or not change at all (this is called "underfitting"). This shows the strengths and weaknesses of machine learning by showing how hard it is for the scope and limitations of machine learning in these two cases.

What is the future of Machine learning ?

1. A Reduced Need for Large Amounts of Data
Machine learning used to require large datasets to find desired patterns. Now, however, it is more important that organizations have the right data. Using the right data means prioritizing its quality and relevance over the number of points in a dataset.

Organizations that previously lacked sufficient data can now build models using the right data (supplemented by third-party sources, when needed). The need for less data also increases AI’s overall accessibility of AI—meaning that organizations with lower budgets and smaller teams can also reap the benefits of AI.

2. Increased Use of Multimodal Data
In order for a machine learning model to truly understand what’s going on around us, it has to analyze multimodal data. Just as humans use five senses to process their surroundings, a single machine learning model will learn to understand spreadsheets, images, text, audio, and other mediums.

3. Development, Use, and Adaptation of Foundational Models
We’re also starting to see AI projects benefiting from transfer learning, where a foundational machine learning model trained on a large amount of data is reused as a “starting point” to train another model for another task. Models like these can improve the efficiency of AI design by eliminating the need to start over on every project.

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