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Machine learning and neural networks are two important technologies in the field of artificial intelligence (AI). While they are often used together, they are not the same thing. Here, we will explore the differences between machine learning and neural networks and how they are related.

Let us first understand both the terms in detail and then their differences.

What is Machine Learning?

Machine Learning is that field of computer science with the help of which computer systems can provide sense to data in much the same way as human beings do.

In simple words, ML is a type of artificial intelligence that extracts patterns out of raw data by using an algorithm or method.

Machine learning can be classified into three different categories on the basis of human supervision. These categories are supervised learning, unsupervised learning, and reinforcement learning.

In supervised learning, machine learning algorithms are trained with labeled data sets to perform tasks related to classification and regression. Some of the used supervised learning algorithms are linear regression, K-nearest neighbors, decision trees, random forest, etc.

In unsupervised learning, the models are trained on unlabeled datasets. Unsupervised learning is mainly used for tasks related to clustering, association rule mining, and dimensionality reduction. Some of the most used unsupervised algorithms include K-means clustering, apriori algorithm, etc.

Reinforcement learning is somehow similar to supervised learning where an agent (algorithm or software entity) learns to interact environment by performing actions and monitoring results. Learning is based on rewards and penalties. There are various algorithms used in reinforcement learning, such as Q-learning, policy gradient methods, the Monte Carlo method, and many more.

What are Neural Networks?

Neural networks are a type of machine learning algorithm that is inspired by the structure of the human brain. They are designed to simulate how the brain works by using layers of interconnected nodes, or artificial neurons. Each neuron takes in input from the neurons in the previous layer and uses that input to produce an output. This process is repeated for each layer until a final output is produced.

Neural networks can be used for a wide range of tasks, including image recognition, speech recognition, natural language processing, and prediction. They are particularly well-suited to tasks that involve processing complex data or recognizing patterns in data.

The following diagram represents the general model of ANN followed by its processing.

Machine Learning vs. Neural Networks

Now that we have a basic understanding of what machine learning and neural networks are. Let’s dive deeper into the differences between the two.

• Firstly, machine learning is a broad category that encompasses many different types of algorithms, including neural networks. Neural networks are a specific type of machine learning algorithm that is designed to simulate the way the brain works.
• Secondly, while machine learning algorithms can be used for a wide range of tasks, neural networks are particularly well-suited to tasks that involve processing complex data or recognizing patterns in data. Neural networks can recognize complex patterns and relationships in data that other machine learning algorithms may not be able to detect.
• Thirdly, neural networks require a lot of data and processing power to train. Neural networks typically require large datasets and powerful hardware, such as graphics processing units (GPUs), to train effectively. Machine learning algorithms, on the other hand, can be trained on smaller datasets and less powerful hardware.
• Finally, neural networks can provide highly accurate predictions and decisions, but they can be more difficult to understand and interpret than other machine learning algorithms. The way that neural networks make decisions is not always transparent, which can make it difficult to understand how they arrived at their conclusions.

Artificial Intelligence and Machine Learning are two buzzwords that are commonly used in the world of technology. Although they are often used interchangeably, they are not the same thing. Artificial intelligence (AI) and machine learning (ML) are related concepts, but they have different definitions, applications, and implications. In this article, we will explore the differences between machine learning and artificial intelligence and how they are related.

What is Artificial Intelligence?

Artificial intelligence is a broad field that encompasses the development of intelligent machines that can perform tasks that typically require human intelligence, such as perception, reasoning, learning, and decision-making. In simple terms, AI is the ability of machines to perform tasks that normally require human intervention or intelligence.

There are two types of AI: narrow or weak AI and general or strong AI. Narrow AI is designed to perform specific tasks, such as speech recognition or image recognition, while general AI is designed to be able to perform any intellectual task that a human can do. Currently, we only have narrow AI in use, but the goal is to develop general AI that can be applied to a wide range of tasks.

Branches of AI

AI is like a basket containing several branches, the important ones being Machine Learning (ML), Robotics, Expert Systems, Fuzzy Logic, Neural Networks, Computer Vision, and Natural Language Processing (NLP).

Here is a brief overview of the other important branches of AI:

• Robotics Robots are primarily designed to perform repetitive and tedious tasks. Robotics is an important branch of AI that deals with designing, developing and controlling the application of robots.
• Computer Vision It is an exciting field of AI that helps computers, robots, and other digital devices to process and understand digital images and videos, and extract vital information. With the power of AI, Computer Vision develops algorithms that can extract, analyze and comprehend useful information from digital images.
• Expert Systems Expert systems are applications specifically designed to solve complex problems in a specific domain, with humanlike intelligence, precision, and expertise. Just like human experts, Expert Systems excel in a specific domain in which they are trained.
• Fuzzy Logic We know computers take precise digital inputs like True (Yes) or False (No), but Fuzzy Logic is a method of reasoning that helps machines to reason like human beings before taking a decision. With Fuzzy Logic, machines can analyze all intermediate possibilities between a YES or NO, for example, “Possibly Yes”, “Maybe No”, etc.
• Neural Networks Inspired by the natural neural networks of the human brain, Artificial Neural Networks (ANN) can be considered as a group of highly interconnected group of processing elements (nodes) that can process information by their dynamic state response to external inputs. ANNs use training data to improve their efficiency and accuracy.
• Natural Language Processing (NLP) NLP is a field of AI that empowers intelligent systems to communicate with humans using a natural language like English. With the power of NLP, one can easily interact with a robot and instruct it in plain English to perform a task. NLP can also process text data and comprehend its full meaning. It is heavily used these days in virtual chatbots and sentiment analysis.

Examples of AI include virtual assistants, autonomous vehicles, facial recognition, natural language processing, and decision-making systems.

What is Machine Learning?

Machine learning is a subset of artificial intelligence that focuses on teaching machines how to learn from data. In other words, machine learning is a process by which computers can automatically learn patterns and relationships in data without being explicitly programmed to do so. Machine learning algorithms are designed to detect and learn from patterns in data to make predictions or decisions.

There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is when the machine is trained on labeled data with known outcomes. Unsupervised learning is when the machine is trained on unlabeled data and is asked to find patterns or similarities. Reinforcement learning is when the machine learns by trial and error through interactions with the environment.

Examples of machine learning include image recognition, speech recognition, recommendation systems, fraud detection, and natural language processing.

Artificial Intelligence Vs. Machine Learning Overview

Now that we have a basic understanding of what machine learning and artificial intelligence are, let’s dive deeper into the differences between the two.

Firstly, machine learning is a subset of artificial intelligence, meaning that machine learning is a part of the larger field of AI. Machine learning is a technique used to implement artificial intelligence.

Secondly, while machine learning focuses on developing algorithms that can learn from data, artificial intelligence focuses on developing intelligent machines that can perform tasks that normally require human intelligence. In other words, machine learning is more focused on the process of learning from data, while AI is more focused on the end goal of creating machines that can perform intelligent tasks.

Thirdly, machine learning algorithms are designed to learn from data and improve their accuracy over time, while artificial intelligence systems are designed to learn and adapt to new situations and environments. Machine learning algorithms require a lot of data to be trained effectively, while AI systems can adapt and learn from new data in real-time.

Finally, machine learning is more limited in its capabilities compared to AI. Machine learning algorithms can only learn from the data they are trained on, while AI systems can learn and adapt to new situations and environments. Machine learning is great for solving specific problems that can be solved through pattern recognition, while AI is better suited for complex, real-world problems that require reasoning and decision-making.

Difference Between Artificial Intelligence and Machine Learning

The following table highlights the important differences between Machine Learning and Artificial Intelligence

Key	Artificial Intelligence	Machine Learning
Definition	Artificial Intelligence refers to the ability of a machine or a computer system to perform tasks that would normally require human intelligence, such as understanding language, recognizing images, and making decisions.	Machine Learning is a type of Artificial Intelligence that allows a system to learn and improve from experience without being explicitly programmed. It articulates how a machine can learn and apply its knowledge to improve its decisions.
Concept	Artificial Intelligence revolves around making smart and intelligent devices.	Machine Learning revolves around making a machine learn/decide and improve its results.
Goal	The goal of Artificial Intelligence is to simulate human intelligence to solve complex problems.	The goal of Machine Learning is to learn from data provided and make improvements in machine’s performance.
Includes	Artificial Intelligence has several important branches including Artificial Neural Networks, Natural Language Processing, Fuzzy Logic, Robotics, Expert Systems, Computer Vision, and Machine Learning.	Machine Learning training methods include supervised learning, unsupervised learning, and reinforcement learning.
Development	Artificial Intelligence is leading to the development of such machines which can mimic human behavior.	Machine Learning is helping in the development of self-learning algorithms.

Machine learning is an interdisciplinary field that involves computer science, statistics, and mathematics. In particular, mathematics plays a critical role in developing and understanding machine learning algorithms. In this chapter, we will discuss the mathematical concepts that are essential for machine learning, including linear algebra, calculus, probability, and statistics.

Linear Algebra

Linear algebra is the branch of mathematics that deals with linear equations and their representation in vector spaces. In machine learning, linear algebra is used to represent and manipulate data. In particular, vectors and matrices are used to represent and manipulate data points, features, and weights in machine learning models.

A vector is an ordered list of numbers, while a matrix is a rectangular array of numbers. For example, a vector can represent a single data point, and a matrix can represent a dataset. Linear algebra operations, such as matrix multiplication and inversion, can be used to transform and analyze data.

Followings are some of the important linear algebra concepts highlighting their importance in machine learning −

• Vectors and matrix − Vectors and matrices are used to represent datasets, features, target values, weights, etc.
• Matrix operations − operations such as addition, multiplication, subtraction, and transpose are used in all ML algorithms.
• Eigenvalues and eigenvectors − These are very useful in dimensionality reduction related algorithms such principal component analysis (PCA).
• Projection − Concept of hyperplane and projection onto a plane is essential to understand support vector machine (SVM).
• Factorization − Matrix factorization and singular value decomposition (SVD) are used to extract important information in the dataset.
• Tensors − Tensors are used in deep learning to represent multidimensional data. A tensor can represent a scalar, vector or matrix.
• Gradients − Gradients are used to find optimal values of the model parameters.
• Jacobian Matrix − Jacobian matrix is used to analyze the relationship between input and output variables in ML model
• Orthogonality − This is a core concept used in algorithms like principal component analysis (PCA), support vector machines (SVM)

Calculus

Calculus is the branch of mathematics that deals with rates of change and accumulation. In machine learning, calculus is used to optimize models by finding the minimum or maximum of a function. In particular, gradient descent, a widely used optimization algorithm, is based on calculus.

Gradient descent is an iterative optimization algorithm that updates the weights of a model based on the gradient of the loss function. The gradient is the vector of partial derivatives of the loss function with respect to each weight. By iteratively updating the weights in the direction of the negative gradient, gradient descent tries to minimize the loss function.

Followings are some of the important calculus concepts essential for machine learning −

• Functions − Functions are core of machine learning. In machine learning, model learns a function between inputs and outputs during the training phase. You should learn basics of functions, continuous and discrete functions.
• Derivative, Gradient and Slope − These are the core concepts to understand how optimization algorithms, like gradient descent, work.
• Partial Derivatives − These are used to find maxima or minima of a function. Generally used in optimization algorithms.
• Chain Rules − Chain rules are used to calculate the derivatives of loss functions with multiple variables. You can see the application of chain rules mainly in neural networks.
• Optimization Methods − These methods are used to find the optimal values of parameters that minimizes cost function. Gradient Descent is one of the most used optimization methods.

Probability Theory

Probability is the branch of mathematics that deals with uncertainty and randomness. In machine learning, probability is used to model and analyze data that are uncertain or variable. In particular, probability distributions, such as Gaussian and Poisson distributions, are used to model the probability of data points or events.

Bayesian inference, a probabilistic modeling technique, is also widely used in machine learning. Bayesian inference is based on Bayes’ theorem, which states that the probability of a hypothesis given the data is proportional to the probability of the data given the hypothesis multiplied by the prior probability of the hypothesis. By updating the prior probability based on the observed data, Bayesian inference can make probabilistic predictions or classifications.

Followings are some of the important probability theory concepts essential for machine learning −

• Simple probability − It’s a fundamental concept in machine learning. All classification problems use probability concepts. SoftMax function uses simple probability in artificial neural networks.
• Conditional probability − Classification algorithms like the Naive Bayes classifier are based on conditional probability.
• Random Variables − Random variables are used to assign the initial values to the model parameters. Parameter initialization is considered as the starting of the training process.
• Probability distribution − These are used in finding loss functions for classification problems.
• Continuous and Discrete distribution − These distributions are used to model different types of data in ML.
• Distribution functions − These functions are often used to model the distribution of error terms in linear regression and other statistical models.
• Maximum likelihood estimation − It is a base of some machine learning and deep learning approaches used for classification problems.

Statistics

Statistics is the branch of mathematics that deals with the collection, analysis, interpretation, and presentation of data. In machine learning, statistics is used to evaluate and compare models, estimate model parameters, and test hypotheses.

For example, cross-validation is a statistical technique that is used to evaluate the performance of a model on new, unseen data. In cross-validation, the dataset is split into multiple subsets, and the model is trained and evaluated on each subset. This allows us to estimate the model’s performance on new data and compare different models.

Followings are some of the important statistics concepts essential for machine learning −

• Mean, Median, Mode − These measures are used to understand the distribution of data and identify outliers.
• Standard deviation, Variance − These are used to understand the variability of a dataset and to detect outliers.
• Percentiles − These are used to summarize the distribution of a dataset and identify outliers.
• Data Distribution − It is how data points are distributed or spread out across a dataset.
• Skewness and Kurtosis − These are two important measures of the shape of a probability distribution in machine learning.
• Bias and Variance − They describe the sources of error in a model’s predictions.
• Hypothesis Testing − It is a tentative assumption or idea that can be tested and validated using data.
• Linear Regression − It is the most used regression algorithm in supervised machine learning.
• Logistic Regression − It’s also an important supervised learning algorithm mostly used in machine learning.
• Principal Component Analysis − It is used mainly in dimensionality reduction in machine learning.

Data structure plays a critical role in machine learning as it facilitates the organization, manipulation, and analysis of data. Data is the foundation of machine learning models, and the data structure used can significantly impact the model’s performance and accuracy.

Data structures help to build and understand various complex problems in Machine learning. A careful choice of data structures can help to enhance the performance and optimize the machine learning models.

What is Data Structure?

Data structures are ways of organizing and storing data to use it efficiently. They include structures like arrays, linked lists, stacks, and others, which are designed to support specific operations. They play a crucial role in machine learning, especially in tasks such as data preprocessing, algorithm implementation, and optimization.

Here we will discuss some commonly used data structures and how they are used in Machine Learning.

Commonly Used Data Structure for Machine Learning

Data structure is an essential component of machine learning, and the right data structure can help in achieving faster processing, easier access to data, and more efficient storage. Here are some commonly used data structures for machine learning −

1. Arrays

Array is a fundamental data structure used for storing and manipulating data in machine learning. Array elements can be accessed using the indexes. They allow fast data retrieval as the data is stored in contiguous memory locations and can be accessed easily.

As we can perform vectorized operations on arrays, it is a good choice to represent the input data as arrays.

Some machine learning tasks that use arrays are:

• The raw data is usually represented in the form of arrays.
• To convert pandas data frame into list, because pandas series require all the elements to be the same type, which Python list contains combination of data types.
• Used for data preprocessing techniques like normalization, scaling and reshaping.
• Used in word embedding, while creating multi-dimensional matrices.

Arrays are easy to use and offer fast indexing, but their size is fixed, which can be a limitation when working with large datasets.

2. Lists

Lists are collections of heterogeneous data types that can be accessed using an iterator. They are commonly used in machine learning for storing complex data structures, such as nested lists, dictionaries, and tuples. Lists offer flexibility and can handle varying data sizes, but they are slower than arrays due to the need for iteration.

3. Dictionaries

Dictionaries are a collection of key-value pairs that can be accessed using the keys. They are commonly used in machine learning for storing metadata or labels associated with data. Dictionaries offer fast access to data and are useful for creating lookup tables, but they can be memory-intensive when dealing with large datasets.

4. Linked Lists

Linked lists are collections of nodes, each containing a data element and a reference to the next node in the list. They are commonly used in machine learning for storing and manipulating sequential data, such as time-series data. Linked lists offer efficient insertion and deletion operations, but they are slower than arrays and lists when it comes to accessing data.

Linked lists are commonly used for managing dynamic data where elements are frequently added and removed. They are less common compared to arrays, which are more efficient for the data retrieval process.

5. Stack and Queue

Stack is based on the LIFO(Last In First Out). Stacking classifier approach can efficiently be implemented in solving multi-classification problems by dividing it into several binary classification problems. This is done by stacking all the outputs from binary classification and passing it as input to the meta classifier.

Queue follows FIFO(First In First Out) structure which is similar to people waiting in a line. This data structure is used in Multi threading, which is used to optimize and coordinate data flow between threads in multi threaded environment. It is usually used to handle large amounts of data, to feed batches of data for the training process. To make sure that the training process is continuous and efficient.

6. Trees

Trees are hierarchical data structures that are commonly used in machine learning for decision-making algorithms, such as decision trees and random forests. Trees offer efficient searching and sorting algorithms, but they can be complex to implement and can suffer from overfitting.

Binary trees are hierarchical data structures that are commonly used in machine learning for decision-making algorithms, such as decision trees and random forests. Trees offer efficient searching and sorting algorithms, but they can be complex to implement and can suffer from overfitting.

7. Graphs

Graphs are collections of nodes and edges that are commonly used in machine learning for representing complex relationships between data points. Data structures such as adjacency matrices and linked lists are used to create and manipulate graphs. Graphs offer powerful algorithms for clustering, classification, and prediction, but they can be complex to implement and can suffer from scalability issues.

Graphs are widely used in recommendation system, link prediction, and social media analysis.

8. Hash Maps

Hash maps are predominantly used in machine learning due to its key-value storage and retrieval capabilities. They are commonly used in machine learning for storing metadata or labels associated with data. Dictionaries offer fast access to data and are useful for creating lookup tables, but they can be memory-intensive when dealing with large datasets.

In addition to the above-mentioned data structures, many machine learning libraries and frameworks provide specialized data structures for specific use cases, such as matrices and tensors for deep learning. It is important to choose the right data structure for the task at hand, considering factors such as data size, processing speed, and memory usage.

How Data Structure is Used in Machine Learning?

Below are some ways data structures are used in machine learning −

Storing and Accessing Data

Machine learning algorithms require large amounts of data for training and testing. Data structures such as arrays, lists, and dictionaries are used to store and access data efficiently. For example, an array can be used to store a set of numerical values, while a dictionary can be used to store metadata or labels associated with data.

Pre-processing Data

Before training a machine learning model, it is necessary to pre-process the data to clean, transform, and normalize it. Data structures such as lists and arrays can be used to store and manipulate the data during pre-processing. For example, a list can be used to filter out missing values, while an array can be used to normalize the data.

Creating Feature Vectors

Feature vectors are a critical component of machine learning models as they represent the features that are used to make predictions. Data structures such as arrays and matrices are commonly used to create feature vectors. For example, an array can be used to store the pixel values of an image, while a matrix can be used to store the frequency distribution of words in a text document.

Building Decision Trees

Decision trees are a common machine learning algorithm that uses a tree data structure to make decisions based on a set of input features. Decision trees are useful for classification and regression problems. They are created by recursively splitting the data based on the most informative features. The tree data structure makes it easy to traverse the decision-making process and make predictions.

Building Graphs

Graphs are used in machine learning to represent complex relationships between data points. Data structures such as adjacency matrices and linked lists are used to create and manipulate graphs. Graphs are used for clustering, classification, and prediction tasks.

Machine learning has transformed various industries by automating processes, predicting outcomes, and discovering patterns in large data sets. Some real-life examples of machine learning include virtual assistants & chatbots such as Google Assistant, Siri & Alexa, recommendation systems, Tesla autopilot, IBM’s Watson for Oncology, etc.

Most of us think that machine learning is something that is related to technology about futuristic robots that is very complex. Surprisingly, every one of us uses machine learning in our daily lives knowingly or unknowingly, such as Google Maps, email, Alexa, etc. Here we are providing the top real-life examples of machine learning −

• Virtual Assistants and Chatbots
• Fraud Detection in Banking and Finance
• Healthcare Diagnosis and Treatment
• Autonomous Vehicles
• Recommendation Systems
• Target Advertising
• Image Recognition

Let’s discuss each of the above real-life examples of machine learning in detail −

Virtual Assistants and Chatbots

Natural language processing (NLP) is an area of machine learning that focuses on understanding and generating human language. NLP is used in virtual assistants and chatbots, such as Siri, Alexa, and Google Assistant, to provide personalized and conversational experiences. Machine learning algorithms can analyze language patterns and respond to user queries in a natural and accurate way.

Virtual assistants are applications of machine learning that interact with users through voice instructions. They are used to replace the work performed by human personal assistants, which includes making phone calls, scheduling appointments, or reading an email loud. The most popular virtual assistants that are used in our daily lives are Alexa, Apple Siri, and Google Assistant .

Chatbots are machine learning programs designed to engage in conversations with users. This application is designed to replace the work of customer care. It is widely used by websites for providing information, answering FAQ, and providing basic customer support.

Fraud Detection in Banking and Finance

Machine learning is not only applied to make things easier but is also applied for safety and security purposes, like fraud detection. These algorithms are trained on datasets with undesired or fraud activities to identify similar patterns of these events and detect them when they occur in the future.

These algorithms can analyze transaction data and identify patterns that indicate fraud. For example, credit card companies use machine learning to identify transactions that are likely to be fraudulent and notify customers in real time. Banks also use machine learning to detect money laundering, identify unusual behavior in accounts, and analyze credit risk.

Machine learning algorithms are widely used in the financial industry to detect fraudulent activities. One real-life example can include PayPal which uses machine learning to improve authorized transactions on its platform.

Healthcare Diagnosis and Treatment

The applications of machine learning in health care are as diverse as they impact. The combination of machine learning and medicine aims to enhance the efficiency and personalization of healthcare. Some of them include personalized treatment, patient monitoring, and medical imaging diagnosis.

Machine learning algorithms can analyze medical data, such as X-rays, MRI scans, and genomic data, to assist with the diagnosis of diseases. These algorithms can also be used to identify the most effective treatment for a patient based on their medical history and genetic makeup. For example, IBM’s Watson for Oncology uses machine learning to analyze medical records and recommend personalized cancer treatments.

Autonomous Vehicles

Autonomous vehicles use machine learning to partially replace human drivers. These vehicles are designed to reach the destination avoiding obstacles and responding to traffic conditions. Autonomous vehicles use machine learning algorithms to navigate and make decisions on the road. These algorithms can analyze data from sensors and cameras to identify obstacles and make decisions about how to respond.

Autonomous vehicles are expected to revolutionize transportation by reducing accidents and increasing efficiency. Companies such as Tesla, Waymo, and Uber are using machine learning to develop self-driving cars.

Tesla’s self-driving cars are installed with Tesla Vision, which uses cameras, sensors, and powerful neural net processing to sense and understand the environment around them. One of the real-life examples of machine learning in autonomous vehicles is Tesla AutoPilot. AutoPilot is an advanced driver assistance system.

Recommendation Systems

E-commerce platforms, such as Amazon and Netflix, use recommendation systems (machine learning algorithms) to provide personalized recommendations to users based on their browsing and viewing history. These recommendations can improve customer satisfaction and increase sales. Machine learning algorithms can analyze large amounts of data to identify patterns and predict user preferences, enabling e-commerce platforms and entertainment providers to offer a more personalized experience to their users.

This application of Machine learning is used to narrow down and predict what people are looking for among the growing number of options. Some popular real-world examples of recommendation systems are as follows −

• Netflix − Netflix’s recommendation system uses machine learning algorithms to analyze user’s watch history, search behavior, and rating to suggest movies and TV shows.
• Amazon − Amazon’s recommendation system makes personalized recommendations based on user’s prior products viewed, purchases, and items added to their carts.
• Spotify − Spotify’s recommendation system suggests songs and playlists depending on the user’s listening history, search, and liked songs, etc.
• YouTube − YouTube’s recommendation system suggests videos based on the user’s viewing history, search, liked video, etc. The machine learning algorithm considers many other factors to make personalized recommendations.
• LinkedIn − LinkedIn’s recommendation system suggests jobs, connections, etc., based on the user’s profile, skills, etc. The machine learning algorithms take the user’s current job profile, skills, location, industry, etc., to make personalized job recommendations.

Target Advertising

Targeted advertising uses machine learning to gain insights from data-driven to tailor advertisements based on the interests, behavior, and demographics of the individuals or groups.

Image Recognition

Image recognition is an application of computer vision that requires more than one computer vision task, such as image classification, object detection and image identification. It is prominently used in facial recognition, visual search, medical diagnosis, people identification and many more.

In addition to these examples, machine learning is being used in many other applications, such as energy management, social media analysis, and predictive maintenance. Machine learning is a powerful tool that has the potential to revolutionize many industries and improve the lives of people around the world.

Machine learning is a powerful technology that has transformed the way we approach data analysis, but like any technology, it has its limitations. Here are some of the key limitations of machine learning −

Dependence on Data Quality

Machine learning models are only as good as the data used to train them. If the data is incomplete, biased, or of poor quality, the model may not perform well.

Lack of Transparency

Machine learning models can be very complex, making it difficult to understand how they arrive at their predictions. This lack of transparency can make it challenging to explain model results to stakeholders.

Limited Applicability

Machine learning models are designed to find patterns in data, which means they may not be suitable for all types of data or problems.

High Computational Costs

Machine learning models can be computationally expensive, requiring significant processing power and storage.

Data Privacy Concerns

Machine learning models can sometimes collect and use personal data, which raises concerns about privacy and data security.

Ethical Considerations

Machine learning models can sometimes perpetuate biases or discriminate against certain groups, raising ethical concerns.

Dependence on Experts

Developing and deploying machine learning models requires significant expertise in data science, statistics, and programming, making it challenging for organizations without access to these skills.

Lack of Creativity and Intuition

Machine learning algorithms are good at finding patterns in data but lack creativity and intuition. This means that they may not be able to solve problems that require creative thinking or intuition.

Limited Interpretability

Some machine learning models, such as deep neural networks, can be difficult to interpret. This means that it may be challenging to understand how the model arrived at its predictions.

Machine learning is a rapidly growing field with many promising applications. However, there are also several challenges and issues that must be addressed to fully realize the potential of machine learning. Some of the major challenges and common issues faced in machine learning include −

Overfitting

Overfitting occurs when a model is trained on a limited set of data and becomes too complex, leading to poor performance when tested on new data. This can be addressed by using techniques such as cross-validation, regularization, and early stopping.

Underfitting

Underfitting occurs when a model is too simple and fails to capture the patterns in the data. This can be addressed by using more complex models or by adding more features to the data.

Data Quality Issues

Machine learning models are only as good as the data they are trained on. Poor quality data can lead to inaccurate models. Data quality issues include missing values, incorrect values, and outliers.

Imbalanced Datasets

Imbalanced datasets occur when one class of data is significantly more prevalent than another. This can lead to biased models that are accurate for the majority class but perform poorly on the minority class.

Model Interpretability

Machine learning models can be very complex, making it difficult to understand how they arrive at their predictions. This can be a challenge when explaining the model to stakeholders or regulatory bodies. Techniques such as feature importance and partial dependence plots can help improve model interpretability.

Generalization

Machine learning models are trained on a specific dataset, and they may not perform well on new data that is outside the training set. This can be addressed by using techniques such as cross-validation and regularization.

Scalability

Machine learning models can be computationally expensive and may not scale well to large datasets. Techniques such as distributed computing, parallel processing, and sampling can help address scalability issues.

Ethical Considerations

Machine learning models can raise ethical concerns when they are used to make decisions that affect people’s lives. These concerns include bias, privacy, and transparency. Techniques such as fairness metrics and explainable AI can help address ethical considerations.

Addressing these issues requires a combination of technical expertise and business knowledge, as well as an understanding of ethical considerations. By addressing these issues, machine learning can be used to develop accurate and reliable models that can provide valuable insights and drive business value.

Implementing machine learning involves several steps, which include −

Data Collection and Preparation

The first step in implementing machine learning is collecting the data that will be used to train and test the model. The data should be relevant to the problem that the machine learning model is being built to solve. Once the data has been collected, it needs to be preprocessed and cleaned to remove any inconsistencies or missing values.

Data Exploration and Visualization

The next step is to explore and visualize the data to gain insights into its structure and identify any patterns or trends. Data visualization tools such as matplotlib and seaborn can be used to create visualizations such as histograms, scatter plots, and heat maps.

Feature Selection and Engineering

The features of the data that are relevant to the problem need to be selected or engineered. Feature engineering involves creating new features from existing data that can improve the accuracy of the model.

Model Selection and Training

Once the data has been prepared and features selected or engineered, the next step is to select a suitable machine learning algorithm to train the model. This involves splitting the data into training and testing sets and using the training set to fit the model. Various machine learning algorithms such as linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks can be used to train the model.

Model Evaluation

After training the model, it needs to be evaluated to determine its performance. The performance of the model can be evaluated using metrics such as accuracy, precision, recall, and F1 score. Cross-validation techniques can also be used to test the model’s performance.

Model Tuning

The performance of the model can be improved by tuning its hyperparameters. Hyperparameters are settings that are not learned from the data, but rather set by the user. The optimal values for these hyperparameters can be found using techniques such as grid search and random search.

Deployment and Monitoring

Once the model has been trained and tuned, it needs to be deployed to a production environment. The deployment process involves integrating the model into the business process or system. The model also needs to be monitored regularly to ensure that it continues to perform well and to identify any issues that need to be addressed.

Each of the above steps requires different tools and techniques, and successful implementation requires a combination of technical and business skills.

Choosing the Language and IDE for ML Development

To develop ML applications, you will have to decide on the platform, the IDE and the language for development. There are several choices available. Most of these would meet your requirements easily as all of them provide the implementation of AI algorithms discussed so far.

If you are developing the ML algorithm on your own, the following aspects need to be understood carefully −

The language of your choice − this essentially is your proficiency in one of the languages supported in ML development.

The IDE that you use − This would depend on your familiarity with the existing IDEs and your comfort level.

Development platform − There are several platforms available for development and deployment. Most of these are free-to-use. In some cases, you may have to incur a license fee beyond a certain amount of usage. Here is a brief list of choice of languages, IDEs and platforms for your ready reference.

Language Choice

Here is a list of languages that support ML development −

• Python
• R
• Matlab
• Octave
• Julia
• C++
• C

This list is not essentially comprehensive; however, it covers many popular languages used in machine learning development. Depending upon your comfort level, select a language for the development, develop your models and test.

IDEs

Here is a list of IDEs which support ML development −

• R Studio
• Pycharm
• iPython/Jupyter Notebook
• Julia
• Spyder
• Anaconda
• Rodeo
• Google Colab

The above list is not essentially comprehensive. Each one has its own merits and demerits. The reader is encouraged to try out these different IDEs before narrowing down to a single one.

Machine learning is a rapidly growing field that requires a combination of technical and soft skills to be successful. Machine learning is expanding its applications into different sectors, and choosing to become an expert in machine learning would be a wise career move. So make sure to learn all the skills that would help you improve your capabilities in pursuing machine learning as a career.

Here are some of the key skills required for machine learning −

• Programming Skills
• Statistics and Mathematics
• Data Structures
• Data Preprocessing
• Data Visualization
• Machine Learning Algorithms
• Neural Networks & Deep Learning
• Natural Language Processing
• Problem-solving Skills
• Communication Skills
• Business Acumen

The following image depicts some important skills required for machine learning −

Let us discuss each of the above skills required for machine learning in detail −

Programming Skills

Machine learning requires a solid foundation in programming skills, particularly in languages such as Python, R, and Java. Proficiency in programming allows data scientists to build, test, and deploy machine learning models.

Python is the most popular programming language due to its increased adoption of machine learning algorithms in recent years. It is ideal as it offers various libraries and packages like NumPy, Matplotlib, Sklearn, Seaborn, Keras, TensorFlow, etc., that ease the processes in machine learning. Following are some basics of Python that would help you understand machine learning algorithms −

• Basic data types, Dictionaries, Lists, Sets
• Loops and Conditional statements
• Functions
• List comprehensions

R programming is another popular programming language in the field of machine learning. It might not be as popular as Python, but it makes heavy machine learning tasks easier. Along with learning the fundamentals of the programming language, one should also gain knowledge of the packages that the programming language offers.

Statistics and Mathematics

A strong understanding of statistics and mathematics is essential for machine learning. Data scientists must be able to understand and apply statistical models, algorithms, and methods to analyze and interpret data.

Statistics is used to make inferences in data and draw conclusions. The formulas in statistics are used to interpret data for data-driven decisions. It is broadly categorized into descriptive and inferential statistics. Descriptive analysis involves simplifying and organizing data using concepts like mean, range, variance and standard deviation. Whereas inferential analysis involves considering smaller data to draw conclusions on large datasets using concepts like hypothesis testing, null & alternative testing, etc.

A lot of mathematical formulas are used to develop machine learning algorithms and also to set parameters and evaluate performance metrics. Some concepts of mathematics that are good to know are −

• Algebra − You don’t have to be an expert in all the concepts; you just need to know the basics like variables, constants and functions, linear equations, and logarithms.
• Linear algebra− It is the study of vectors and linear mapping. Get a firm grip on fundamental concepts like vectors, matrices, and eigenvalues.
• Calculus− Understand the concepts of derivatives, integrals, and gradient descent, which help to develop advanced models that identify patterns and predict outcomes.

You might be wondering how mathematics is related to machine learning algorithms. Well, an example of it is that the formula for Linear regression(a type of supervised learning algorithm) is y=ax+b, which is a linear expression in algebra.

To give you a brief idea of what skills you need to acquire, let us discuss some examples

Mathematical Notation

Most of the machine learning algorithms are heavily based on mathematics. The level of mathematics that you need to know is probably just a beginner level. What is important is that you should be able to read the notation that mathematicians use in their equations. For example – if you can read the notation and comprehend what it means, you are ready for learning machine learning. If not, you may need to brush up your mathematics knowledge.

$$f_{AN}(net-\theta)=\begin{cases}\gamma & if\:net-\theta \geq \epsilon\\net-\theta & if – \epsilon

maxα[∑i=1mα−12∑i,j=1mlabel(i)⋅label(j)⋅ai⋅aj⟨x(i),x(j)⟩]

fAN(net−θ)=(eλ(net−θ)−e−λ(net−θ)eλ(net−θ)+e−λ(net−θ))

Probability Theory

Probability is another important fundamental pre-requisite since machine learning is all about making machines learn how to predict. Major concepts in probability that one should be familiar with are random variables, probability density or distribution, etc.

Here is an example to test your current knowledge of probability theory: Classifying with conditional probabilities.

p(ci|x,y)=p(x,y|ci)p(ci)p(x,y)

With these definitions, we can define s Bayesian classification rule −

• If P(c1|x, y) > P(c2|x, y) , the class is c1 .
• If P(c1|x, y) < P(c2|x, y) , the class is c2 .

Optimization Problem

Here is an optimization function,

maxα[∑i=1mα−12∑i,j=1mlabel(i)⋅label(j)⋅ai⋅aj⟨x(i),x(j)⟩]

Subject to the following constraints −

α≥0,and∑i−1mαi⋅label(i)=0

If you can read and understand the above, you are all set.

Data Structures

Gaining good exposure on data structures would help solve real-world problems and build software products. Data structures help to tackle and understand complex problems in machine learning. Some concepts in data structures used in machine learning are arrays, stack, queue, binary trees, maps, etc.

Data Preprocessing

Preparing data for machine learning requires knowledge of data cleaning, data transformation, and data normalization. This involves identifying and correcting errors, missing values, and inconsistencies in the data.

Data Visualization

Data visualization is the process of creating graphical representations of data to help users understand and interpret complex data sets. Data scientists must be able to create effective visualizations that communicate insights from the data. Some data visualization tools that you have to be familiar with are Tableau, Power BI, and many more.

In many cases, you will need to understand the various types of visualization plots to understand your data distribution and interpret the results of the algorithm’s output.

Besides the above theoretical aspects of machine learning, you need good programming skills to code those algorithms.

Machine Learning Algorithms

Machine learning requires knowledge of various algorithms, such as regression, decision trees, random forests, k-nearest neighbors, support vector machines, and neural networks. Understanding the strengths and weaknesses of these algorithms is critical for building effective machine learning models. Learning about all the algorithms would help understand where and how to apply algorithms.

Neural Networks & Deep Learning

Neural networks are algorithms designed to teach computers to possess the ability to function similarly to the human brain. It consists of interconnected nodes or neurons to learn from data.

Deep learning is a subfield of machine learning that involves training deep neural networks to analyze complex data sets. Deep learning requires a strong understanding of neural networks, convolutional neural networks, recurrent neural networks, and other related topics.

Natural Language Processing

Natural language processing (NLP) is a branch of artificial intelligence focusing on the interaction between computers and humans using natural language. NLP requires knowledge of techniques such as sentiment analysis, text classification, and named entity recognition.

Problem-solving Skills

Machine learning requires strong problem-solving skills, including the ability to identify problems, generate hypotheses, and develop solutions. Data scientists must be able to think creatively and logically to develop effective solutions to complex problems.

Communication Skills

Communication skills are essential for data scientists, as they must be able to explain complex technical concepts to non-technical stakeholders. Data scientists must be able to communicate the results of their analysis and the implications of their findings in a clear and concise manner.

Business Acumen

Machine learning is used to solve business problems, and therefore, understanding the business context and the ability to apply machine learning to business problems is essential.

Overall, machine learning requires a broad range of skills, including technical, mathematical, and soft skills. To be successful in this field, data scientists must be able to combine these skills to develop effective machine learning models that solve complex business problems.

Machine learning life cycle is an iterative process of building an end to end machine learning project or ML solution. Building a machine learning model is a continuous process especially with the growing amount of data. Machine learning focuses on improving a system’s performance through training the model with real world data. We have to follow some well-defined steps for making a machine learning project successful. The machine learning life cycle provides us with these well-defined steps or phases.

What is Machine Learning Life Cycle?

The machine learning life cycle is an iterative process that moves from a business problem to a machine learning solution. It is used as a guide for developing a machine learning project to solve a problem. It provides us with instructions and best practices to be used in each phase while developing ML solutions.

The machine learning life cycle is a process that involves several phases from problem identification to model deployment and monitoring. While developing an ML project, each step in the life cycle is revisited many times through these phases. The stages/ phases involved in the end to end machine life cycle process are as follows −

• Problem Definition
• Data Preparation
• Model Development
• Model Deployment
• Monitoring and Maintenance

Let’s discuss the above phases of machine learning life cycle process in detail −

Problem Definition

The first step in the machine learning life cycle is to identify the problem you want to solve. It is a crucial step which helps you start building a machine learning solution for a problem. This process of identifying a problem would establish an understanding about what the output might be, scope of the task and its objective.

As this step lays the foundation for building a machine learning model, the problem definition has to be clear and concise.

This stage involves understanding the business problem, defining the problem statement, and identifying the success criteria for the machine learning model.

Data Preparation

Data preparation is a process to prepare data for analysis by performing data exploration, feature engineering, and feature selection. Data exploration involves visualizing and understanding the data, while feature engineering involves creating new features from the existing data. Feature selection involves selecting the most relevant features that will be used to train the machine learning model.

Data preparation process includes collecting data, preprocessing data, and feature engineering & feature selection. This stage generally also includes exploratory data analysis.

Let’s discuss each step involved in the data preparation phase of machine learning life cycle process −

1. Data Collection

After the problem statement is analyzed, the next step would be collecting data. This involves gathering data from various sources which is given as a raw material to the machine learning model. Few features that are considered while collecting data are −

• Relevant and usefulness − The data collected has to be relevant to the problem statement, and also should be useful enough to train the machine learning model efficiently.
• Quality and Quantity − The quality and quantity of the data collected would directly impact the performance of the machine learning model.
• Variety − Make sure that the data collected is diverse so that the model can be trained with multiple scenarios to recognize the patterns.

There are various sources from where the data can be collected like surveys, existing databases, and online platforms like Kaggle. The sources may be primary data which includes data collected exclusively for the problem statement while the secondary data includes the existing data.

2. Data Preprocessing

The data collected often might be unstructured and messy which causes it to negatively affect the outcomes, hence pre processing data is important to improve the accuracy and performance of the machine learning model. Issues that have to be addressed are missing values, duplicate data, invalid data and noise.

This step of data preprocessing also called data wrangling is intended to make the data more consumable and useful for analytics.

3. Analyzing Data

After the data is all sorted, it is time to understand the data that is collected. The data is visualized and statistically summarized to gain insights.

Various tools like Power BI, Tableau are used to visualize data which helps in understanding the patterns and trends in the data. This analysis will help to make choices in feature engineering and model selection.

4. Feature Engineering and Selection

A ‘Feature’ is an individual measurable quantity which is preferably observed when the machine learning model is being trained. Feature Engineering is the process of creating new features or enhancing the existing ones to accurately understand the patterns and trends in the data.

Feature selection involves the process of picking up features that are consistent and more relevant to the problem statement. The process of feature engineering and selection are used to reduce the size of the dataset which is important to tackle the issue of growing data.

Model Development

In the model development phase, the machine learning model is built using the prepared data. The model building process involves selecting the appropriate machine learning algorithm, algorithm training, tuning the hyperparameters of the algorithm, and evaluating the performance of the model using cross-validation techniques.

This phase mainly consists of three steps, model selection, model training, and model evaluation. Let’s discuss these three steps in detail −

1. Model Selection

Model selection is a crucial step in the machine learning workflow. The decision of choosing a model depends on basic features like characteristics of the data, complexity of the problem, desired outcomes and how well it aligns with the defined problem. This step affects the outcomes and performance metrics of the model.

2. Model Training

In this process, the algorithm is fed with a preprocessed dataset to identify and understand the patterns and relationships in the specified features.

Consistent training of a model by adjusting parameters would improve the prediction rate and enhance accuracy. This step makes the model reliable in real-world scenarios.

3. Model Evaluation

In model evaluation, the performance of the machine learning model is evaluated using a set of evaluation metrics. These metrics measure the accuracy, precision, recall, and F1 score of the model. If the model has not achieved desired performance, the model is tuned to adjust hyper parameters and improve the predictive accuracy. This continuous iteration is essential to make the model more accurate and reliable.

If the model’s performance is still not satisfactory, it may be necessary to return to the model selection stage and continue to model training and evaluation to improve the model’s performance.

Model Deployment

In the model deployment phase, we deploy the machine learning model into production. This process involves integrating the tested model with existing systems to make it available to users, management or other purposes. This also involves testing the model in a real-world scenario.

Two important factors that have to be checked before deploying are whether the model is portable i.e, the ability to transfer the software from one machine to another and scalable i.e, the model need not be redesigned to maintain performance.

Monitor and Maintenance

Monitoring in machine learning involves techniques to measure the model performance metrics and to detect issues in the models. After an issue is detected, the model has to be trained with new data or the architecture has to be modified.

Sometimes when the issue detected in the designed model cannot be solved with training it with new data, the issue becomes the problem statement. So, the machine learning life cycle revamps from analyzing the problem again to develop an improved model.

The machine learning life cycle is an iterative process, and it may be necessary to revisit previous stages to improve the model’s performance or address new requirements. By following the machine learning life cycle, data scientists can ensure that their machine learning models are effective, accurate, and meet the business requirements.