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Beginner’s Guide To Machine learning

Growing up we all had fantasies of having a friend who can understand us like no one else can, who knows our likes and dislikes better than us. well guess what folks your fantasy is not fantasy anymore, thanks to Machine Learning. Ever advancing technological advancements have amazed mankind with amazing inventions and discoveries. And on of such marvel is Machine Learning.

What is Machine Learning?

As its name sounds, Machine Learning or ML is a field of application of Artificial Intelligence which gives the machines, i.e. Computers, Mobiles, TVs or any electronic gadget which is capable of performing algorithmic computations. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that which makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect. You might be thinking how does Machine Learning or ML works? or how can a machine think like humans? Well, here is the answer. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

However, various machine learning certification are available in the market through which one can master machine learning easily.

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 to learn automatically without human intervention or assistance and adjust actions accordingly.
Coming back to our initial analogy, let's take my favorite toy. Now, I want my toy to know about me. So, what do I do?
Using some methods and algorithms (or computer programs in layman terms), I will make my toy able enough so as to observe my daily activities. Such as what do I like to eat, what do I love watching, what are my favorites, how do I spend most of my money and whatnot. From a basic detail such as my favorite color to my deepest darkest truths my toy, which is now a smart toy, now knows everything about me. Just by observing my day to day activities to collect my data and then processing it using Some machine learning methods it can know about me more than I know about myself.

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 are 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 large volumes of information. To get the maximum value from big data, businesses must know exactly how to pair the right algorithm with a particular tool or process and build machine learning models based on iterative learning processes. Some of the key machine learning algorithms are -

  • Random forests
  • Neural networks
  • Discovery of a sequence and associations
  • Decision trees
  • Mapping of nearest neighbor
  • Supporting vector machines
  • Boosting and bagging gradient
  • Self-organizing maps
  • Multivariate adaptive regression
  • SEO
  • Analysis of principal components

As mentioned above, the secret to successfully harnessing the applications of ML lies in not just knowing the algorithms, but in pairing them accurately with the right tools and processes, which include -

  • Data exploration followed by visualization of the model results
  • Overall data quality and management
  • Easy model deployment to quickly get reliable and repeatable results
  • Developing a graphical user interface for creating process flows and building models
  • Comparing various machine learning models and identifying the best
  • Identify best performers through automated ensemble model evaluation
  • Automated data-to-decision process

There wouldn’t be any safer bet than adding machine learning programming skills to your portfolio if you are seeking a career in the industry and pursuing a certified machine learning course can help in taking the precise step in the ML world.

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