Trending February 2024 # The Winning Approaches From Codefest 2023 – Nlp, Computer Vision And Machine Learning! # Suggested March 2024 # Top 9 Popular

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Introduction

Analytics Vidhya’s hackathons are one of the best ways to evaluate how far you’ve traveled in your data science journey. And what better way than to put your skills to the test against the top data scientists from around the globe?

Participating in these hackathons also helps you understand where you need to improve and what else you can learn to get a better score in the next competition. And a very popular demand after each hackathon is to see how the winning solution was designed and the thought process behind it. There’s a lot to learn from this, including how you can develop your own unique framework for future hackathons.

We are all about listening to our community, so we decided to curate the winning approaches from our recently concluded hackathon series, codeFest! This was a series of three hackathons in partnership with IIT-BHU, conducted between 31st August and 2nd September. The competition was intense, with more than 1,900 aspiring data scientists going head-to-head to grab the ultimate prize!

Each hackathon had a unique element to it. Interested in finding out more? You can view the details of each competition below:

Linguipedia – Natural Language Processing

Enigma – Machine Learning

Vista – Computer Vision

It’s time to check out the winners’ approaches!

Liguipedia – Natural Language Processing

Winners

Abhinav Gupta and Abhishek Sharma.

Problem Statement

The participants were given a list of tweets from customers about various tech firms who manufacture and sell mobiles, computers, laptops, etc. The challenge was to find the tweets which showed a negative sentiment towards such companies or products.

The metric used for evaluating the performance of the classification model was weighted F1-Score.

Winner’s Approach

Abhinav and Abhishek have summarized their approach in a very intuitive manner, explaining everything from preprocessing and feature engineering to model building.

Pre-processing:

Converted words to lower case

Replaced URLs with the word ‘URL‘ and @handle with the word ‘USER_MENTION‘

Removed RT (retweet), – , and ‘

Replaced #hashtag with hashtag

Replaced more than two dots with space, multiple spaces with a single space

Replaced emojis with either EMO_POS or EMO_NEG

Strip space, ” and ‘ from tweet

Removed punctuation

Used stemmer

Feature Extraction:

Top 15,000 unigrams in case of sparse vector representation (one-hot encoding), 90,000 in case of dense vector representation

They used tf-idf method in case of a sparse vector representation

For dense vectors, they used Glove embedding (trained on tweets)

Classifiers used:

Naive Bayes

Maximum entropy classifier

Decision Tree

Random Forest

XGBoost

Support Vector Machine

Multi-layer perceptron

Convolutional neural networks (We experimented with 1, 2, 3 and 4 layers)

LSTM (using the last layer obtained for classification)

LSTM (with attention mechanism)

They hypertuned each of the above classifiers and found that LSTM (with attention mechanism) produced the best result.

Ensemble

They gave one weight to each classifier – Naive Bayes, Maximum entropy classifier, Decision Tree, Random Forest, XGBoost, SVM, Multi-layer perceptron; two weights to CNN, three weights to LSTM (without attention), and five weights to LSTM (with attention)

These weights were obtained after hyperparameter tuning on a portion of data (they divided the train dataset into three parts)

Vista – Computer Vision

Winner

Deepak Rawat.

Problem Statement

The Vista hackathon had a pretty intriguing problem statement. The participants had to build a model that counted the number of people in a given group selfie/photo. The dataset provided had already been split, wherein the training set consisted of images with coordinates of the bounding boxes and headcount for each image.

The evaluation metric for this competition was RMSE (root mean squared error) over the headcounts predicted for test images.

Winner’s Solution

Check out Deepak’s approach in his own words below:

As this was an object detection problem, I implemented Mask R-CNN in Python 3, using Keras and TensorFlow. The model generated bounding boxes and segmentation masks for each instance of an object in the image. It’s based on Feature Pyramid Network (FPN) and a ResNet101 backbone

Mask R-CNN and ResNet101

Mask R-CNN is a two-stage framework:

The first stage scans the image and generates proposals (areas likely to contain an object)

The second stage classifies the proposals and generates bounding boxes and masks

Both stages are connected to the backbone structure.

I have used the ResNet101 backbone. Backbone is an FPN-style deep neural network. It consists of a bottom-up pathway, a top-bottom pathway, and lateral connections:

Bottom-up pathway extracts features from raw images

Top-bottom pathway generates feature pyramid map which is similar in size to the bottom-up pathway

Lateral connections are convolution and add operations between two corresponding levels of the two pathways

Pre-processing

I have used default 1024×1024 image size for better accuracy

Apart from this, I also applied some data augmentation techniques to avoid overfitting and for better generalization

Model Building

I also used pre-trained weights trained on the MS-COCO dataset. MS-COCO is a large-scale object detection, segmentation, and captioning dataset. I used transfer learning and fine-tuning of pre-trained weights to train own custom Mask R-CNN model on the given dataset

Finally, I used a weighted majority voting to ensemble the best models and predict the final values

Enigma – Machine Learning

Winner

Raj Shukla.

Problem Statement

As a part of enigma competition, the target was to predict the number of upvotes on a question based on other information provided. For every question – its tag, number of views received, number of answers, username and reputation of the question author, was provided. Using this information, the participant had to predict the upvote count that the question will receive.

The evaluation metric for this competition was RMSE (root mean squared error). Below is the data dictionary for your reference:

Variable Definition

ID Question ID

Tag Anonymised tags representing question category

Reputation Reputation score of question author

Answers Number of times question has been answered

Username Anonymised user id of question author

Views Number of times question has been viewed

Upvotes (Target) Number of upvotes for the question

Winner’s Solution

Here is Raj’s approach to cracking the Enigma hackathon:

Feature Engineering:

My focus was on feature engineering, i.e., using the existing features to create new features. Below are some key features I cooked up:

The first feature I create was taking the ratio of views and answers. I believe that the ratio is a better metric than the individual number of views or answers. Otherwise, a person with more answers and a high number of total views would get more credit than a person with few (but good) answers and overall fewer views

The second feature is the ratio of Reputation to five times the number of answers. I added the factor 5, because the reputation was roughly 5 times the answers. This ratio intuitively makes more sense

I created another feature using the views and reputation values. I took the absolute value of the difference between views and reputation

Model Building:

I used a linear regression model with polynomial features

I have restricted the degree of polynomial to 2, as increasing this will lead to a more flexible model and increase the chances of overfitting

End Notes

A big thank you to everyone for participating in codeFest 2023! This competition was all about quick and structured thinking, coding, experimentation, and finding the one approach that got you up the leaderboard. In short, what machine learning is all about!

Missed out this time? Don’t worry, you can check out all upcoming hackathons on our DataHack platform and register yourself today!

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How To Make A Winning Machine Learning Resume?

Machine Learning Resume Structure and Formatting

Presenting your skills and experiences in the right format is crucial for ensuring that your Machine Learning resume stands out.

Structure

Professional Header

Concise Summary/Objective Statement

Technical Skills

Education

Work Experience

Projects

Certifications and Training

Publications and Presentations

Awards and Recognition

Professional Affiliations

References

Format

Consider the standard details for a well-structured and neat AI ML resume:

Fonts

Font Size

Line Spacing

Allignment

File Type

Highlighting Relevant Skills and Knowledge

To highlight your relevant skills and knowledge in the machine learning engineer resume, include the following keywords:

AspectSkills and TechniquesMachine Learning AlgorithmsLinear, logistic, decision trees, deep learning, random forestsProgramming LanguagesPython, R, MATLABLibraries and FrameworksKeras, TensorFlow, PyTorch, pandas, scikit-learnData Preprocessing and Feature chúng tôi cleaning, normalization, transformation, feature extractionData Manipulation ToolsNumPy, pandasModel Evaluation and ValidationCross-validation, accuracy, recall, precision, AUC, F1-scoreData VisualizationMatplotlib, SeabornBig Data and Distributed ComputingSpark, HadoopDomain KnowledgeComputer vision, recommendation systems, NLP, time series analysisCollaboration and CommunicationStakeholder collaboration, teamwork, explaining ML to non-tech audiencesContinuous LearningRelevant courses, workshops, certifications, competitionsProblem-Solving and Analytical ThinkingProblem analysis and machine learning application to complex projects

Showcasing Machine Learning Projects 

The following is the suggested format for presenting your machine-learning projects in an ML resume:

Project Title

Project Overview

Data Description

Methodology

Results

Visualization and Interpretation

Impact and Contributions

Technical Skills Demonstrated

Team Collaboration

GitHub or Portfolio Links

Remember to prioritize projects that are closely related to machine learning and provide enough context for recruiters and hiring managers to comprehend the extent and magnitude of what you do.

Demonstrating Education and Certifications

Education:

Degree or highest level of education along with the field of study

University/Institution where you obtained your degree.

Graduation Year

Bullet points with information about your participation in extra-curricular

Certifications:

Certification name

Issuing authority that provided the certification

Year you obtained the certification

Specializations or Concentrations:

For example, “Concentration in Natural Language Processing.” or “Specialization in Computer Vision.” 

Capstone or Thesis Projects:

A brief overview of the project

Objectives

Methodologies and outcomes

Academic Achievements:

Academic honors

Awards

Scholarships 

Relevant Workshops or Seminars:

Workshops, seminars, or conferences attended on machine learning. 

Include the event name, year, and any specific topics covered.

Online Courses / Conferences / Workshops

You can add all the certifications you acquire through workshops, conferences, hackathons, online courses, etc. For example Blackbelt program or DHS attendance certification.

Quantifying Achievements and Impact

To create a job-winning resume, it is best to be mindful of the following tips:

Use numbers and metrics

Highlight business or performance improvements

Showcase data-driven results

Highlight scalability and efficiency improvements

Mention data volume or scale

Use time-related metrics

Focus on ROI or cost savings

Optimizing Resume for ATS

Consider the following tips to develop a resume for an Applicant Tracking System:

Use relevant keywords according to the job description and tailor the resume to the job to pass the ATS screening

Use standard section heading like Summary, Education, Experience, Skills, and Projects.

The formatting has to be simple and consistent.

Optimize file compatibility

Include Relevant Skills Section

Emphasize ML projects

Incorporate industry keywords

Avoid abbreviations/acronyms/jargon

Proofread and review

Key Factors for Landing Your Dream Job

Technical Expertise

Build a solid Machine Learning foundation, keep yourself up-to-date and constantly develop technical abilities through projects, open-source contributions, and research articles.

Networking

Connect with ML community professionals through conferences, webinars, meetups, social media groups, online forums, and platforms like GitHub for valuable information, employment referrals, and mentorship.

Practical Experience

Experience with machine learning through real-world applications, ML projects, portfolio development, and contests to demonstrate problem-solving and competence.

Continuous Learning

Demonstrate commitment to continual learning in machine learning by participating in online classes, workshops, and tutorials, obtaining certificates from credible sites like Analytics Vidhya, Coursera, or edX, and staying current on trends and innovations.

Domain Knowledge

Improve your worth as an ML specialist by gaining knowledge in a specialized topic, for example, computer vision, NLP, finance, autonomous systems, or healthcare.

Collaborative Skills

As ML frequently requires teamwork, demonstrate your ability to interact successfully. Highlight any experience working in multidisciplinary teams or cooperation between industry and academics. Highlight your communication abilities, versatility, and eagerness to learn from others.

Research and Publications

Participate in ML research by releasing papers at conferences, workshops, or journals. Experience conducting research displays your ability to delve deeply into ML topics, undertake experiments, and contribute to the larger ML community. Highlight any significant research contributions.

Communication and Presentation Skills

ML experts must effectively communicate complex concepts to stakeholders with limited technical knowledge, showcasing clear communication skills through verbal and written communication, technical reports, presentations, and non-technical teaching.

Tailored Resumes and Cover Letters

Customise your resume and cover letter to match the job criteria for machine learning, highlighting relevant abilities, experiences, and projects, employing keywords, and displaying the company’s mission and objectives.

Interview Preparation

Practise ML algorithms, coding questions, and data analysis problems to prep up for ML job interviews. Prepare to explain your projects and technical judgments by reviewing ML concepts. During the interview, demonstrate your critical thinking skills, problem-solving ability, and enthusiasm for machine learning.

Pro Tips for Creating a Winning ML Resume 

The design you use should be clutter-free design.

The entire resume should not be filled with text.

Paragraphs are less attractive, so it is good to use bullet points.

Active voice enhances readability.

The use of simple vocabulary and shorter sentences are a must.

Scan the job description for requirements and the same words (if applicable) to your resume. These serve as keywords and help your resume pass ATS.

Avoid having too much content on one page; use more if necessary, but keep the quantity to a minimum.

Edit to create a concise, visually appealing, and comprehensive resume for recruiters.

Make use of popular online tools like Grammarly to check your resume for grammar, fluency, engagement, clarity, etc.

Most importantly, customize your resume for every job specifically. Do not use the same resume for all.

Machine Learning Specialist Resume Sample

Machine Learning Engineeer Resume Sample

Source: Pinterest

Conclusion 

You can add some of these machine learning projects to your resume. If you need guidance in solving these projects, then you must consider taking up our blackbelt program! Get 1:1 mentorship, solve real world projects and learn latest ML topics from experts. This is your chance to become fullstack ML Engineer!

Frequently Asked Questions

Q1. How do you put machine learning on a resume?

A. In the skills part, include machine learning, and in the experience section, emphasize relevant ML projects, algorithms, tools and techniques.

Q2. What skills should I put on my machine learning CV?

A. Machine learning techniques, frameworks (TensorFlow, PyTorch), programming languages (Python, R), data preprocessing, domain expertise, and model evaluation.

Q3. What is an ML CV?

A. An ML CV (Curriculum Vitae) is a document that summarises a person’s academic credentials, ML abilities, research experience, publications, and ML-related initiatives. It is more detailed than a resume.

Q4. Are machine learning projects good for resumes?

A. Yes, machine learning projects enhance resumes by demonstrating problem-solving ability, practical application of skills and real-world effects in the field of ML.

Related

Best Machine Learning Books For Beginners And Experts 2023

Are you searching for the best machine learning books to learn more about the field, broaden your understanding, or even review your knowledge and skills? We have listed the top 11 machine learning books for anyone looking to get into the business as a data science or machine learning practitioner to assist you in selecting a well-structured study path.

Each book is endorsed by Machine Learning specialists and core experts, making it the most comprehensive collection of helpful information in the Machine Learning world. Hence, let’s get started! 

Best Machine learning Books for Beginners and Experts Most of the books below provide an introduction or overview of machine learning through the perspective of a particular subject area, like case studies and algorithms, statistics, or those already familiar with Python.

1. The Hundred-Page Machine Learning Book by Andriy Burkov 

The book combines theory and practice, highlighting essential methodologies such as conventional linear and logistic regression with examples, models, and Python-written algorithms. It’s not entirely for beginners, but it’s an excellent introduction for data professionals who want to learn more about machine learning. 

Price: $37.99 

2. Machine Learning For Absolute Beginners by Oliver Theobald

It’s one of the most useful machine learning books for beginners. To begin reading this book, you don’t need any prior knowledge of coding, maths, or statistics.

It is an excellent introduction to machine learning, in which the author discusses the topic’s definition, methods, and algorithms, as well as its prospects and available tools for students. Detailed explanations and illustrations accompany each machine-learning algorithm in the book so that easier to understand for those studying the basics of machine learning. 

Price: $15.5 

3. Machine Learning for Hackers by Drew Conway and John Myles White:

Sometimes the writers use hackers to describe programmers who assemble code for a particular project or goal, not persons who illegally access other people’s data.

Price: $49 

4. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Geron Aurelien 

This book provides additional assistance in comprehending the ideas and resources needed to create intelligent systems if you’ve already worked with the Python programming language. Each chapter in Hands-On Machine Learning includes tasks that let you put what you’ve learned in earlier chapters into practice.

Price: $42.40 

5. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Price: $48.95 

6. An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani 

This book, which draws its inspiration from “The Elements of Statistical Learning, offers simple instructions for using cutting-edge statistical and machine learning techniques. ISL makes

cutting-edge techniques available to everyone without a statistics or computer science degree. The authors provide explicit R code and straightforward descriptions of available approaches and when to utilize them. It is among the best machine learning books for beginners that intelligent readers should own if they wish to examine complex facts. 

Price: $44 

7. Programming Collective Intelligence by Toby Segaran 

This book demonstrates how to design practical machine learning algorithms to mine and gather data from apps, create applications to access data from websites, and infer the collected data.

This book covers bayesian filtering, collaborative filtering techniques, search engine algorithms, methods to detect groups or patterns, create algorithms in machine learning and non-negative matrix factorization. 

Price: $27.49 

8. Fundamentals of Machine Learning for Predictive Data Analytics by John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy 

The most effective machine learning techniques used in predictive data analytics are depicted in-depth, especially in this starting textbook, which also covers speculative concepts and real-world implementations. Case studies show how these models are applied in a larger corporate environment, and illustrative worked examples are used to supplement the technical and mathematical information.

Price: $80 

9. Machine Learning for Humans by Vishal Maini and Samer Sabri

Everybody should be able to read this book. It is unnecessary to have any prior understanding of calculus, linear algebra, programming, statistics, probability, or any of these topics to benefit from this series.

It is a simple, easy-to-read introduction to machine learning that includes arithmetic, code, and context-rich real-world examples. Your understanding of supervised and unsupervised learning, neural networks, and reinforcement learning will be developed throughout five chapters. It also comes with a list of references for more research.

Price: Free 

10. Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Müller & Sarah Guido 

This is one of the best books for data scientists who are well-versed in Python and want to understand machine learning. You can build robust machine-learning applications with free and open-source Python modules like Scikit-learn, Numpy, Pandas, and Matplotlib.

Price: $48.65 

11. Machine Learning in Action by Ben Wilson 

Ben William pens down the fundamental ideas and procedures for planning, developing, and executing effective machine learning projects from Machine Learning Engineering in Action. You’ll learn software engineering practices that provide consistent cross-team communication and durable structures, such as running tests on your prototypes and putting the modular design into practice.

Every technique presented in this book has been applied to resolve real-world projects based on the author’s significant experience. 

Price: $47.99 

Best Machine Learning Book Reddit Users Recommend 

Deep Learning (Adaptive Computation and Machine Learning series) Ian Goodfellow, Yoshua Bengio, and Aaron Courville wrote the book. It provides the mathematical and conceptual basis, covering pertinent ideas in numerical computation, probability theory, machine learning, and linear algebra.

It also provides research perspectives on theoretical subjects like representation theory, linear factor modeling, autoencoding, structured probabilistic modeling, the partition function, Monte Carlo techniques, deep generative models, and approximation inference.

Price: $80 

Conclusion

10 Machine Learning Innovation Tracking And Management Tools Of 2023

Machine Learning Innovation Tracking and Management tools are algorithms applications of AI Neptune

Neptune is a metadata store for any MLOps workflow. It was built for both research and production teams that run a lot of experiments. It lets you monitor, visualize, and compare thousands of ML models in one place. Neptune supports experiment tracking, model registry, and model monitoring and it’s designed in a way that enables easy collaboration. It is one of the best Machine Learning Innovation Tracking and Management tools of 2023.

Weights & Biases

Weight & Biases is a machine learning platform built for experiment tracking, dataset versioning, and model management. For the experiment tracking part, its main focus is to help Data Scientists track every part of the model training process, visualize models, and compare experiments. One of the best machine learning management tools of 2023.

Comet

Comet is a Machine Learning platform that helps data scientists track, compare, explain and optimize experiments and models across the model’s entire lifecycle, i.e. from training to production. In terms of experiment tracking, data scientists can register datasets, code changes, experimentation history, and models. It is one of the top ML tools for 2023.

Sacred + Omniboard

Sacred is open-source software that allows machine learning researchers to configure, organize, log, and reproduce experiments. Sacred doesn’t come with its proper UI but there are a few dashboarding tools that you can connect to it, such as Omniboard. It is one of the best Machine Learning Innovation Tracking and Management tools of 2023.

MLflow

MLflow is an open-source platform that helps manage the whole machine learning lifecycle. This includes experimentation, but also model storage, reproducibility, and deployment. Each of these four elements is represented by one MLflow component: Tracking, Model Registry, Projects, and Models. It is one of the best Machine Learning Management tools of 2023.

TensorBoard

TensorBoard is the visualization toolkit for TensorFlow, so it’s often the first choice of TensorFlow users. TensorBoard offers a suite of features for the visualization and debugging of machine learning models. Users can track experiment metrics like loss and accuracy, visualize the model graph, project embeddings to a lower-dimensional space, and much more. One of the top ML tools for 2023.

Guild AI

Guild AI is an experiment tracking system for machine learning, available under the Apache 2.0 open-source license. It’s equipped with features that allow you to run analysis, visualization, and diffing, automate pipelines, tune hyperparameters with AutoML, do scheduling, parallel processing, and remote training. It is one of the best Machine Learning Innovation Tracking and Management tools of 2023.

Polyaxon

Polyaxon is one of the best ML tools for reproducible and scalable machine learning and deep learning applications. It includes a wide range of features from tracking and optimization of experiments to model management, run orchestration, and regulatory compliance. The main goal of its developers is to maximize the results and productivity while saving costs.

ClearML

ClearML is an open-source platform, a suite of tools to streamline your ML workflow, supported by the team behind Allegro AI. The suite includes model training logging and tracking, ML pipelines management and data processing, data management, orchestration, and deployment. It is one of the best Machine Learning Innovation Tracking and Management tools of 2023.

Valohai

What Are Machine Learning And Deep Learning In Artificial Intelligence

Devices connected to the Internet are called smart devices. Pretty much everything related to the Internet is known as a smart device. In this context, the code that makes the devices SMARTER – so that it can work with minimal or without any human intervention – can be said to be based on Artificial Intelligence (AI). The other two, namely: Machine Learning (ML), and Deep Learning (DL), are different types of algorithms built to bring more capabilities to the smart devices. Let’s see AI vs ML vs DL in detail below to understand what they do and how they are connected to AI.

What is Artificial Intelligence with respect to ML & DL

AI can be called a superset of Machine Learning (ML) processes, and Deep Learning (DL) processes. AI usually is an umbrella term that is used for ML and DL. Deep Learning is again, a subset of Machine Learning (see image above).

Some argue that Machine Learning is no more a part of the universal AI. They say ML is a complete science in its own right and thus, need not be called with reference to Artificial Intelligence. AI thrives on data: Big Data. The more data it consumes, the more accurate it is. It is not that it will always predict correctly. There will be false flags as well. The AI trains itself on these mistakes and becomes better at what it is supposed to do – with or without human supervision.

Artificial Intelligence cannot be defined properly as it has penetrated into almost all industries and affects way too many types of (business) processes and algorithms. We can say that Artificial Intelligence is based on Data Science (DS: Big Data) and contains Machine Learning as its distinct part. Likewise, Deep Learning is a distinct part of Machine Learning.

The way the IT market is tilting, the future would be dominated with connected smart devices, called the Internet of Things (IoT). Smart devices mean artificial intelligence: directly or indirectly. You are already using artificial intelligence (AI) in many tasks in your daily life. For example, typing on a smartphone keyboard that keeps on getting better on “words suggestion”. Among other examples where you unknowingly are dealing with Artificial Intelligence are searching for things on the Internet, online shopping, and of course, the ever-smart Gmail and Outlook email inboxes.

What is Machine Learning

Machine Learning is a field of Artificial Intelligence where the aim is to make a machine (or computer, or a software) learn and train itself without much programming. Such devices need less programming as they apply human methods to complete tasks, including learning how to perform better. Basically, ML means programming a computer/device/software a bit and allowing it to learn on its own.

There are several methods to facilitate Machine Learning. Of them, the following three are used extensively:

Supervised,

Unsupervised, and

Reinforcement learning.

Supervised Learning in Machine Learning

Usually, it is confirmed using the 80/20 rule. Huge sets of data are fed to a computer that tries and learns the logic behind the answers. 80 percent of data from an event is fed to the computer along with answers. The remaining 20 percent is fed without answers to see if the computer can come up with proper results. This 20 percent is used for cross-checking to see how the computer (machine) is learning.

Unsupervised Machine Learning

Unsupervised Learning happens when the machine is fed with random data sets that are not labeled, and not in order. The machine has to figure out how to produce the results. For example, if you offer it softballs of different colors, it should be able to categorize by colors. Thus, in the future, when the machine is presented with a new softball, it can identify the ball with already present labels in its database. There is no training data in this method. The machine has to learn on its own.

Reinforcement Learning

Machines that can make a sequence of decisions fall into this category. Then there is a reward system. If the machine does good at whatever the programmer wants, it gets a reward. The machine is programmed in a way that it craves maximum rewards. And to get it, it solves problems by devising different algorithms in different cases. That means the AI computer uses trial and error methods to come up with results.

For example, if the machine is a self-driving vehicle, it has to create its own scenarios on road. There is no way a programmer can program every step as he or she can’t think of all the possibilities when the machine is on the road. That is where Reinforcement Learning comes in. You can also call it trial and error AI.

How is Deep Learning different from Machine Learning

Deep Learning is for more complicated tasks. Deep Learning is a subset of Machine Learning. Only that it contains more neural networks that help the machine in learning. Manmade neural networks are not new. Labs across the world are trying to build and improve neural networks so that the machines can make informed decisions. You must have heard of Sophia, a humanoid in Saudi that was provided regular citizenship. Neural networks are like human brains but not as sophisticated as the brain.

There are some good networks that provide for unsupervised deep learning. You can say that Deep Learning is more neural networks that imitate the human brain. Still, with enough sample data, the Deep Learning algorithms can be used to pick up details from sample data. For example, with an image processor DL machine, it is easier to create human faces with emotions changing according to the questions the machine is asked.

The above explains AI vs MI vs DL in easier language. AI and ML are vast fields – that are just opening up and have tremendous potential. This is the reason some people are against using Machine Learning and Deep Learning in Artificial Intelligence.

The Exciting Future Potential Of Machine Learning

Machine learning, the cutting-edge field of artificial intelligence, has emerged as a transformative force with far-reaching implications. As technology evolves at an unprecedented pace, the future of machine learning promises to unlock boundless potential across industries and reshape our world. From healthcare to finance, transportation to entertainment, machine learning is key to revolutionizing processes, enhancing decision-making, and uncovering valuable insights from vast data. This article delves into the exciting trajectory of machine learning, exploring the latest trends, potential applications, and the profound impact it is set to have on businesses, society, and our everyday lives. Get ready to dive into the limitless possibilities of the future.

This article was published as a part of the Data Science Blogathon.

History of Machine Learning

Machine Learning has a rich history dating back to the mid-20th century. It emerged as a subfield of artificial intelligence (AI) to enable computers to learn and improve from data without explicit programming. 

In the 1950s and 1960s, early work on neural networks and the perceptron model laid the foundation for Machine Learning. Researchers explored algorithms and architectures inspired by the functioning of the human brain.

The field experienced a resurgence in the 1990s with the introduction of statistical learning theory and the development of more sophisticated algorithms. Support Vector Machines (SVMs) and Decision Trees gained prominence, allowing for improved pattern recognition and classification.

In the 2000s, the availability of large datasets and increased computational power fueled the growth of Machine Learning. This era witnessed breakthroughs in deep learning, where neural networks with multiple layers demonstrated superior performance in image and speech recognition tasks.

In recent years, Machine Learning has become pervasive in various domains, including healthcare, finance, autonomous vehicles, and natural language processing. Advances in algorithms, data availability, and computing resources have accelerated the development and adoption of Machine Learning techniques.

Looking ahead, Machine Learning continues to evolve with innovations such as reinforcement learning, generative models, and explainable AI. Ethical considerations and responsible use of AI are also gaining prominence.

Why Machine Learning?

Machine Learning is essential for numerous reasons, and its significance continues to grow in today’s data-driven world. Here are some key reasons why we need Machine Learning:

Handling Big Data: With the exponential growth of data, traditional manual analysis methods need to be revised. Machine Learning algorithms can efficiently process and analyze large datasets, extracting valuable insights and patterns humans may miss.

Automation and Efficiency: Machine Learning enables repetitive tasks’ automation, freeing human resources to focus on more complex and creative endeavors. It can perform tasks quickly and accurately, increasing productivity and efficiency in various industries.

Improved Decision Making: Machine Learning algorithms can analyze vast amounts of data, identify patterns, and make predictions or recommendations. This capability empowers decision-makers to make data-driven decisions, improving accuracy and better outcomes.

Fraud Detection and Cybersecurity: Machine Learning algorithms can detect anomalies and patterns in data, enabling efficient fraud detection and cybersecurity. It helps identify suspicious activities, prevent fraudulent transactions, and protect sensitive information.

Autonomous Systems: Machine Learning is fundamental to developing autonomous systems like self-driving cars and drones. It enables these systems to perceive and interpret their surroundings, make decisions, and adapt to changing environments.

Natural Language Processing and Translation: Machine Learning algorithms enable computers to understand and interpret human language. This facilitates applications like speech recognition, language translation, chatbots, and virtual assistants.

Best Applications of Machine Learning 1. Accurate Results for the Search on the Web Engine 2. Accurate Tailor-made Customisation 3. Surge in the Quantum Computing

No commercially-ready quantum hardware or algorithms applications are readily accessible as of now. Nonetheless, in order to get quantum computing off the ground, several government agencies, academic institutions, and think tanks have spent millions. In the futurity of machine learning, quantum computing is set to have an enormous role. As we witness instant processing, rapidly learning, expanded capacities, and enhanced capabilities the introduction of quantum computing into machine learning would metamorphose the domain completely. This implies that in a tiny split moment, complicated issues that we may not have the capacity to tackle with conventional methods, and existing technologies may well be done so.

4. Mass Growth of Data Units:

It would not be unusual to be engrossed with coding, systematic activities, engineering by technology, and information units. It can be predicted that further developments in machine learning can further improve these units’ everyday operations towards the efficient realization of the targets. In the coming decades, machine learning will be one of the cornerstone methods for creating, sustaining, and developing digital applications. It implies that data curators and technology engineers spend comparatively lesser time period in programming, upgrading ML techniques, so instead make them understand and continuously improve their operations.

5. Fully Automated Self-Learning System:

In software engineering, machine learning will be just another component. In addition to standardizing the way people implement machine learning algorithms, open-source frameworks such as Keras, PyTorch, and Tensorflow have also eliminated the basic requirements for doing just that. some of this may sound like utopia, but these types of ecosystems are slowly but steadily coming out, with so many technology, databases, and resources accessible online today. This would lead to environments that really are near or close to zero codings, and so an automated system emerges.

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