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What is Artificial Intelligence?

Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry. 


What is Machine Learning?

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

But, using the classic algorithms of machine learning, text is considered as a sequence of keywords; instead, an approach based on semantic analysis mimics the human ability to understand the meaning of a text.

Some Machine Learning Methods

Machine learning algorithms are often categorized as supervised or unsupervised.

  • Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
  • In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
  • Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.
  • Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.

Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing largge volume of information.


Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. 

Data science combines multiple fields including statistics, scientific methods, and data analysis to extract value from data.

Those who practice data science are called data scientists, and they combine a range of skills to analyze data collected from the web, smartphones, customers, sensors, and other sources.

Challenges of implementing data science projects

Challenges of implementing data science projects

Despite the promise of data science and huge investments in data science teams, many companies are not realizing the full value of their data. In their race to hire talent and create data science programs, some companies have experienced inefficient team workflows, with different people using different tools and processes that don’t work well together. Without more disciplined, centralized management, executives might not see a full return on their investments.

This chaotic environment presents many challenges.

Data scientists can’t work efficiently. Because access to data must be granted by an IT administrator, data scientists often have long waits for data and the resources they need to analyze it. Once they have access, the data science team might analyze the data using different—and possibly incompatible—tools. For example, a scientist might develop a model using the R language, but the application it will be used in is written in a different language. Which is why it can take weeks—or even months—to deploy the models into useful applications.

Application developers can’t access usable machine learning. Sometimes the machine learning models that developers receive are not ready to be deployed in applications. And because access points can be inflexible, models can’t be deployed in all scenarios and scalability is left to the application developer.

IT administrators spend too much time on support. Because of the proliferation of open source tools, IT can have an ever-growing list of tools to support. A data scientist in marketing, for example, might be using different tools than a data scientist in finance. Teams might also have different workflows, which means that IT must continually rebuild and update environments.

Business managers are too removed from data science. Data science workflows are not always integrated into business decision-making processes and systems, making it difficult for business managers to collaborate knowledgably with data scientists. Without better integration, business managers find it difficult to understand why it takes so long to go from prototype to production—and they are less likely to back the investment in projects they perceive as too slow.

What’s the difference between data science, artificial intelligence, and machine learning?

What’s the difference between data science, artificial intelligence, and machine learning?

To better understand data science—and how you can harness it—it’s equally important to know other terms related to the field, such as artificial intelligence (AI) and machine learning. Often, you’ll find that these terms are used interchangeably, but there are nuances.

Here’s a simple breakdown:

  • AI means getting a computer to mimic human behavior in some way.
  • Data science is a subset of AI, and it refers more to the overlapping areas of statistics, scientific methods, and data analysis—all of which are used to extract meaning and insights from data.
  • Machine learning is another subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.

And for good measure, we’ll throw in another definition:

  • Deep learning, which is a subset of machine learning that enables computers to solve more complex problems


A data scientist must have a strong understanding of data analysis as well as strong programming abilities. Based on the demands of the company, they hire a range of expertise. 

This career’s attractive skills can be split into two categories: 

  • Technical Skills 

To be a good data scientist, you must specialize in Algebra, Computer Engineering, and Statistics. 

Other technological abilities needed include: 

  • Programming – Competence in machine languages and programming languages. 
  • Awareness of analytical tools – SAS, Hadoop, Spark, and R are some of the most common data scientists’ analytical tools. 
  • Workability of Unstructured Data – The ability to handle unstructured data obtained across various networks. 
  • Non-Technical Skills 

The majority of a person’s abilities are classified as non-technical. They are as follows: 

  • Exceptional business sense 
  • Capacity to interact 
  • Intuition about data

To move forward in the difference between data science and machine learning, it’s time to talk about machine learning. A machine learning expert ought to have a strong command of some basic skills. Let’s go through a couple of key skills you’ll need. 

  • Probability and statistics

Your experience of theory has a lot to do with your comprehension of algorithms. Examples include Hidden Markov templates, Naive Bayes, and Gaussian Mixture, among others. These algorithms would be challenging to grasp if you aren’t acquainted with numbers and chance. 

  • Evaluation and modeling of data

Evaluation of the efficacy of various models at frequent intervals is vital in maintaining the reliability of the measurement method with ML. Numerous approaches such as regression and classification may be used to evaluate the error or consistency of a model. Along with that, you’ll need an assessment plan. 

  • Algorithms for machine learning

It’s crucial to consider how various machine learning algorithms operate and which algorithm is better for a specific scenario. You’ll need to read about partial differential equations, gradient descent, quadratic programming, convex optimization, and other relevant topics. 

  • Languages for programming

You’ll still need to know how to program in languages like Java, R, Python, and C++ if you choose to work in machine learning. These programming languages can assist you at various levels of your machine learning project. 

  • Techniques for signal processing

Machine learning depends highly on feature extraction. You will be required to use various specialized signal processing algorithms, such as bandlets, shearlets, contourlets, and curvelets.


Data science is a deep, interdisciplinary sector that uses the vast quantities of data and computing power at its disposal to obtain insights. Machine learning is one of the most exciting developments in contemporary data science. Machine learning encourages machines to learn on their own from the vast amounts of data accessible. 

These systems have a wide range of uses, but they are not limitless. While data science is strong, it can only be used effectively if you have highly trained workers and high-quality data.


Author: refuge_2020

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