Introduction
There has never been a better alternative to Python when it comes to Machine Learning and Data Analytics, thanks to a lot of libraries that are available to us for free, and they offer such a great power that some of the most difficult tasks can be achieved with the help of them.
Let's first understand what ML with Python is and how various python libraries have made it very easy for us to make advanced machine learning models, and then let's look at almost all of the popular libraries that are available in Python and where you can find their uses.
Also Read About, Python for Data Science
Machine Learning
Machine Learning is nothing but predicting the future with the help of past data, there can be many techniques and use cases to do this, but the essence remains the same.
Sometimes we are given what the output of these past data points is, and sometimes the output of the data points is not even known to us and not even the definition of the output for the data point. This is precisely what is the case between supervised and unsupervised learning.
In supervised learning, we get labelled data, i.e. in this, everything is told to us what information the data has.
What are the different types of machine learning?
Traditional machine learning is defined as the process through which an algorithm learns to improve its prediction accuracy. The four main approaches are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. The type of data that data scientists want to predict determines the algorithm they apply.
Supervised learning: Data scientists feed labelled training data to algorithms and tell them the variables they want the programme to seek for correlations between in this type of machine learning. The input and output of the algorithm are both available.
Unsupervised learning: This machine learning employs algorithms that train on unlabeled data. The algorithm looks for links between datasets that are important. The data utilized to train algorithms, as well as the predictions or suggestions generated by them, are all predetermined.
Semi-supervised learning: This method of machine learning combines the two previous approaches. Although data scientists may feed an algorithm primarily labelled training data, the model is allowed to explore the data and expand its understanding of the set.
Reinforcement learning: Reinforcement learning is a technique used by data scientists to teach a machine to execute a multi-step procedure with precisely stated rules. Data scientists submit labelled training data to algorithms and indicate the variables they want the software to seek for correlations between in this type of machine learning. Both the input and output of the algorithm are supplied.
Who's using machine learning, and what's it used for?
Machine learning is used in a wide range of applications. One of the most well-known instances of machine learning in action is the recommendation engine that drives Facebook's news feed.
Facebook uses machine learning to personalise how each member's feed is delivered. If a member frequently visits a group's posts, the recommendation engine will start highlighting the activity of that group in the feed.
The engine works behind the scenes to encourage the member's online behaviour habits. If a member's reading habits change and he or she fails to read posts from that group in the coming weeks, the news feed will be changed.
Other applications of machine learning, in addition to recommendation engines, include:
Customer relationship management. Machine learning models can be used in CRM software to scan email and motivate sales team members to respond to the most critical communications first. Advanced systems can even make recommendations for possible beneficial solutions.
Business intelligence. Machine learning is used in analytics suppliers in their software to discover anomalies, patterns of data points, and potentially crucial data points.
Human resource information systems.HRIS systems can utilize machine learning models to go through applications and locate the best applicants for open positions.
Self-driving cars. A semi-autonomous vehicle may use machine learning techniques to detect a partially visible object and alert the driver.
A type of virtual helper is a virtual assistant. Smart assistants frequently use supervised and unsupervised machine learning models to analyze spoken speech and provide context.
Recommendation Systems: When you open Instagram or Twitter how do you think they give you a personalized feed? Here also machine learning comes into use.
Natural Language Processing: Where machines decipher the language and make the meaning out of it.
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