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HR and digital marketing may seem like two distinct functions inside a company, where HR is mainly focused on internal processes and enhancing employee experience. On the other hand, digital marketing aims more at external communication and customer engagement.

However, these two functions are starting to overlap where divisions between them are exceedingly blurring. The synergies between them are proving to be extremely important for stronger employer branding.

HR can use digital marketing to build a strong employer brand which can have transformative results on not only recruitment and employee experience but for business success as well.

In this blog, we will navigate through the basics of employer branding and its importance. We will also explore 3 effective ways HR can use digital marketing for effective employer branding to ensure company success.

What is Employer Branding?

It is the practice of managing a company’s reputation as an employer among job seekers and internal employees. It focuses on projecting the company as a desirable place to work by showcasing its unique values, work culture, and overall reputation within the job market.

A strong employer brand not only attracts top talent but also helps in retaining high-performing employees by enhancing job satisfaction and engagement.

It involves various stakeholders within the company including HR, marketing, and executive teams, who must work together to create and promote a consistent and authentic representation of the employer brand.

Roadmap for Effective Employer Branding

In today’s competitive job market, crafting a compelling employer brand is crucial for attracting and retaining top talent.

Here is a roadmap to help you build a strong employer brand.

1. Understand Your Company’s Unique Value Proposition

A powerful employer brand begins by defining your company’s mission, values, vision, and culture. This forms the foundation of your Employee Value Proposition (EVP) which highlights what makes your company unique and why potential employees should join your team.

Your EVP should align with your overall business objectives and resonate with your target talent pool.

2. Conduct an Employer Brand Audit

Before you can improve your employer brand, you need to understand your current standing. Conduct internal and external surveys, monitor social media and career sites, and gather feedback from current employees.

This audit will help you identify strengths and areas for improvement, ensuring your employer branding efforts are grounded in reality.

3. Enhance Your Career Site

Your careers site is often the first point of contact for potential candidates. Make sure it is engaging and informative, showcasing your company’s culture, mission, and values. Use high-quality videos, photos, and testimonials from employees to provide a behind-the-scenes look at what it’s like to work at your company.

4. Write Compelling Job Descriptions

Job descriptions are a critical touchpoint in the candidate’s journey. Ensure they are clear, inclusive, and reflective of your company’s culture. Highlight the unique aspects of the role and the benefits of working at your company to attract the right candidates.

5. Leverage Social Media

Social media is a powerful tool for employer branding. Use platforms like LinkedIn, Instagram, and Twitter to share content that highlights your company culture, employee experiences, and job opportunities. Engage with your audience by responding to comments and starting conversations to build a community around your brand.

6. Focus on Employee Wellbeing

Employees who feel valued and cared for are more likely to promote your brand positively. Offer a robust benefits package, flexible work options, and prioritize work-life balance to enhance employee satisfaction and retention. A healthy workplace culture not only attracts new talent but also retains existing employees.

7. Promote Diversity and Inclusion

A genuine commitment to diversity, equity, and inclusion (DEI) positions your company as a fair and supportive employer. Showcase your DEI initiatives and ensure that your workplace policies reflect these values. Employees in inclusive environments are more likely to become enthusiastic brand advocates.

8. Create a Positive Candidate Experience

The candidate’s experience plays a significant role in shaping your employer brand. Ensure a smooth and respectful hiring process, from application to onboarding. Provide timely feedback and maintain open communication with candidates, even if they are not selected. A positive experience can turn candidates into brand ambassadors.

9. Utilize Employee Advocacy

Your employees are your best brand ambassadors. Encourage them to share their positive experiences and stories on social media and other platforms. Employee-generated content is often seen as more authentic and credible than corporate messaging.

10. Measure and Refine Your Efforts

Track the success of your employer branding initiatives using key metrics such as cost per hire, time to fill, retention rates, and employee satisfaction scores. Regularly review these metrics to refine your strategies and ensure continuous improvement.

By implementing these strategies, you can build a compelling employer brand. It is a strategic and ongoing effort to ensure your brand stands out in the competitive talent market.

Employer Branding and Digital Marketing

The new generation of workers is exceedingly focusing on the company culture and its values when choosing their place of work. With a rise in digital platforms, more and more companies are able to showcase this by increasing their online presence which has placed importance on employer branding.

It usually falls under the HR department to focus on creating a positive brand of the company alongside its work on recruitment, retention, and workplace culture.

Since employer branding is closely linked to creating a workspace that attracts top talent, the way that this message is communicated online is extremely important. This is where digital marketing strategies come into play.

A strong digital marketing strategy for employer branding will be able to communicate a company’s culture and values to a broader audience which not only helps attract talent but also works to build a positive reputation that resonates with potential employees and even customers.

Strategic Link between HR and Digital Marketing

HR and digital marketing both play an important role in creating the perception of a company. Beyond employer branding and using digital tools to enhance recruitment, there is an important strategic link between these two functions which determine the kind of reputation a company has in the online space.

Thus, both functions should also be strategically aligned with each other to make sure that their efforts are fruitful.

Explore key trends of generative AI in digital marketing

Building Better Relationships

Building trust between employer and prospective employees is crucial and having a strong employer brand can help in doing so. When a company is known for creating a positive experience for its employees, it also inspires confidence among its customers, investors, and other stakeholders.

So, in response to creating a strong employer brand a company can also foster better business relationships.

Driving Innovation and Retaining Top Talent

In addition to helping build strong business relationships, attracting top talent through strategic employer branding is what helps drive innovation in companies. Attracting and retaining top talent is essential for competing with the market which helps drive business growth. This enunciates the important link between HR and Digital Marketing. 

Leveraging the strengths of two important functions within a company can determine the success of a business. Companies built on cross-functional collaboration not only have better work cultures but also create workers who have a variety of skills.

This also helps in their own professional and personal development. To create a better employee experience, HR and digital marketing can combine to create a strategic partnership which can lead to building a strong employer brand and give companies a unique strategic advantage.

Ways to Use Digital Media for Employer Branding

Some of the ways that HR teams can leverage social media for employer branding can include: 

1. For Recruitment

Social media platforms can prove to be powerful tools for effective hiring. Recruitment teams can use digital marketing strategies to create social media campaigns that highlight new job openings and company culture. They can use their current employees’ success stories to highlight their work environment and create engagement.

This can help attract talent that aligns with a business’s mission and vision and can also fit into its culture.

Example: LinkedIn Campaigns for Social Media Marketing

LinkedIn is the go-to social media platform for job seekers as well as recruiters. Leveraging social media marketing within LinkedIn campaigns can prove to be a powerful tool for hiring the right talent.

HR teams can do this by creating compelling content that showcases the company’s work environment, growth opportunities, and employee benefits. This will not only help attract job seekers but even workers who may not be actively looking for a new job but are open to new opportunities.

LinkedIn provides a great way of showcasing your company culture as well as current employee experience which makes it one of the best ways to leverage digital media marketing for better hiring.

Learn more about social media recommendation systems

2. For Enhanced Candidate Experience

How the relationship between an employee and a company is created depends largely on their experience during various interactions they have with the potential employer. HR teams can ensure that candidates have a seamless and positive experience that will leave a lasting impression on them.

This sets the tone for their future relationship with the company. Here as well, digital marketing tools can come in handy where throughout the recruitment process, a targeted digital campaign will make this experience positive and engaging. This can include personalized communications, timely updates, and engaging content.

Common practices for an enhanced candidate experience include:

• Automated Campaigns: to keep candidates updated on their application status
• Specific Landing Pages: to consolidate information on cultural values and recruitment processes

Any information about the company and what it expects from its prospective employees can be beneficial for candidates since it will help them in the interview process.

Read more about the use of data science for employee churn rate predictions

3. For Making Informed Decisions

To be able to create better strategies for employer branding, HR teams must rely on data. HR has insights on employee satisfaction, retention, and employee engagement whereas digital marketing offers insights into website traffic, social media engagement, and candidate conversions.

Using both these data sets, HR teams can identify patterns that will help in creating a better employer branding strategy that has insights into what employees are happy with and what future workers want from the workplace.

Future of Employer Branding with Digital Marketing

This blog lists several YouTube channels that can help you get started with llms, generative AI, prompt engineering, and more. 

 Large language models, like GPT-3.5, have revolutionized the field of natural language processing. These models, trained on massive datasets, can generate coherent and contextually relevant text, making them invaluable across numerous applications. 

These YouTube videos will help you learn large language models

Learning about them has become increasingly important in today’s rapidly evolving technological landscape. These models are at the forefront of advancements in artificial intelligence and natural language processing. Understanding their capabilities and limitations empowers individuals and professionals across various fields. 

Top YouTube channels to learn large language models  

Want to delve deeper into large language models? 

Databricks

From the basics of foundation models to fundamental concepts, you can find a ton of useful tutorials and talks that can help you get started with LLMS. Learn about fine-tuning, deployment, and other related concepts with this channel.

Link to channel

Data Science Dojo

Want to get started with the basics of large language model basics? Want to fine-tune your LLM application? Want to build your own ChatGPT? If so, then hop on to this channel now because it covers a number of tutorials, master classes, and free crash courses pertaining to large language models.

Learn about Llama Index, LangChain, Redis, Retrieval Augmented Generation, AI observability, and more with this channel. Subscribe now and start learning.  

Link to channel

AssemblyAI

Learn about Llama index, vector databases and LangChain, explore how to build your own coding assistant with ChatGPT, and apply large language models to audio data with AssemblyAI. This channel offers plentiful learning tutorials within the domain of large language models.

Link to channel  

FreeCodeCamp

FreeCodeCamp offers a wide range of tutorials, including how to build large language models with Python, prompt engineering for web developers, a LangChain course, and more. This channel can help you to get started with the basics.

Link to channel

Mathew Berman

From artificial intelligence to generative art, this channel sheds light on several significant areas including AI art, ChatGPT, large language Models, machine learning, technology, and coding. Subscribe now and start learning.

 Link to channel

IBM Technology

This channel includes several talks and tutorials pertaining to machine learning and generative AI. From useful tutorials like building a Chabot with AI to insightful talks like the rise of generative AI, this channel can help you navigate your learning path.

Link to channel

Yannic Kilcher

Talks to short tutorials, this channel offers a number of resources to learn about large language models, llama 2, ReST for language modeling, retentive networks, and more that can assist you in building your LLM knowledge base.

Link to channel  

Nicholas Renotte

Nicholas shares practical ways to get started with data science, machine learning, and deep learning using a bunch of different tools but mainly Python and Javascript. The channel includes many useful talks like breaking down the generative AI stack, building an AutoGPT, and using Llama 2 70B to rebuild GPT Banker.

  Link to channel

Eve on Tech

Eye on Tech focuses on the latest business technology and IT topics, including AI, DevOps, security, networking, cloud, storage, and more. This channel covers a number of useful talks like the introduction to foundation models, AI buzzwords, conversational AI versus generative AI, and more that can help you get started with the basics.

Link to channel  

Start learning large language models today!

Large language models (LLMs) are a type of artificial intelligence (AI) model that can generate and understand text. They are trained on massive datasets of text and code, which allows them to learn the statistical relationships between words and phrases.

The field of LLMs is rapidly growing, and new models are being developed all the time. In recent years, there have been a number of breakthroughs in LLM technology, including: 

• The development of new training algorithms that allow LLMs to be trained on much larger datasets than ever before. 
• The development of new hardware architectures that can support the training and inference of LLMs more efficiently. 
• The release of open-source LLM models, which has made LLMs more accessible to researchers and developers. 

As a result of these advances, LLMs are becoming increasingly powerful and capable. By understanding LLMs, you can position yourself to take advantage of the opportunities that they create. 

Looking for the best tech YouTube channels in 2023? Look no further than this list of top-ranked channels. With millions of viewers and subscribers tuning in daily, these channels offer informative and engaging content on the latest tech trends, innovations in AI, big data challenges, and analytics trends to look out for. These channels cover a range of topics and interests for YouTube viewers across the world. 

Whether you’re looking for your daily tech fix or researching your next tech purchase, these channels have got you covered. So why wait? Let’s explore the best tech YouTube channels of 2023 in this blog!  

Check out these 8 must-subscribe tech YouTube channels

In this blog post, we’ve compiled a list of eight must-subscribe tech YouTube channels to help you stay on top of the game.

1. Deeplearning.ai

Deeplearning.ai is an education technology company founded by Andrew Ng, a prominent figure in the world of AI and machine learning. The official Deep Learning AI YouTube channel offers video tutorials from the deep learning specialization on Coursera, covering a wide range of topics in AI and machine learning. 

Visit the channel here: Deeplearning.AI 

2. Daniel Bourke

If you are new to the field of data science, then Daniel Bourke’s channel is a great place to start. He covers topics like artificial intelligence, machine learning, deep learning, and data science in a simple, easy-to-understand manner. 

 Visit the channel here: Daniel Bourke

3. Data Science Dojo – Data Science eLearning Company 

Data Science Dojo is an e-learning company that offers comprehensive training programs in data science, machine learning, and artificial intelligence. The company also has a YouTube channel that provides a wealth of free tutorials, tips, and insights on these topics. The videos on this channel are designed to be engaging and easy to understand, with a focus on simplifying complex concepts so that viewers can grasp them quickly.  

Some of the topics covered in these tutorials include Python Programming, R Programming, time series analysis, text analytics, and web scraping, among others. Overall, the Data Science Dojo YouTube channel is a great resource for anyone looking to learn more about data science, regardless of their level of expertise. 

 Link for the channel – Data Science Dojo 

4. Springboard

Springboard’s YouTube channel publishes interviews with data scientists from top companies such as Google, Uber, Airbnb, etc. From these videos, you can get a glimpse of what it’s like to be a data scientist and acquire invaluable advice to apply in your life. 

Check out the channel here: Springboard YouTube channel

5. Yannic Kilcher

Yannic Kilcher’s channel covers a wide range of topics in AI and machine learning, including deep learning, natural language processing, and computer vision. He also covers the latest research papers in the field, making it an excellent resource for those interested in the latest advancements. 

Link for the channel – Yannic Kilcher 

6. StatQuest with Josh Starmer

StatQuest is a fantastic channel for beginners in the world of machine learning. Josh Starmer explains complex topics with the help of illustrations, making them easy to understand. 

 Link for the channel – Statquest with Josh Starmer

7. Sentdex

Sentdex covers a range of topics from data science and machine learning to finance and trading. The channel is an excellent resource for those interested in data science and its applications in the real world. 

 Visit the channel here – Sentdex

8. Data School

Data School is a YouTube channel run by Kevin Markham, who has over 15 years of experience in the field of data science. He covers a range of topics, including data cleaning, visualization, and machine learning, making it a great resource for beginners and experts alike. 

 Visit the channel here – Data School

Conclusion

In the world of AI, data science, and machine learning, it’s essential to keep up with the latest trends and best practices. Fortunately, there are many excellent YouTube channels out there that can help you do just that.  

Let us know in the comments about your favorite tech YouTube channel that helped you grow as a professional. 

In this blog, we will have a look at the list of top 10 Machine Learning Demos offered by Data Science Dojo that will provide ease to use ML (Machine Learning) techniques free.

With more people entering Data Science, Machine Learning and Artificial Intelligence are among the top emerging areas of work in the 21st century. Many people are opting for this area for them. 

The other perspective to view the situation is to utilize these innovative technologies in business. For this reason, recently Data Science Dojo has revamped its platform called Machine Learning Demos. The primary benefit of using these demos is that a few of them are programmed on Azure APIs while others are trained on different ML models, and we can easily use them free of cost.

Machine learning demos from DSD

DSD offers a lot of training and boot camps Data Science Bootcamps to get started with the field, so these demos are also an add-on to our teaching. 

So, if you are interested in exploring the practical applications of this modern tech, this set of free ML demos can help you a lot in many ways. The top ones are listed below go and check them out: 

1. Cleanse stop words: 

This demo uses the Azure services for the backend while according to the user point of view, it has quite easy to use Interface and we can use this demo to make text data cleaner for ML models. Go to Cleanse Stop words demo input your text data and get the cleaned text in just one click.

2. Text entity extractor: 

Entity extraction helps to sort the unstructured data and find valuable information from the given text. This demo is based on Azure API. It’s simple UI (User Interface) provides an effortless way to use azure services for entity extraction. Go to Text Entity Extractor demo and just input your text to categorize it based on semantic type.  

3. Opinion mining: 

 Sentiment analysis, also referred to as opinion mining, is one of the key techniques in Natural Language Processing (NLP). The business view of opinion mining is highly appreciable as it leads to extracting sentiments from customers’ feedback. This demo is based on Azure Text API while its UI efficiently separates the praises and complaints from the given text. Try Opinion Mining Demo! 

4. American sign language detection: 

 Systems for recognizing sign language are being developed to make it easier for signers and non-signers to communicate. This demo is built on Python famous package called Mediapipe with some other packages like Tensorflow, Cvzone and Numpy. Go to Sign Language demo, and when the user inputs an alphabet using the right hand in the camera it detects the alphabet. 

5. Wikipedia article scrape:  

Besides the fact that Wikipedia is free, it is an also open multilingual content online encyclopedia. This demo is based on famous python packages Wikipedia and Worcloud. This demo really helps in research to find the articles. Go to Wikipedia Article Scrape, and give the article name and language code and scrape the article to extract content, linked articles etc. 

6. Credit card streamer: 

We have a few Data streaming demos; Credit Card Streamer is one from that category. This demo is based on Azure SDK in python, give the endpoint string of Event Hub, and set the stream, it will connect this app to Event Hub and your swipes send to Azure Event Hub. Go to Credit Card Streamer and try. 

7. Paraphrasing: 

The basic objective of paraphrasing is to translate the original message into your own words to demonstrate that you have understood the paragraph sufficiently to restate it.

This demo is built on Python, and it uses a transformer library with some other famous Python packages like PyTorch, timm, sentence piece, and sentence-splitter. Go to the Paraphrasing demo, it uses natural language processing to create a paraphrasing of your input text. 

8. Titanic survival predictor: 

 This demo is unique from our predictive demos category and is based on Azure API. It will predict that the person would survive the Titanic Disaster based on the given required inputs. The backend is built on Python while the UI gives the message based on chances of survival. Go to the Titanic Survival Predictor demo and try it once (just for curiosity 😊) 

9. Question generator:  

This demo is built on a Python library transformer. Transformers package contains over 30 pre-trained models and 100 languages, along with eight major architectures for natural language understanding (NLU) and natural language generation (NLG). 

In educational purposes, we can use this demo. It saves teachers time and effort to make a quiz related to the given content. Go to Question Generator demo, just give the context of the question and the correct answer then click submit, this demo automatically generates the Question based on given inputs. 

10. Bike sharing demand predictor: 

 The last demo we are going to discuss in this blog is also from the list of predictive demos category. This demo uses Azure API for predicting the demand of bike sharing while the UI allows you to change the inputs dynamically from sliders. Must go and check Bike Sharing Demand Predictor.  

Stay updated for interesting ML demos

Recently in 2022, we have revamped our demo site completely. And now we have 29+ demos on our site. We have categorized them into categories for the ease of users so that they can pick the demo based on tasks, these are only a few top ML demos, other than these, we do have many informative and interesting demos on this site. 

Once you are familiar with data-driven tasks it is most important to utilize them for improving our businesses, we have received a lot of positive feedback from the customers this year that motivates us to improve and add more advanced demos to our site. I assure you; it is worth it to use, go, and explore: 

At Data Science Dojo, we publish blogs every day to keep our audience updated and informed about current industry trends. Here are the top 10 blogs of 2022, based on page views/traffic. Have a look at the top 10 blogs that you should not miss reading before the year-end.

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In this article, we will be highlighting the top 10 discussions of the year that have garnered a lot of attention from learners at Data Science Dojo. 

Every year Data Science Dojo serves a lot of students from different geographics and academic backgrounds with its detailed courses and informative sessions. All these students have one single goal; to be equipped with enough baseline understanding of data science that they can learn and become experts eventually on their own.

In order to expedite the process and make it more collaborative, we make use of different resources, and our discussion platform is one of the most effective ones. 

1. Operators in query matching   

Operators are an important aspect of query matching, as they allow users to specify the type of relationship, they are seeking between different search terms. The ‘LIKE’ operator is one of the most used ones and helps the user in separating out data with specific characteristics. This Q/A will help you understand the LIKE operator and will also provide you with a code runner to practice as well. 

Check out the discussion here: Operators in query matching 

2. Alias command 

The alias command is an important tool for improving the efficiency and organization of command line workflows. It is widely used by system administrators, developers, and other users who work with the command line on a regular basis. This short snippet on Alias Command will give you a basic understanding of what Alias command can do and how it can help you improve the organization of your code.

Check out the discussion here: Alias command

3. Data wrangling in Python

 Data wrangling, also known as data munging or data preparation, is the process of cleaning, organizing, and transforming raw data into a format that is more suitable for analysis and visualization.

This is a crucial step in the data science process, as it helps to ensure that the data is in a usable and accurate form before it is analyzed. If you are looking for the shortest crash course on data wrangling, this discussion is for you, and it comes with some very easy hands-on practice exercises as well.

Check out the discussion here: Data wrangling in Python

4. Clauses of SELECT query

The SELECT clause is a crucial part of the SQL language, as it is used to specify the columns or expressions that should be included in the results of a query. The SELECT clause is the first command that is taught to SQL students. If you want to take the first step towards learning SQL, this short article is the best way to take that first step. 

Check out the discussion here: Clauses of SELECT query

5. JOIN and its different types 

Now that you have learned SELECT, the JOIN is the next step. The JOIN clause is a key component of the SQL language, as it allows users to combine data from multiple tables in a single query.

The JOIN clause is a powerful tool for working with data in SQL, and a good understanding of its various clauses is essential for anyone working with data in a SQL database. But before you take on a SQL database, read our small but concise intro to the JOIN clause in this Q/A. 

Check out the discussion here: JOIN and its different types

6. Lambda functions 

Lambda functions, also known as anonymous functions, are a powerful tool in programming languages that support them, including Python, C#, and JavaScript. They allow developers to create small, self-contained functions that can be passed as arguments to other functions or used to define the behavior of other objects. 

Use our code runner in this discussion and create your first lambda function today. 

Check out the discussion here: Lambda functions

7. Matplotlib 

Matplotlib is a powerful and widely used library for creating data visualizations in Python. The ability to effectively plot and visualize data is an important skill for any data scientist or analyst and knowing the fundamentals of plotting with matplotlib is essential for anyone working with data in Python.

If you are a Python beginner and want to use it to create effective visualizations, this Q/A can get you started right away.

Check out the discussion here: Matplotlib

8. Sub-query

A subquery is a query that is nested within another query, and it is used to retrieve data that is used in the outer query. If you are struggling with the idea of nesting and how you can use it more effectively, visit this discussion and get answers instantly. 

Check out the discussion here: Sub-query 

9. Break, continue, and pass 

The break, continue, and pass statements are control flow statements. These are commonly used in programming languages such as Python, C, and Java.

The ability to use break, continue, and pass statements is an important skill for any programmer, as it allows them to control the flow of their code and implement the desired logic and behavior more effectively. If you want to start using the break, continue and pass statements in your code today but need to practice before you give it a go, this Q/A is for you. 

Check out the discussion here: Break, continue, and pass

10.Window functions 

Window functions, also known as OVER functions, are a powerful tool in SQL that allow users to perform calculations or aggregations on a set of rows, rather than on the entire table.

The ability to use window functions is an important skill for anyone working with data in a SQL database and understanding their different types and uses can help users to more effectively and efficiently retrieve and manipulate data.

If these sound like the skills that you need, visit this discussion and start practicing right away. 

Check out the discussion: Window functions

Learn from our discussion platform

These discussions cover a wide range of topics, but we make sure that these topics cover fundamentals so that they become the first step towards our students’ data science learning journey. 

Whether you’re a beginner or a seasoned data scientist, these discussions will provide you with a better understanding of the fundamental concepts along with a bit of hands-on practice, thanks to our embedded code runner.

Written by Habiba Khan

There are several informative data science podcasts out there right now, giving you everything you need to stay up to date on what’s happening. We previously covered many of the best podcasts in this blog, but there are lots more that you should be checking out. Here are 10 more excellent podcasts to try out.

10 Best Podcasts on Data Science You Must Listen To

1. Analytics Power Hour 

Every week hosts, Michael Helbling, Tin Wilson, and Moe Kiss cover a different analytics topic that you may want to know about. The show was founded on the premise that the best discussions always happen at drinks after a conference or show. 

Recent episodes have covered topics like analytics job interviews, data as a product, and owning vs. helping in analytics. There are a lot to learn here, so they’re well worth a listen. 

2. DataFramed

This podcast is hosted by DataCamp, and in it, you’ll get interviews with some of the top leaders in data. “These interviews cover the entire range of data as an industry, looking at its past, present, and future. The guests are from both the industry and academia sides of the data spectrum too” says Graham Pierson, a tech writer at Ox Essays and UK Top Writers.   

There are lots of episodes to dive into, such as ones on building talent strategy, what makes data training programs successful, and more.

3. Lex Fridman Podcast

If you want a bigger picture of data science, then listen to this show. The show doesn’t exclusively cover data science anymore, but there’s plenty here that will give you what you’re looking for. 

You’ll find a broader view of data, covering how data fits in with our current worldview. There are interviews with data experts so you can get the best view of what’s happening in data right now.

4. The Artists of Data Science

This podcast is geared toward those who are looking to develop their career in data science. If you’re just starting, or are looking to move up the ladder, this is for you. There’s lots of highly useful info in the show that you can use to get ahead. 

There are two types of episodes that the show releases. One is advice from experts, and the others are ‘happy hours, where you can send in your questions and get answers from professionals.

5. Not So Standard Deviations

This podcast comes from two experts in data science. Roger Peng is a professor of biostatistics at John Hopkins School of Public Health, and Hilary Parker is a data scientist at Stitch Fix. They cover all the latest industry news while bringing their own experience to the discussion.

Their recent episodes have covered subjects like QR codes, the basics of data science, and limited liability algorithms.

Find out other exciting  18 Data Science podcasts

6. Gradient Dissent

Released twice a month, this podcast will give you all the ins and outs of machine learning, showing you how this tech is used in real-life situations. That allows you to see how it’s being used to solve problems and create solutions that we couldn’t have before. 

Recent episodes have covered high-stress scenarios, experience management, and autonomous checkouts.

7. In Machines We Trust

This is another podcast that covers machine learning. It describes itself as covering ‘the automation of everything, so if that’s something you’re interested in, you’ll want to make sure you tune in. 

“You’ll get a sense of what machine learning is being used for right now, and how it impacts our daily lives,” says Yvonne Richards, a data science blogger at Paper Fellows and Boom Essays. The episodes are around 30 minutes long each, so it won’t take long to listen and get the latest info that you’re looking for.

8. More or Less

This podcast covers the topic of statistics through noticeably short episodes, usually 8 minutes or less each. You’ll get episodes that cover everything you could ever want to know about statistics and how they work.   

For example, you can find out how many swimming pools of vaccines would be needed to give everyone a dose, see the one in two cancers claim debunked, and how data science has doubled life expectancy.

9. Data Engineering Podcast

This show is for anyone who’s a data engineer or is hoping to become one in the future. You’ll find lots of useful info in the podcast, including the techniques they use, and the difficulties they face. 

Ensure you listen to this show if you want to learn more about your role, as you’ll pick up a lot of helpful tips.

10. Data viz Today

This show doesn’t need a lot of commitment from you, as they release 30-minute episodes monthly. The podcast covers data visualization, and how this helps to tell a story and get the most out of data no matter what industry you work in.

Share with us Exciting Data Science Podcasts

These are all great podcasts that you can check out to learn more about data science. If you want to know more, you can check out Data Science Dojo’s informative sessions on YouTube. If we missed any of your favorite podcasts, do share them with us in the comments!

These interviews cover the entire range of data as an industry, looking at its past, present, and future. The guests are from both the industry and academia sides of the data spectrum too, says Graham Pierson, a tech writer at Academized.

In this blog, we have gathered the top 10 machine learning books. Learning this subject is a challenge for beginners. Take your learning experience one step ahead with these top-rated ML books on Amazon. 

1. Machine Learning: 4 Books in 1

Machine Learning: 4 Books in 1 is a complete guide for beginners to master the basics of Python programming and understand how to
build artificial intelligence through data science. This book includes four books: Introduction to Machine Learning, Python Programming for
Beginners, Data Science for Beginners, and Artificial Intelligence for Beginners. It covers everything you need to know about machine learning, including supervised and unsupervised learning, regression and classification, feature engineering, model selection, and more. Muhammad Junaid – Marketing manager, BTIP

With clear explanations and practical examples, this book will help you quickly learn the essentials of machine learning and start building your own AI applications.

2. Mathematics for Machine Learning

Mathematics for Machine Learning is a tool that helps you understand the mathematical foundations of machine learning, so that you
can build better models and algorithms. It covers topics such as linear algebra, probability, optimization, and statistics. With this book, you
will be able to learn the mathematics needed to develop machine learning models and algorithms. Daniel – Founder, Gadget FAQs

This book is excellent for brushing up your mathematics knowledge required for ML. It is very concise while still providing enough details to help readers determine important parts. This is the go-to if you need to review some concepts or brush up on my knowledge in general.

This book is not recommended if you have absolutely no prior math experience though as it can be hard to digest and sometimes, they would skip parts here and there in proofs and examples. Especially for the probability section, the concepts will be very hard to grasp without prior knowledge

3. Linear Algebra and Optimization for Machine Learning

This textbook provides a comprehensive introduction to linear algebra and optimization, two fundamental topics in machine learning. It
covers both theory and applications and is suitable for students with little or no background in mathematics. Allan McNabb, VP – Image Building Media

The book begins with a review of basic linear algebra, before moving on to more advanced topics such as matrix decompositions, eigenvalues and eigenvectors, singular value decomposition, and least squares methods. Optimization techniques are then introduced, including gradient descent, Newton’s Method, conjugate gradient methods, and interior point methods.

4. The Hundred-Page Machine Learning Book

If we have to teach machine learning to someone in juts few weeks, it is a lot better not to bother starting from scratch, instead hand over this book to the learners, because no doubt Andriy Burkov does a better job than we could do to quickly teach this vast subject in a limited time.

The book has a litany of rave reviews from some of the biggest names in tech, with scores more five-star reviews to boot, and you can see why. Burkov keeps his lessons concise and as easy to understand as possible given the subject matter, but still drills down into the details where necessary. Overall, the book excels at linking together complicated and sometimes seemingly unrelated concepts into a coherent whole. Peter, CEO and founder – Lantech

The book is very well organized, giving the reader an introduction and discussion on the mathematical notation used, a well written chapter that discusses several quite common algorithms, talks about best practices (like feature engineering, breaking up the data into multiple sets, and tuning the model’s hyperparameters), digs deeper into supervised learning, discusses unsupervised learning, and gives you a taste of a variety of other related topics.

This is a well-rounded book, far more so than most books I’ve read on machine learning or artificial intelligence. After reading through this, you will feel like you can competently discuss the subject, read one of the simpler machine learning research papers, and not be totally lost on the mathematics involved. The language used is concise and reads very well, showing very tight editing

5. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron

It’s good for new programmers without over-simplifying. I’d recommend it for really getting into practice exercises. It’s a book you need to take your time with, but you’ll learn a lot from it. One thing observed by the learners of this book as a con is that the quality of the print varies, but the quality of its content makes it more than worth it. Chris Martinez – Founder of Idiomatic

6. Machine Learning for Absolute Beginners by Oliver Theobald

Machine Learning is easy only when you have the right teacher and an appropriate reference book. Most of us fail to understand the importance of simple concepts that help us understand complex ones. Therefore, I recommend using Oliver Theobald’s *Machine Learning for Absolute Beginners *as the base reference book. Layla Acharya – Owner at Edwize

This book uses simple language to explain to the reader and teaches Machine learning from the scratch. Although non-technical people will find this book more relatable, people wanting to make a career in the machine learning field can benefit equally. It also has good references that can help a person who wants to learn like an expert.

7. Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD by Jeremy Howard and Sylvain Gugger

This book is very well-rated, and it’s helped me a lot in understanding the basics of deep learning.

The main reason readers suggest this book is because it’s very accessible and easy to follow. As the authors themselves say, you don’t need a PhD to understand and use the concepts in the book, and it follows a top-down approach (starting with the applications and working backwards to the theory). So, you’ll first have fun building cool applications and then gradually learn the underlying theory as you go. Ed Shway – Owner & Writer at ByteXD.com

Fast AI have kept updating their courses and library, so you might want to check out their website (https://www.fast.ai/) for the latest and greatest Just this July they released a latest version of the course that the book is associated with (https://course.fast.ai/).

Furthermore, the book also comes in a free online version https://github.com/fastai/fastbook. Since the *Fast AI team put all this effort and made every resource available for free, you can be sure they’re in it for the love of the game and to help the community*, rather than to make a quick buck. So, this book is definitely worth your time.

The first practical applications it teaches you is in computer vision – you’ll build an image classifier, which you can use to tell apart different
kinds of images. For example, you can use it to distinguish between different kinds of animals. It will be very easy to follow along and build
this classifier yourself.

8. Bayesian Reasoning and Machine Learning by David Barber

It’s a real must-have for beginners interested in deepening their knowledge of machine learning in an engaging way. The book covers topics such as dynamic and probabilistic models, approximate interference, graphical models, Naive Bayes algorithms, and more. What makes it worth checking out is the fact that the book is full of examples and exercises, which makes it a hands-on guide full of useful practice rather than dry theoretical frameworks. Marcin Gwizdala – Chief Technical Officer – Tidio

For relative beginners, Bayesian techniques began in the 1700s to model how a degree of belief should be modified to account for new evidence. The techniques and formulas were largely discounted and ignored until the modern era of computing, pattern recognition and AI, now machine learning.

The formula answers how the probabilities of two events are related when represented inversely, and more broadly, gives a precise mathematical model for the inference process itself (under uncertainty), where deductive reasoning and logic becomes a subset (under certainty, or when values can resolve to 0/1 or true/false, yes/no etc. In “odds” terms (useful in many fields including optimal expected utility functions in decision theory), posterior odds = prior odds * the Bayes Factor.

9. Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools by Eli Stevens, Luca Antiga, Thomas Viehmann

This book provides a good and fairly complete description of the basic principles and abstractions of one of the most popular frameworks for
Machine Learning – PyTorch.

It’s great that this book is written by the creator and key contributors of PyTorch, unlike many books that claim to be a definitive treatise, it is not overloaded with non-essential details, the emphasis is on making the book practical. The book gives a reader a deep understanding of the framework and methods for building and training models on it (with advanced best practices) describing what is under the hood. Vitalii Kudelia, TUTU – Machine Learning Scientist

There is an example of solving a real-world problem in this book, it analyzes the problem of searching for malignant tumors on a computer
diagram with an analysis of approaches, possible errors, options for improvements, and provides code examples.

It also includes options for translating the model into production, using the models in other programming languages, and on mobile devices.
As a result, the book is highly useful for understanding and mastering the framework. Mastering PyTorch helps not only in computer vision, but also in other areas of deep learning, such as, for example, natural language processing.

10. Introduction to Machine Learning by Ethem Alpaydin

This comprehensive text covers everything from the basics of linear algebra to more advanced topics like support vector machines. In addition to being an excellent resource for students, Alpaydin’s book is also very accessible for practitioners who want to learn more about this exciting field. Rajesh Namase – Co-Founder and Tech Blogger

For learners, this is the best book for machine learning for a number of reasons. First, the book provides a clear and concise introduction to the basics of machine learning. Second, it covers a wide range of topics in machine learning, including supervised and unsupervised learning, feature selection, and model selection.

Third, the book is well-written and easy to understand. Finally, the book includes exercises and solutions at the end of each
chapter, which is extremely helpful for readers who want to learn more about machine learning.

Share more machine learning books with us 

If you have read any other interesting machine learning books, share with us in the comments below and let us help the learners to begin with computer vision. 

What can be a better way to spend your days listening to interesting bits about trending AI and Machine learning topics? Here’s a list of the 10 best AI and ML podcasts.

1. Future of Data and AI Podcast

Hosted by Data Science Dojo

Throughout history, we’ve chased the extraordinary. Today, the spotlight is on AI—a game-changer, redefining human potential, augmenting our capabilities, and fueling creativity. Curious about AI and how it is reshaping the world? You’re right where you need to be.

The Future of Data and AI podcast hosted by the CEO and Chief Data Scientist at Data Science Dojo, dives deep into the trends and developments in AI and technology, weaving together the past, present, and future. It explores the profound impact of AI on society, through the lens of the most brilliant and inspiring minds in the industry.

2. The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Hosted by Sam Charrington

Artificial intelligence and machine learning are fundamentally altering how organizations run and how individuals live. It is important to discuss the latest innovations in these fields to gain the most benefit from technology. The TWIML AI Podcast outreaches a large and significant audience of ML/AI academics, data scientists, engineers, tech-savvy business, and IT (Information Technology) leaders, as well as the best minds and gather the best concepts from the area of ML and AI.  

The podcast is hosted by a renowned industry analyst, speaker, commentator, and thought leader Sam Charrington. Artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science, and other technologies are discussed. 

3. The AI Podcast

Hosted by NVIDIA

One individual, one interview, one account. This podcast examines the effects of AI on our world. The AI podcast creates a real-time oral history of AI that has amassed 3.4 million listens and has been hailed as one of the best AI and machine learning podcasts.

They always bring you a new story and a new 25-minute interview every two weeks. Consequently, regardless of the difficulties, you are facing in marketing, mathematics, astrophysics, paleo history, or simply trying to discover an automated way to sort out your kid’s growing Lego pile, listen in and get inspired.

Here are 6 Books to Help you Learn Data Science

4. DataFramed

Hosted by DataCamp

DataFramed is a weekly podcast exploring how artificial intelligence and data are changing the world around us. On this show, we invite data & AI leaders at the forefront of the data revolution to share their insights and experiences into how they lead the charge in this era of AI.

Whether you’re a beginner looking to gain insights into a career in data & AI, a practitioner needing to stay up-to-date on the latest tools and trends, or a leader looking to transform how your organization uses data & AI, there’s something here for everyone.

5. Data Skeptic

Hosted by Kyle Polich

Data Skeptic launched as a podcast in 2014. Hundreds of interviews and tens of millions of downloads later, it is a widely recognized authoritative source on data science, artificial intelligence, machine learning, and related topics. 

The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence, and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.

Data Skeptic runs in seasons. By speaking with active scholars and business leaders who are somehow involved in our season’s subject, we probe it. 

Data Skeptic is a boutique consulting company in addition to its podcast. Kyle participates directly in each project the team undertakes. Our work primarily focuses on end-to-end machine learning, cloud infrastructure, and algorithmic design. 

 Pro-tip: Enroll in the Large Language Models Bootcamp today to get ahead in the world of Generative AI

6. Last Week in AI

Hosted by Skynet Today

Tune in to Last Week in AI for your weekly dose of insightful summaries and discussions on the latest advancements in AI, deep learning, robotics, and beyond. Whether you’re an enthusiast, researcher, or simply curious about the cutting-edge developments shaping our technological landscape, this podcast offers insights on the most intriguing topics and breakthroughs from the world of artificial intelligence.

7. Everyday AI

Hosted by Jordan Wilson

Discover The Everyday AI podcast, your go-to for daily insights on leveraging AI in your career. Hosted by Jordan Wilson, a seasoned martech expert, this podcast offers practical tips on integrating AI and machine learning into your daily routine.

Stay updated on the latest AI news from tech giants like Microsoft, Google, Facebook, and Adobe, as well as trends on social media platforms such as Snapchat, TikTok, and Instagram. From software applications to innovative tools like ChatGPT and Runway ML, The Everyday AI has you covered. 

8. Learning Machines 101

Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. The objective of this podcast series is to inform students and instructors about the advanced technologies introduced by AI and the following: 

•  How do these devices work? 
• Where do they come from? 
• How can we make them even smarter? 
• And how can we make them even more human-like

9. Practical AI: Machine Learning, Data Science

Hosted by Changelog Media

Making artificial intelligence practical, productive, and accessible to everyone. Practical AI is a show in which technology professionals, businesspeople, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs (Generative adversarial networks), MLOps (machine learning operations) (machine learning operations), AIOps, and more).

The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you! 

10. The Artificial Intelligence Podcast

Hosted by Dr. Tony Hoang

The Artificial Intelligence podcast talks about the latest innovations in the artificial intelligence and machine learning industry. The recent episode of the podcast discusses text-to-image generators, Robot dogs, soft robotics, voice bot options, and a lot more.

Have we missed any of your favorite podcasts?

 Do not forget to share in the comments the names of your favorite AI and ML podcasts. Read this amazing blog if you want to know about Data Science podcasts.

In this blog, we have gathered the top 7 computer vision books. Learning this subject is a challenge for beginners. Take your learning experience one step ahead with these seven computer vision books. Explore a range of topics, from Computer vision to Python. 

1. Learning openCV 4 computer vision with Python 3 book by Joe Minichino and Joseph Howse:

This book will teach you how to create a computer vision system using Python. You will learn how to use the OpenCV library, which is a cross-platform library that has been used in many research and commercial projects. Joe and Joseph in this book introduce computer vision and OpenCV with Python programming language. 

Both novices and seasoned pros alike will find something of use in this book’s extensive coverage of the subject of CV. It explains how to use Open CV 4 and Python 3 across several platforms to execute tasks like image processing and video analysis and comprehension.

Machine learning algorithms and their many uses will be covered in this course. With these ideas in hand, you may design your image and video object detectors!  ~ Adam Crossling, Marketing manager at Zenzero.

2. Multiple view geometry in computer vision book by Richard Hartley:

This book discusses the use of geometry and algebra in image reconstruction, with applications to computer vision. In this book, Richard discusses the geometry of images and how they are processed in this area. The book covers topics such as image formation, camera models, image geometry, and shape from shading. 

The main goal of this book is to provide a comprehensive introduction to computer vision by focusing on the geometric aspects of images. This article describes a wide variety of tactics, from traditional to innovative, to make it very evident when particular approaches are being employed.  

Camera projection matrices, basic matrices (which project an image into 2D), and the trifocal tensor are all introduced, along with their algebraic representations, in this book. It explains how to create a 3D model using a series of photographs taken at various times or in different sequences.

3. Principles, algorithms, applications, learning book by E. R. Davies:

New developments in technology have given rise to an exciting academic discipline: computer vision. The goal of this field is to understand information about objects and their environment by creating a mathematical model from digital images or videos, which can be used to extract meaningful data for analysis or classification purposes.  

This book teaches its readers not just the basics of the subject but also how it may be put to use and gives real-world scenarios in which it might be of benefit.

4. Deep learning for vision systems by Mohamed Elgendy:

This book should be the go-to text for anyone looking to learn about how machine learning works in AI (Artificial Intelligence) and, fundamentally, how the computer sees the world. By using only the simplest algebra a high school student would be able to understand, they can demonstrate some overly complicated topics within the AI engineering world.  

Learn about deep learning using Python

Hands-on deep learning using Python in Cloud

Through illustrations as well as Elgendy’s expertise, the book is the most accurate yet simplest way to understand computer vision for the modern day. ~ Founder & CEO of Lantech

5. Digital image processing by Rafael C. GONZALES and Richard E. Woods:

Image processing is one of the topics that form the core of Computer Vision and DIP by Gonzalez is one of the leading books on the topic. It provides the user with a detailed explanation of not just the basics like feature extraction and image morphing but also more advanced concepts like wavelets and superpixels.

It is good for both beginners and people who need to refresh their basics. It also comes with MATLAB exercises to help the reader understand the concepts practically.

Senior Machine Learning Developer, AltaML  Rafael C. GONZALES and Richard E. Woods wrote this book to provide an introduction to digital image processing for undergraduate students and professionals who are interested in this field.

The book covers the fundamentals of image formation, sampling and quantization, the design of analog-to-digital converters, image enhancement techniques such as filtering and edge detection, image compression techniques such as JPEG and MPEG, digital watermarking techniques for copyright protection purposes and more advanced topics like fractal analysis in texture synthesis.

6. Practical machine learning for computer vision: End-to-end machine by Martin Görner, Ryan Gillard, and Valliappa Lakshmanan:

Learning for Images. This tutorial shows how to extract information from images using machine learning models. ML (Machine Learning) engineers and data scientists will learn how to use proven ML techniques such as classification, object detection, autoencoders, image generation, counting, and captioning to solve a variety of image problems.  

You will find all aspects of deep learning from start to finish, including dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interoperability.

Valliappa Lakshmanan, Martin Görner, and Ryan Gillard of Google show how to use robust ML architecture to develop accurate and explainable computer vision ML models and put them into large-scale production in a flexible and maintainable manner.

You will learn how to use TensorFlow or Keras to design, train, evaluate, and predict models. Senior IT Director at Propnex 

Further, this book provides a great introduction to deep end-to-end learning for computer vision, including how to design, train, and deploy models. You will learn how to select appropriate models for various tasks, preprocess images for better learnability, and incorporate responsible AI best practices.

The book also covers how to monitor and manage image models after deployment. You will also learn how to put your models into large-scale production using robust ML architecture. The authors are Google engineers with extensive experience in the field, so you can be confident you are learning from the best. – Will Cannon, CEO, and Founder of Uplead.

7. Computer vision by Richard Szeliski:

This book is all about algorithms and applications. This book is perfect for undergraduate students in computer science as it aims to provide a comprehensive course in computer vision. It is also known as the bible of computer vision. The focus of this book is on the algorithm, application, and techniques for image processing and recognition in CV.

It also helps one to get an understanding of the real-based applications and further discuss the implementation and practical challenges of techniques in computer vision. Co-Founder at Twiz LLC 

If you are interested in teaching senior-level courses in this subject, then this book is for you as it can help you to learn more techniques and enhance your knowledge about computer vision. 

Share more computer vision books with us 

If you have read any other interesting computer vision books, share them with us in the comments below, and let us help the learners begin with computer vision.