Probability is about how Likely something is to occur, or how likely something is true.
Probability = Ways / Outcomes
Category: Data Science
Machine can mimic humans in learning
Speech recognition, decision-making, and visual perception are some features that an ‘AI’ possess. The main goal of artificial intelligence has always been for these machines to be able to learn, reason, and perceive as human beings with little or no human intervention. But humans are always going to be needed to observe and supply equipment necessary to perform the processes.
Machines are driven by software packages that stores, sorts, processes complex datasets based on entities relationships feed by humans to perform event-driven actions that reduces human interventions.
AI enables an unprecedented ability to analyze enormous data sets and computationally discover complex relationships and patterns. AI, augmenting human intelligence, is primed to transform the scientific research process, unleashing a new golden age of scientific discovery in the coming years.
With artificial intelligence automating all kinds of work, we can think of a more comfortable future for ourselves that will create new jobs. According to a report on the Future of Jobs by World Economic Forum, AI will create 80 million new artificial intelligence jobs world wide by 2024.
The difference between Machine Learning (ML) and Artificial Intelligence (AI)
Cloud ML:
The Cloud ML Engine is a hosted platform to run machine learning training jobs and predictions at scale. The service can also be used to deploy a model that is trained in external environments. Cloud ML Engine automates all resource provisioning and monitoring for running the jobs.
The cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science. AWS, Microsoft Azure, and Google Cloud Platform offer many machine learning options that don’t require deep knowledge of AI, machine learning theory, or a team of data scientists.
- The cloud’s pay-per-use model is good for bursty AI or machine learning workloads.
- The cloud makes it easy for enterprises to experiment with machine learning capabilities and scale up as projects go into production and demand increases.
- The cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science.
- AWS, Microsoft Azure, and Google Cloud Platform offer many machine learning options that don’t require deep knowledge of AI, machine learning theory, or a team of data scientists.
Cloud AI:
The AI cloud, a concept only now starting to be implemented by enterprises, combines artificial intelligence (AI) with cloud computing. An AI cloud consists of a shared infrastructure for AI use cases, supporting numerous projects and AI workloads simultaneously, on cloud infrastructure at any given point in time.
Artificial intelligence (AI) assists in the automation of routine activities within IT infrastructure, which increases productivity. The combination of AI and cloud computing results in an extensive network capable of holding massive volumes of data while continuously learning and improving.
- Data Mining.
- Agile Development.
- Reshaping of IT Infrastructure.
- Seamless Data Access.
- Analytics and Prediction.
- Cloud Security Automation.
- Cost-Effective.
| Cloud ML | Cloud AI |
| The Cloud ML Engine is a hosted platform to run machine learning training jobs and predictions at scale. | An AI cloud consists of a shared infrastructure for AI use cases, supporting numerous projects and AI workloads simultaneously, on cloud infrastructure at any given point in time. |
| The service can also be used to deploy a model that is trained in external environments. Cloud ML Engine automates all resource provisioning and monitoring for running the jobs. | Enterprises use the power of AI-driven cloud computing to be more efficient, strategic, and insight-driven. AI can automate complex and repetitive tasks to boost productivity, as well as perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. |
| The pay-per-use model further makes it easy to access more sophisticated capabilities without the need to bring in new advanced hardware. | Cloud AI Platform is a service that enables user to easily build machine learning models, that work on any type of data, of any size. |
| This storage service provides petabytes of capacity with a maximum unit size of 10 MB per cell and 100 MB per row. 1024 Petabytes of data. | 1024 Petabytes of data. The larger the RAM the higher the amount of data it can handle hence faster processing. 16GB RAM and above is recommended for most deep learning tasks. |
| High Flexibility and Cost Effective. | Seamless Data Access. High Flexibility and Cost Effective. |
| Cloud ML Engine is used to train machine learning models in TensorFlow and other Python ML libraries (such as scikit-learn) without having to manage any infrastructure. | In Artificial Intelligence, the Decision Tree (DT) model is used to arrive at a conclusion based on the data from past decisions. |
| Cloud DLP – Data Loss Prevention provides tools to classify, mask, tokenize, and transform sensitive elements to help you better manage the data that you collect, store, or use for business or analytics. | Cloud DLP – Data Loss Prevention provides tools to classify, mask, tokenize, and transform sensitive elements to help you better manage the data that you collect, store, or use for business or analytics. |
| The cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science. | The cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science. |
| Google, Amazon, Microsoft, and IBM | Google, Amazon, Microsoft, and IBM |
| ML’s aim is to improve accuracy without caring for success. | The goal of AI is to increase the chances of success. |
| ML is the way for the computer program to learn from experience. | AI is a computer program doing smart work. |
| The ML’s goal is to keep learning from data to maximize the performance. | The future goal of AI is to stimulate intelligence for solving highly complex programs. |
| ML allows the computer to learn new things from the available information. | AI involves decision-making. |
| ML looks for the only solution. | AI looks for optimal solutions. |
ML and AI:
Even though many differences exist between ML and AI, they are closely connected. AI and ML are often viewed as the body and the brain. The body collects information, the brain processes it. The same is with AI, which accumulates information while ML processes it.
Conclusion:
AI involves a computer executing a task a human could do. Machine learning involves the computer learning from its experience and making decisions based on the information. While the two approaches are different, they are often used together to achieve many goals in different industries.
Correlation Measures The Relationship Between Two Variables
What is Correlation?
Correlation is a statistical measure that expresses the extent to which two variables are linearly related (meaning they change together at a constant rate). It’s a common tool for describing simple relationships without making a statement about cause and effect.
Why is Correlation important?
Once correlation is known it can be used to make predictions. When we know a score on one measure we can make a more accurate prediction of another measure that is highly related to it. The stronger the relationship between/among variables the more accurate the prediction.
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Useful Tools for Python Developers
Whether you need Python tools for data science, machine learning, web development, or anything in between, this list has you covered. Here’s the link to the article.
Differences between Power BI, Tableau and Python Dash
Data visualization has gained massive popularity in recent years owing to the demand for data. In a business setup, these business intelligence tools can help in analyzing all the data and monitor performance to enhance growth for the firm, and productivity for the employees. With the world switching to digital means all together in the year that went by, data is now considered fuel for every small, medium, or big firm.
In such a scenario, what sounds better- a spreadsheet that mentions the date, time, sales, and profit OR a colorful, descriptive bar chart interactively explaining all the details? Our vote goes to the latter.
What is a data visualization tool?
An essential part of any business strategy, data visualization is the process of collecting data and transforming it into a meaningful visualization to support decision-making. These visualizations could be in the form of bar charts, maps, or anything that is visually appealing and interactive. They convey the information to the viewer by simply looking at them, whereas normally one needs to read spreadsheets or text reports to understand the data.
Talking of the best data visualization tools used by analysts in various industries according to their specifications and applications, they comprise Power BI, Tableau and Python Dash. All these software programs help businesses make decisions questions faster.
Power BI is a Data Visualization and Business Intelligence tool provided by Microsoft. It can collect data from different data sources like Excel spreadsheets, on-premise database, cloud database and convert them into meaningful reports and dashboards. Its features such as creating quick insights, Q&A, Embedded Report, and Self Service BI made it top among all BI tools. It is also robust and always ready for extensive modeling and real-time analytics, as well as custom visual development.
Tableau offers business analysts to take business decisions by its feature, data visualization available to all business users of any background. It can establish a connection with any data source (Excel, local/on-premise database, cloud database).
Tableau is the fastest growing Data Visualization Tool among all visualization tools. Its visualizations are created as worksheets and dashboards. The beauty of tableau is that it does not require any technical or programming knowledge to create or develop reports and dashboards.
Python Dash
Dash is a python framework created by plotly for creating interactive web applications. With Dash, you don’t have to learn HTML, CSS and Javascript in order to create interactive dashboards, you only need python. Dash is open source and the application build using this framework are viewed on the web browser.
Dash is Downloaded 600,000 times per month, it’s the original low-code framework for rapidly building data apps in Python, R, Julia and F#(experimental).
It’s written on top of Plotly.js and React.js. Dash is ideal for building and deploying data apps with customized user interfaces. It’s particularly suited for anyone who works with data.
Through a couple of simple patterns, Dash abstracts away all of the technologies and protocols that are required to build a full-stack web app with interactive data visualization.
Dash is simple enough that you can bind a user interface to your code in less than 10 minutes.
Dash apps are rendered in the web browser. You can deploy the apps to VMs or kubermetes clusters and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile ready.
There is a lot behind the framework. To learn more about how it’s built and what motivated Dash, read announcement letter or Dash is React for Python post.
Dash is an open source library released under the permissive MIT license. Plotly develops Dash and also offers a platform for writing and deploying Dash apps in an enterprise environment.
Python Dash is mostly suited for the quick and easy representation of big data which helps in analyzing and resolving issues. Power BI, on the other hand, has its data models focused on ingestion and building relatively complex models. Python is the best when it comes to handling streaming data.
Power BI vs. Tableau vs, Python Dash:
| Power BI | Tableau | Python Dash |
| It is provided by Microsoft | It is provided by Tableau | It is a Python library, provided by the Python Software Foundation |
| It is available at a moderate price | It is expensive than Power BI | It is an open-source programming language that is freely available for everyone to use. |
| Need a business/private email to open an account | Need a business/private email to open an account | Any email address is acceptable |
| Uses DAX for Measures and Calculated columns | Uses MDX for Measures and Dimensions | Uses dynamic, interpretive script programming language |
| Connect limited Data Sources but increases it Data Source connections in Monthly updates | It can connect to numerous Data Sources | Python has an ecosystem of modules and tools to collect data from multiple sources. |
| Can handle large Datasets using Premium capacity | Can handle large Datasets | Can handle large Datasets |
| It provides Account base subscription | It provides Key base subscription | No subscription necessary |
| Embedding report is easy | Embedding report is a Real time challenge | Dash is simple enough that you can bind a user interface to your code in less than 10 minutes |
| It is integrated with Microsoft Azure, which helps in analyzing the data and understanding the patterns of the product | Tableau has built-in machine learning capabilities which makes it suitable for doing ML operations on datasets | Dash is integrated with Python which offers multiple libraries in graphics that are packed with different features. Python is preferred for data analysis of the highest levels |
| It supports R and Python language-based visualizations | It provides full integrated support for R and Python | Dash is ideal for building and deploying data apps with customized user interfaces. |
Which one to choose, Power BI, Tableau or Python Dash?
Data Analytics field has been changed over time from traditional bi practice, embedded bi and collaborative bi. Initially, data analytics was led by companies like IBM, Oracle, SAP but now this is not the situation. Now, this led by companies like Microsoft, Tableau and Python because of their features like Embedded BI Collaborative BI, Data Blending, Data Binding and Multi Data Source Connection.
Power BI, Tableau and Python Dash have their own Pros and Cons. The right product can be chosen based on touchstones & priority.
| Touchstones | Power BI | Tableau | Python Dash |
| Description | A cloud-based business intelligence platform which offers an overview of critical data | A collection of intuitive business intelligence tools used for data discovery | A python framework created by Plotly for creating interactive web applications. It is best when it comes to handling Streaming Data |
| Visualization | Provides various visualizations | Provides a larger set of visualizations than Power BI | Provides numerous set of visualizations |
| OS Support | Only Windows | Windows and Macintosh OS | Mac OS, Windows, Linux, AWS, and others |
| Graphical features | Regular charts, graphs, and maps | Any category of charts, bars, and graphs | Dash is ideal for building and deploying data apps with customized user interfaces. Any category of charts, bars and graphs |
| Cost | Cheaper | Costly | Free |
| Organization | Suitable for Small, Medium & Large type of Organization | Suitable for Medium & Large type of Organization | Suitable for Small, Medium & Large type of Organization |
Data Analysis Expressions (DAX) is a programming language that is used throughout Microsoft Power BI for creating calculated columns, measures, and custom tables. It is a collection of functions, operators, and constants that can be used in a formula, or expression, to calculate and return one or more values.
In Multidimensional Expressions (MDX), a measure is a named DAX expression that is resolved by calculating the expression to return a value in a Tabular Model. This innocuous definition covers an incredible amount of ground.
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