SAS Business Intelligence Tool is an augmented analytics and business intelligence platform which stands as a stalwart, providing organizations with a robust array of features to unlock the power of their data. It is recognized for its sophistication and analytical capabilities with the drag and drop capabilities which facilitates a visual process for finding insights. Its Artificial Intelligence and Machine Learning based augmented analytics helps business users to gain insights and predictions from data. Thus, SAS BI Tool is proved to be a go-to solution for businesses seeking to harness the full potential of their information.

In this article, I’ll explain the key features that make SAS Business Intelligence Tool a standout in the world of Business Intelligence Tools.

1. Advanced Analytics Capabilities
SAS BI Tool distinguishes itself with advanced analytics capabilities. Users can leverage statistical analysis, predictive modeling, and machine learning algorithms to extract meaningful insights from complex datasets, facilitating informed decision-making.

2. Comprehensive Reporting and Dashboards
The platform excels in offering comprehensive reporting and dashboard capabilities. Users can create detailed reports and interactive dashboards that provide a visual representation of key performance indicators (KPIs) and trends, aiding in strategic analysis.

3. Data Integration and Transformation
SAS BI Tool ensures seamless data integration and transformation. Users can connect to diverse data sources, cleanse and transform data, and integrate information from disparate platforms, enabling a holistic view of organizational data.

4. Self-Service BI
Empowering users with self-service business intelligence, SAS BI Tool allows individuals to create their own reports and visualizations. This democratization of data ensures that insights are accessible to a broader audience, fostering a culture of data-driven decision-making.

5. Visual Data Exploration
SAS Business Intelligence Tool emphasizes visual data exploration, providing users with interactive tools to analyze and interpret data intuitively. From interactive charts to heat maps, users can choose from a variety of visualizations to convey insights effectively.

6. Real-Time Analytics
For organizations requiring real-time insights, SAS BI Tool supports real-time analytics. Users can monitor and analyze data as it streams in, facilitating prompt responses to evolving business scenarios.

7. Role-Based Access Controls
Security is paramount, and SAS BI Tool addresses this through robust role-based access controls. Organizations can define and manage user roles, ensuring that sensitive information is accessible only to authorized personnel, enhancing overall data security.

8. Predictive Modeling and Forecasting
SAS BI Tool incorporates predictive modeling and forecasting features. Organizations can anticipate future trends and outcomes based on historical data, enabling proactive decision-making and strategic planning.

9. Mobile Accessibility
Recognizing the importance of mobility, SAS BI Tool is optimized for mobile devices. Users can access reports, dashboards, and analytics on smartphones and tablets, ensuring that critical insights are available anytime, anywhere.

10. Scalability for Enterprise Solutions
Designed for scalability, SAS Business Intelligence Tool caters to the analytical needs of both small businesses and large enterprises. Its architecture allows for the processing of vast datasets while maintaining high performance, ensuring flexibility as organizational needs grow.

11. Collaboration Features
SAS Business Intelligence Tool fosters collaboration among team members through features that enable users to share insights, reports, and dashboards. The real-time collaboration tools enhance teamwork, ensuring that decision-makers are aligned in their understanding of the data.

12. Integration with SAS Ecosystem
For organizations using other SAS solutions, SAS BI Tool seamlessly integrates with the broader SAS ecosystem. This integration provides a unified analytics environment, allowing users to leverage the combined power of SAS solutions.

In conclusion, the SAS Business Intelligence Tool emerges as a comprehensive and powerful solution for organizations seeking to derive valuable insights from their data. With its emphasis on advanced analytics, comprehensive reporting, and scalability, SAS BI Tool continues to be a strategic choice for businesses aiming to make informed decisions and drive success through data intelligence.

If you are data science and machine learning professionals and looking for the top machine learning tools then you are at the right place, I’ll help you to explore these machine learning tools in this article. Machine learning, a link between Data Science and Artificial Intelligence, enables computers to learn and make decisions from data. It has revolutionized industries across the globe. Behind every successful machine learning project lies a set of powerful tools that facilitate data manipulation, model creation, and insights extraction.

Earlier to this article, I’ve given examples of Data Science tools and how data science is different from Machine Learning. Also, you can refer the article on how Python language is helpful in data science and machine learning.

In this article, we embark on a journey through the realm of machine learning tools, exploring their functionalities and their role in shaping the future.

1. Scikit-Learn – The Versatile Workhorse

Scikit-Learn is an open source machine learning library designed on top of Python code that offers a range of algorithms for classification, regression, clustering, and more. Its user-friendly interface and consistent API design make it a favorite among practitioners. Whether you’re a seasoned data scientist or a newcomer, Scikit-Learn provides the tools to explore the world of machine learning.

2. TensorFlow – Empowering Deep Learning

TensorFlow, developed by Google, is a powerhouse for deep learning. Its flexible architecture allows the creation of complex neural networks for tasks like image recognition and natural language processing. TensorFlow’s popularity stems from its community, abundant resources, and support for production deployment.

3. PyTorch – A Deep Learning Pioneer

PyTorch, known for its dynamic computation graph, is favored by researchers for its ease of use and flexibility. Its intuitive interface enables users to build complex models with ease. PyTorch’s strong focus on research makes it a driving force in advancing the field of deep learning.

4. Keras – The Beginner’s Gateway to Deep Learning

Keras, often used in conjunction with TensorFlow, simplifies the process of creating deep learning models. Its high-level API abstracts complexities, making it an excellent choice for those new to neural networks. Keras’ user-friendly design accelerates experimentation and model prototyping.

5. XGBoost – Boosting Performance with Gradient Boosting

XGBoost is a gradient boosting library renowned for its prowess in predictive modeling. It excels in handling structured data, producing accurate results in areas like classification and regression. Its ability to handle missing values and interpret feature importance sets it apart.

6. Pandas – The Data Wrangling Hero

Pandas is a data manipulation and analysis library that simplifies working with structured data. Its DataFrame object allows effortless data cleaning, transformation, and exploration. Pandas’ efficiency in handling large datasets and data integration makes it indispensable.

7. NLTK and SpaCy – Navigating Natural Language Processing

Natural Language Processing (NLP) requires specialized tools, and NLTK and SpaCy deliver. NLTK offers a comprehensive suite for text analysis and processing, while SpaCy focuses on high-speed and production-ready NLP tasks. These libraries simplify the extraction of insights from textual data.

8. Matplotlib and Seaborn – Visualizing Insights

Data visualization is crucial for understanding and communicating results. Matplotlib and Seaborn provide comprehensive tools for creating a wide range of graphs and visualizations. These libraries empower users to transform complex data into clear, informative visuals.

Conclusion
Machine learning tools are the backbone of innovation, enabling data scientists and researchers to unravel insights from complex datasets. From the versatility of Scikit-Learn to the deep learning capabilities of TensorFlow and PyTorch, each tool plays a unique role in advancing the field. As the landscape of machine learning continues to evolve, these machine learning tools empower professionals to shape a future driven by data-driven insights. The article offers an overview of essential machine learning tools yet new tools and libraries are continually emerging, reflecting the dynamism of the field.

For the professionals who are pursuing machine learning/data science as a career, should always remain updated with the latest developments through the journals published online and the latest research on this field.

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When we talk about data science and machine learning, first question comes in mind which language is best suited for the data scientist and the answer is one among Python, R and C++. Each language has its pros and cons but Python is placed at the top in the field of Artificial Intelligence and data analysis/machine learning.

In the past, I’ve shared How data science is different from machine learning (article) and Why Python is best language for improving quality analysis of your software? (article) and today I will explain why and how Python is helpful in Data Science and Machine Learning for the beginners.

Python is a versatile open source, object oriented programming language which has emerged as one of the most powerful languages to support analyzing data science, machine learning and artificial intelligence. It comes with user-friendly syntaxes, huge libraries, and active community. Python has revolutionized the way professionals extract insights from data and build intelligent systems.

Python friendliness can be covered in below points-

1. Python has user friendly straightforward syntax

Python’s syntax are very simple straightforward English languages words which is a boon for beginners who wants to make career in data science/machine learning. It uses easy-to-read syntaxes similar to our day to day language, making it less intimidating for newcomers. The reduced learning curve allows aspiring data scientists to focus on concepts rather than syntax complexities.

2. Large and progressive Ecosystem of Python Libraries

Python boasts a rich collection of libraries designed for data science and machine learning capabilities. Libraries like NumPy and Pandas provide robust tools for data manipulation and analysis. Scikit-Learn offers a wide range of machine learning algorithms, simplifying model development. TensorFlow and PyTorch cater to deep learning enthusiasts, enabling the creation of complex neural networks.

3. Rapid prototyping for accelerating experimentation

Python’s interactive nature encourages rapid prototyping. Researchers and data scientists can experiment with algorithms and techniques in real-time. This flexibility helps quick iteration and exploration, enhancing the efficiency of the development process.

4. Visualization capabilities which can transform data into insights

Data visualization is an extremely important aspect of data science. Python’s libraries like Matplotlib and Seaborn allow professionals to create compelling visualizations that communicate insights effectively. Visualization tools aid in understanding patterns, trends, and anomalies hidden within data.

5. Large community and documentation makes it more supportive

Python’s vast community is a valuable resource for aspiring data scientists and machine learning enthusiasts. Online forums, tutorials, and open-source projects provide guidance and solutions to challenges. Additionally, Python’s comprehensive documentation streamlines the learning process.

6. Capability of data cleaning and preprocessing

A significant portion of data science involves cleaning and preprocessing data to ensure accuracy. Python’s libraries offer tools to handle missing values, outliers, and inconsistencies, ensuring a solid foundation for analysis and modeling.

7. Machine Learning – Enabling Intelligent Systems

Python’s machine learning libraries helps professionals to build predictive models and intelligent systems. From classification and regression to clustering and recommendation, Python provides a versatile toolkit for solving a variety of real-world problems.

8. Deep Learning for unleashing Neural networks

The rise of deep learning has transformed various industries. Python’s libraries like TensorFlow and PyTorch enable the creation of complex neural networks that excel in tasks like image recognition, natural language processing etc.

9. Scalability – Adapting to Big Data

Python’s scalability ensures that it can handle large datasets and complex computations. Libraries like Dask and Apache Spark facilitate distributed computing, allowing data scientists to work efficiently with big data.

10. Jupyter Notebook

Jupyter notebook (a web interface to Python) allows you to code and collaborate output with other data scientists using your web browser which is really fantastic. Jupyter notebook is born or developed from IPython, an interactive command line terminal for Python.

Conclusion

Python’s versatility and robust ecosystem has helped in the field of data science and machine learning. From its user-friendly syntax to its comprehensive libraries and supportive community, Python stands at the top place to extract insights from data, build intelligent systems, and contribute to groundbreaking research. As the field continues to evolve, Python’s role as a driving force in data science and machine learning remains stronger than ever.

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Not knowing machine learning – Everybody is a rookie when they start something very new, and they don’t have any experience before. The chances of making mistakes are in every step, and how much you take care, there are chances you will fall into the trap even for simple things while you solve the complicated problem.

In every path, you learn something new, very fresh. That tightens your muscle in every next step you take forward, concrete your understanding of that topic, and makes you a better version than your last.

This blog is about how not knowing machine learning makes you a rookie if you are already in the data science field.

So, What is Machine Learning Then?

Machine learning is a sub-part of artificial intelligence and advanced data analytics that automates analytical model building, identifying patterns, and decision-making without human interruption.

Over the years, machine learning has changed drastically; the primary purpose was for recognizing patterns without much code involvement. The best part about machine learning is the algorithms are self-learning and iterative that they learn from the data and draw new conclusions every time.

Therefore machine learning is not a new science anymore but got the best momentum recently. And if you’re in the data science field, the earlier you learn it, the more beneficial it is for you and your career.

How is Machine Learning Crucial For Data Science?

In today’s date, machine learning and artificial intelligence go hand in hand. As per the definition, machines learn from the existing data, and without data, the machines hardly understand anything. And nowadays, machine learning algorithms are the basics to know for data science personnel to make algorithms consume data and learn from the existing patterns.

And a fact may surprise you that the availability of data is directly proportional to the difficulty of finding new patterns that work accurately. Just imagine how much information gets generated in seconds across the world?

According to social media today, can you imagine it’s nearly 1.7 MB/ sec of data across the globe, according to social media today? You can’t analyze them manually, the processes need to be fast and accurate, and machine learning algorithms can help you do it defectively.

4 Ways Not Knowing Machine Learning Makes You a Rookie!!!

If you are into data science, you are already implementing machine learning. What makes a difference is finding those areas where you’re using them and discovering how to make most machine learning.

Here are five tips to know machine learning better and never to feel like an amateur. Let’s dive in together.

1. Not Knowing The Mistakes You’re Already Doing

Being in the early stage of a career, everyone does it intentionally and unintentionally. The more you focus there, the better you evolve there. Mistakes are common, and everyone’s part of life. Learning from those is what makes you stand out from the crowd of data scientists and machine learning engineers.

These are some of the errors you regret the most when you come to know, but you can survive in the market if you continue doing them. Because these errors are explicit, when the program fails, you find a few, and when you correct them, you find a few more there too, and the cycle repeats, and a lot of your time gets wasted there.

2. Errors Resulting From Inaccurate Experiments

Do you still struggle with decision making, don’t panic but fall into this and make blunders making the same mistakes repeatedly. It mostly happens when you approach a new procedure, feel overwhelmed about it, and don’t go deep to grasp the entire process.

Errors that you often make regularly, you keep them unnoticed. It’s better to spot them early and improve your accuracy.

3. Errors That Makes You Believe Your Errors are Better

It happens when you overestimate the results; these errors are hard to spot but easy to fall prey to. These are the biased results that algorithms get you, surprisingly, which are not true at all, but you never fail to praise yourself for that distinction rather than finding the correct approach.

That’s a hit-and-trial approach that doesn’t give correct results all the time but puts you in the zone of making repeated mistakes and never coming out of it. One typical example of this is overfitting the test data by simply screwing up the metrics.

4. Overfitting Struggle with Simple Models on Small Datasets

Are you struggling overfitting and underfitting, and maintaining a good balance? Then, here is the thing: models with low bias and high variance are overfitting, and the models with high bias and low variance are underfitting.

Models trained on a tiny dataset are more likely to see the patterns that hardly exist, which results in overfitting, therefore while working on small datasets, avoid overfitting.

Use these timeless yet straightforward techniques:

• • • Choose simple models; complex models leads you to overfit
    • Remove outliers from the datasets as they have a massive impact on the models
    • Select relevant features as it is arduous to avoid overfitting from tiny datasets taking the help from domain experts
    • Combine results from more than one model to predict the accuracy of the test

Final Words

There is always a first time for everything. First time to learn and first time to implement, and the first time to make mistakes too, but stop making mistakes over and over again. Otherwise, no matter your experience, it often makes you feel like a rookie.

Hold your eyes on every little detail on data science and machine learning, and you end up wasting no more valuable time again. This blog throws the limelight to find most of the mistakes and a few steps to overcome them.

From what is machine learning to how crucial is machine learning for data science and four ways not knowing makes you a rookie in the industry. I hope you get the best insights to keep your eyes on the mistakes and stop yourself from making those blunders again.

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NLP is one of the sought-after techniques to develop better and more effective communication skills. There are various techniques – such as anchoring and mirroring for establishing better communication with the prospects in your absence.

Natural Language is the medium of how humans communicate with each other through text, speech, images, signs, email, and SMS. But when humans try to communicate with machines, they do it in the same way that humans do, and algorithms process it in the backend. Find the translations’ accuracy to reconstruct the sentences in a better way. Find the accurate results and again convert them into human-understandable language.

What Is NLP and How Is It Making Communication Better?

Communication is the key to everything. It should be transparent from both the side to make it better and efficient. It builds trust, transfers knowledge and emotions, and makes things sell in the business. Thus, communication plays a crucial role everywhere and in our day to day life.

Businesses are already spending massive capital on making communication better and effective. Whereas neuro-linguistic programming, in short NLP, makes communication better. By adding lots of filters to give accurate information in the least time. NLP is the advanced level AI and ML programming and training from the existing algorithms on how to deal with human languages and convey them accurately.

Whether it may be voice search or text search, NLP takes the input, processes it through the algorithms, and answers the search queries that the search engine predicts the best and most relevant answers for the users. Most often, computers and smart devices fail to understand human languages. And there is NLP to solve this issue seamlessly with accurate results.

But the process is never simple the way it looks from outside. It has to go through a series of operations to get accurate results or effective communication. Let’s learn about them in detail.

1. Enhancing the Translation Accuracy

For Neural Machine Translation (NMT) models, supervised learning requires a large volume of sentences. However, the data is not available in general, but it requires a lot of research and uses monolingual data where there is no translation available. Most experts use back-translation, i.e., a semi-supervised learning technique for overcoming the above issue.

Translation accuracy involves three raw models: forward, backward, and fluency. The forward models look for how well the translations capture the original meaning. Whereas the backward scores look into how well the original sentences could get reconstructed. And fluency is all about fluency in a candidate’s translation and training in a self-supervised way by looking into the monolingual data. NLP takes the three scores, finds a balance, and produces improved translations.

2. Strengthening the Self-Supervised Models With Pre-training Methods

The pre-training is more about the self-training process, and it strengthens the whole algorithm process and model performance by incorporating the prediction and unlabeled data. It helps to find out the additional information that can get used in the training process.

Adopting NLP has many advantages. It not only makes the device smart, but the process itself gets smarter when your results are accurate to what users are looking for to know or have. And the users get amazed and feel wow to use it more often on their smart devices. And another advanced training is self-supervised learning. The prime purpose of this algorithm is not only to learn high-level features. But to become better and capable of handling a wide variety of tasks and datasets.

3. NLP Mirroring Technique is One of the Ultimate Solution

NLP mirroring technique is an alternative to NLP matching, and it tries to copy the word or sentences to build relationships. Imagine you standing facing the mirror, you raise one of your hands, and in the reflection, you can see the same happening there but with the opposite hand at the other end. This way, one is trying to build a relationship with another, and it is what we call mirroring.

NLP follows the same concept when you say some words, and its algorithms try to supervise them and reconstruct them depending upon the voice tone and sentence length, and structure considering the pitch, tempo, pace tone, and volume. To show the actual results to the users who are doing the type of search or voice search queries.

4. Different People Will React to The Same Differently

Every person has their way of saying, sometimes the influence is because of the mother’s tone that NLP does not understand and self-supervised it. To the same questions, they have multiple answers. They will not feel satisfied and react to the same differently. Using NLP, you can book a table for the party, or you can ask for photos to google about any location if you are planning for a vacation. Or you can ask your smart assistant to play your favorite songs or movies, and it will bring you to it.

Suppose if you want to auto tune or omit additional noise, then advanced lexical processing focuses on removing the miscellaneous noise in textual data so that you can build algorithms that will save you from embarrassment with auto-correction facilities.

There are two processes: stemming and lemmatization. And both are part of an advanced technique known as canonicalization. So, others can hear your right voice and pitch like the approaches the professional speak takes to maintain their flow.

Final Thoughts

There you have it all you need to learn about advanced Natural Processing Techniques for making communication better and more effective. When you finish this blog thoroughly, you will get to know what NLP is? How technologies make our communication better and easier when it is about communicating with smart devices.

When you go further deeper, you will find how to enhance the translation accuracy. Boosting up algorithms models and increasing their efficiencies. And how mirroring techniques reduce complexity and simplify the working process with accurate results. When you approach the end part, you will know why people react to the same things differently and how stemming & lemmatization can save you from further embracement.

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The future is now Machine Learning and Artificial Intelligence. Artificial Intelligence impacts the way we transact and interact more than ever before. From finance and shopping to social media and medicine, machine learning solutions power more aspects of daily life than you could imagine.

In the age of Artificial Intelligence (AI), machine learning (ML) is a popular topic that covers Natural Language Processing and computer vision. Through machine learning, inventors have achieved incredible breakthroughs in diverse fields.

From the facial recognition feature on your phone to hyper-realistic casino gaming on platforms like Mr Bett, what previously seemed science fiction is today a reality, thanks to the advancement in the machine learning technology.

With the help of machine learning, humans can live healthier, happier, and more productive lives. Experts predict that soon, computers will replace both manual and mental labor. In some industries, this is already happening.

Here are some ways algorithms have impacted everyday life-

What is Machine Learning or ML?

ML is a subset of AI in which a computer is programmed to have the ability to self-teach while continually improving its performance of a task. Basically, it involves data analysis and interpretation.

The information extracted is used to make predictions, test whether a prediction is correct, and help the machine learn how to make better decisions. If you wanted to design AI software, you should consider taking a machine learning course. Start with the basics and build your knowledge to become an expert if this is a career you consider pursuing.

Computer Vision and its Application Today

Machine learning has experienced exponential growth over the recent past, especially in the area of computer vision. As of 2016, the human error rate was only 3% in computer vision, which means computers can recognize and analyze pictorial data better than humans. This is incredible, because decades ago before we discovered how machine learning could help change lives, computers were just piles of machinery that could even fill a room. Now, people are walking with powerful ML software in their pockets.

One real-life application of machine learning is in Diabetic Retinopathy, a complication that affects the eye. During the extensive exam required to pinpoint the problem, a machine learning model built around computer vision completes the diagnosis.

The data collected from the diagnosis is then processed and interpreted. Experts can also use the model as a second opinion. The idea is that artificial intelligence models should replicate specialists’ work. So, the technology can be deployed in third-world countries in remote locations with a shortage of specialists.

1. Machine Learning in Ride Sharing Apps

To further demonstrate how ML works, it’s important to discuss more real-life examples. One such example is ride-sharing apps such as Uber and Lyft. You probably already use these apps, but do you know how they calculate the price of your ride in real-time? And how do they match you with cabs to minimize detours? The short answer to all these questions is machine learning.

In an interview, Uber Engineering Lead, Jeff Schneider, explained the company uses AI to predict demand. Also, the head of Machine Learning at Uber, Danny Lange, confirmed the company uses artificial intelligence to estimate meal delivery times, compute pickup points, and detect fraud.

2. Machine Learning for Finance – Banking

Imagine how many people use banking services every day. Or the number of people who have bank accounts. And how many credit cards are in circulation? The simple answer is many. So, how many hours would workers need to sift through all transactions completed in a day? It would be impossible to detect anomalies or even review all transactions if the bank did everything manually.

Using machine learning, banks track credit card transactions and identify fraudulent behavior in real-time. Banking servers work with ML systems with anomaly detection models that monitor transactions and verify the authenticity on the fly. Before a credit card purchase is processed, the system can verify if it’s the owner using the card.

3. Artificial Intelligence in Education

Teachers must perform many tasks: educate, analyze student behavior, mentor, counsel, and a lot more. No computer can do all those tasks, but some of these tasks could be automated through artificial intelligence and ML. There are computer programs that review individual study plans for each student.

The software can analyze data on a student’s attendance, learning disabilities, and academic history using algorithms. With this data, teachers can identify learning gaps for each student.

4. AI in the Gaming Industry – how machine learning is used in video games?

Gaming is one of the areas in which machine learning has shown great promise. In 1997, Deep Blue, a chess computer game by IBM, defeated Gary Kasparov. Just recently, in 2016, AlphaGo by Google beat the Go world champion, Lee Sedol. Considering Go is a more challenging game for computers to learn than chess, it was an incredible milestone in the development of machine learning. The algorithm analyzes the moves of players while also learning how to play and make better moves.

For improved game play that is almost realistic, casino platforms use artificial intelligence systems. This includes generating a list of recommendations based on the type of games a player enjoys while in the casino. Also, you can use AI to predict lottery numbers.

5. AI for Dangerous Jobs

Dangerous jobs like bomb disposal could also be handed over to technologies that run on artificial intelligence. Already, security teams are experimenting with drones, which require human control to dispose of dangerous material. In the future, through machine learning, it will be possible for robots to take charge of every process.

Tasks like welding are also outsourced to robots, so humans don’t have to worry about the intense heat, toxic fumes, and noise produced during welding. Advancements in machine learning have improved accuracy and flexibility, so the robots can work unsupervised.

6. Environmental Impact Protection

Computers can store millions of records. Using stored data and real-time trends, machine learning systems can identify weather trends and recommend solutions to previously untenable problems.

A good example is the Green Horizon Project by IBM, which uses ML to analyze environmental data from sensors to provide accurate weather pollution forecasts. The information helps city planners to simulate situations and design models to mitigate environmental impact. Innovations like self-adjusting thermostats are also entering the market.

7. Smart Homes and Home Security Systems

Machine learning has also been used to create improved home security systems. Technologies such as facial recognition build a catalog of frequent visitors to your home, so any uninvited guests are flagged, and a notification is sent to your mobile device instantly. People can also benefit from AI-powered smart homes that come with features like tracking, which show when you last walked your pet.

8. Personalized Digital Recommendations

Have you ever logged into your Netflix or Spotify account and found recommendations for the kind of content you love to consume? This was machine learning at work. ML algorithms collect data about the type of content you watch or listen to often. From this data, the algorithm creates a recommendation list that matches your preferences. So, you don’t need to search on different pages to find the content you love.

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Talk about the next big thing and the image it creates is like a quantum step up or an inundating tidal force.

The reality is that in software you find incremental progress and then, at some point, the accumulation of good features, ironing out of kinks and affordability could help to make it mainstream.

One can say something of this kind is happening in the field of Machine Learning. That Microsoft, Google and Amazon are offering ML as a service is equivalent to opening floodgates to even more widespread use and implementation is a big enough thing.

Back in the infancy days of big data it was considered as a colossus difficult to handle.

Years later it is easily handled and you have big data services everywhere.

It would not be surprising to see the same thing happening in ML. It is interesting to speculate.

1. Software becomes personalized

Software is pretty smart these days, a far cry from what you got in buggy packages just five to ten years ago. Still, in a way, software packages can be dumb. They work the same way for all users.

Now image ML drenching the software. Now you have ac software package that learns what its “Master” does and how he uses it, the frequent features and steps that he goes through and the results he derives.

The software keeps learning and maybe takes over part of the processes. The software user could simply verbally instruct the software to do the “usual” or to find a solution to a problem. There is no reason why this should not be possible.

Already Facebook, Instagram and Amazon are making good use of machine learning to process user data and offer personalized recommendations.

The trend of intelligent owner aligned devices is already showing in smartphones that listen, talk back and carry out orders of owners.

This is happening but the big thing in ML will be when the device can link contextual elements and be deeply inclusive.

It will be even bigger when the machine develops sensitivity and discretion but that day is far in the future.

2. Machine Learning Solutions for Healthcare

Personalized software would seem trivial compared to the tremendous benefits to global health, patient care, disease control and emergency response that ML brings to healthcare.

Admittedly ML feeds off gargantuan chunks of healthcare data and refining its precision and accuracy of diagnosis. A burgeoning population explosion and lack of doctors gives ML a big boost in healthcare.

ML can now do a lot of things and it will be capable of doing still more in future. It can, for instance distinguish anomalies with more accuracy and even carry out unattended diagnosis from lab reports and MRI/X-rays.

The big thing with ML is how it helps to arrive at a more accurate diagnosis, which is so crucial to precise treatment and prevention.

ML runs the entire gamut from accelerating drug discovery to understanding genetic disorders.

The big thing would be for ML to lead to reductions in cost of healthcare. Google’s ML algorithm can detect tumors from mammograms and Stanford is using ML to detect cancer in early stages.

3. Machine Learning Solutions for Financials

If health is important for individuals, financials are important for the world at large.

ML developers are working together with financial institutions to come up with ML powered solutions that will greatly boost functioning in this core sector.

ML will play a bigger role in banking, finance and insurance in credit rating, automatic scrutiny of loan applications, risk appraisals and prevention of fraud.

If banks, insurance companies and lenders have not already adopted ML it is time to get ML developers to introduce the tech into their operations.

Apart from customer side operations ML also helps for the next big leap as regards employees and internal operations.

4. Machine Learning and HR

ML has already made inroads into HR operations. It will go further in helping HR experts to scrutinize applications and create a more in-depth profile of candidates by studying not only their resume but also their faces.

This type of analysis helps HR identify individuals who will best fit into the company culture and, from the existing employee set, identify those likely to progress or likely to resign.

ML algorithms can be put to good use to distribute workload and allocate work.

It can keep track of external developments like booming economy that will likely lead to employees finding greener pastures elsewhere and the difficulty in finding new recruits.

5. Machine Learning and Big Data in Advertising and Marketing

ML is already making inroads into advertising and marketing as can be seen from the success of programmatic advertising the world over.

Marketing leverages it as can be seen from sites like Amazon. The big thing in ML is that marketers will benefit from real time analysis of streaming big data and gain predictive capabilities that will help them to design a campaign for success and even predict its outcome.

The effort is worth it since 91% customers prefer personalized recommendations and 83% will share data if they are promised personalized experiences.

That is not so surprising or a big thing but one thing that will prove truly big is the use of recurrent neural networks, AI and ML to actually create contents or design ads or script a story-line for a movie or a Netflix series.

Each episode will be a guaranteed hit.

6. Machine Learning Solutions in Image Search/Recognition

Work is already in progress and Facebook and Instagram already have a degree of capability of face recognition.

Search engines can find and match images. However, given the so many different variables ML in this segment is still work in progress kind of thing.

7. Machine Learning Solutions in Travel

Travel will be easier than ever. You simply ask the smart assistant to recommend places according to parameters such as scenic vista, food choice, action or budget.

You get detailed recommendations with no need to worry about schedules, itineraries and bookings.

8. Machine Learning in Agriculture

This is truly one area where ML can be life changing. Agriculture depends on quite a few variables such as soil, market demand for a particular product, climate and availability of inputs.

ML has vast scope with capability to predict yield of crops and output from livestock. It can be used to enhance species breeding and selective genetic engineering.

ML yelps with analysis of leaves, with water management and soil management to give outputs about yields and crop quality.

Smart agriculture takes care of disease detection, weed detection pest control and other threats that farmers must face.

You take guesswork out of farming and you have assurance of outcomes. This can change fortunes of farmers, help them from going bankrupt due to vagaries of weather and propel a country on the path of prosperity.

Like healthcare, this is one segment that has immense scope and holds big promise for and from ML.

One thing to note from the foregoing is that Machine Learning is application specific and requires specific work in one area to derive results.

The ML solution may make use of one or several types of neural networks like Sparse AE, Deep Belief Network, Restricted BM or Markov chain to mention a few from over a dozen different types.

ML solutions may make use of various algorithms such as general minimization algorithm or steepest descent algorithms. ML could be supervised or unsupervised or reinforced.

The result is specific answers to specific questions, something that is quite rudimentary compared to what the human brain is capable of.

The next big thing of value in Machine Learning would be a development that has artificial general intelligence and of thinking like a human mind.

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Data science is one of the fastest growing fields of expertise at the moment. We know that data science involves machine learning, but what’s the difference between these two fields of expertise?

In short, machine learning involves complex algorithms that “learn” from data in order to predict future trends and system behaviors. On the other hand, data science is the process of tackling and making sense of large collections of data. This includes data cleansing, preparation and analysis, which is, in part, machine learning.

In this article, we will unpack these two concepts, aiming to bring an understanding of what each term means and how they relate to each other.

Data Science vs. Machine Learning

A. Data Science

Data science as a field is difficult to define since it draws from so many different fields of knowledge.

Most data scientists know machine learning and understand multiple analytical functions. This person usually has experience in SQL database coding and a strong knowledge of various coding languages, such as Python, SAS, R and Scala. Added to this, they are usually able to use unstructured data in order to extract useful information. Other fields that are sometimes included in data science are bioinformatics, information technology, simulation and quality control, computational finance, epidemiology, industrial engineering and number theory.

As you can see, data science covers a very wide spectrum of knowledge and skills. Depending on which side of this spectrum you are, you may or may not use programming and complicated mathematics, but you will definitely use large sets of data, usually in an unstructured format. Due to the broad nature of this field, it is hard to define and to find one person capable of doing everything involved needed for a successful data science project. Usually, data scientists would work as a team where each would focus on a specific subset of the field.

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Here, you would see titles such as “Machine Learning Engineer”, “Analyst” or “A/B Test Expert” indicating which area of work they focus on. Tools used by data scientists include, but are not limited to, data cleansing, preparation, predictive analytics, machine learning and sentiment analysis. These experts are tasked with making sense of large collections of data, extracting useful information from it and translating that into actionable goals. A data science team would understand how data relates to business and uses this to enable executives to make informed decisions based on solid science in order to propel their businesses forward.

Data science involves processing vast amounts of unstructured data in automated ways in order to extract logical, useful information from it and make prediction regarding future trends and system behaviors. Unstructured data comes from video, audio, social media, manual surveys, clinical trials and many other sources. This can be lumped together as human consumable data, which can be read and analyzed in tabular form, by humans. The amount of data is so vast, though, that this is entirely impractical, hence the need for automating and speeding up the process. Here, the data scientists will have to borrow techniques from related fields, as is done in most practical applications of science.

As time progresses, these predictions must be updated and the system re-calibrated using new data. Data scientists must also understand and decide which analytics tools to use for their specific purposes and applications, since this would affect the type of information that they would be able to extract from a specific set of data. Real world problems are tackled in data science. This field is incredibly complex due to the complex nature of the world we live in.

In data science, unsupervised clustering can be used. Here, an algorithm is used to find clusters or cluster structures without having been given a training set of data. These clusters must be labelled by a data scientist; thus, some human interaction is necessary.

Here, the major complexity of the system is due to the nature of the data (unstructured and vast). It is necessary to synchronize and schedule tasks in a logical manner in order to render the data useful and extract as much information from it as possible.

Simply put, data science is a vast field encompassing many disciplines, of which machine learning is one.

B. Machine Learning

Machine learning is a subset of data science. Arthur Samuel defines it as “a field of study that gives computers the ability to learn without being explicitly programmed”.

An expert in machine learning requires in-depth knowledge of computer fundamentals and must be excellent in data modelling and evaluation skills. Knowledge of probability and statistics is needed and in-depth programming skills and knowledge is essential.

In machine learning, large collections of data are mined in order to find patterns, learn from it and predict future behaviors of systems. It basically “teaches” a system how to behave under certain circumstances. A prime example of this is Facebook’s algorithm. Here, the algorithm observes various users on the social media platform in order to determine patterns of user behavior and interactions. This information is used in order to tailor the user’s news feed to articles that they are likely to enjoy. Amazon uses a similar principle to suggest products in their “you might also like” category. YouTube, Netflix and a myriad of other media platforms and online retailers work on the same principle to suggest your next view, article or purchasing suggestions.

In finance, machine learning is used to predict whether a prospective client applying for a loan is a good or bad prospect based in historical data. This takes the guess work and “gut feel” factors out of financial decision making.

Another example of sophisticated machine learning is the autocomplete or predictive text functionality on your smartphone or search engine. The software is programmed to collect data as you type in order to better predict what you are likely to type next in order to fill in the blanks faster and more accurately as time progresses. This has become so entrenched in our daily lives that few people stop to think about it.

Machine learning is a subset of artificial intelligence (AI). Here, a problem is defined in finite terms and the algorithm is programmed to know the “right” decision. Now, it trawls through the data at hand to learn which parameters are needed in order to get to that decision.

Basically, the computer is given the ability to learn new things and complete complex tasks without being explicitly programmed. When developing a machine learning algorithm, a training set of data would be used to “teach” the algorithm to perform a specific function. This would be fine tuned and can later be re-calibrated using a new set of data. On the long run, this would lead to a highly sophisticated algorithm that can accurately predict future trends and system behavior and can also make complex decisions in an unsupervised manner. This eliminates the need for regular human interference. Here, regression and naive Bayes or supervised clustering could be used.

Machine learning would not include unsupervised clustering, as is the case with the broader data science discipline. Data used in machine learning must be structured in a way that the specific algorithm would understand. Here, feature scaling, word embedding and adding polynomial features are some of the tools that can be used to render data useful and understandable for each specific application. In machine learning, the main complexity is in the algorithm itself. In some cases, an ensemble algorithm would be used, which is a combination of various machine learning algorithms. Here, the contribution from each algorithm would be weighted in order to obtain the desired results.

In short, machine learning is where practical statistics and highly sophisticated programming skills meet.

Overlap Between Machine Learning and Data Science

In machine learning, concepts that are used in data science career, such as regression and supervised clustering, are also used. In contrast to this, data science uses data that may or may not be originated in an actual machine or mechanical process. Both these fields use large collections of data in order to learn from it and arrive at logical actions in order to add financial benefit to an organization.

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Data science is a much broader term that machine learning. Machine learning focuses mainly on statistics and algorithms, while data science encompasses anything related to collecting, analyzing and processing data. Data science is multi-disciplinary. In a data science team context, each person would have a specific role to fulfill. Here, a machine learning expert would work to automate as many tasks as possible, breaking down code in order to simplify and reuse as many components as possible. Statisticians would ensure that the information teased out of data makes sense and is usable. Economic experts would optimize the system responses to ensure economic viability. Machine learning is crucial to data science and should be used in conjunction with other disciplines in order to complete the data science picture.

If you have a high level of knowledge on mathematics and statistics combined with hacking skills, you are able to program in the field of machine learning. Pair these skills with a large portion of substantive expertise, and you have a highly skilled data scientist.

In short, machine learning is one of many tools used by data scientists in order to extract useful information from large collections of data.

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