Machine learning or system learning, we hear this thing more frequently nowadays. The reason is the sudden boom brought by new and innovative technologies in the world, and being a part of new technologies, people are liking and using this technology at a very high pace. Machine learning is a very vast field with a potent base and uses. However, it is the subpart of artificial intelligence but can be used without it in many areas. 


On a fundamental note, machine learning is the process to make a system learn some essential attributes of human intelligence such as thinking and acting. But in the big picture, it is the computational study of some complex computer algorithm that can solve any situation correspondingly to human intelligence. One of the valuable aspects of this technology is that it focuses on the machine's power of self-learning by feeding it data and other relevant information. When new data is given to the device, it automatically learns, changes and grows all by itself.


In current times, everything is getting automated, and innumerable data is being generated every minute. To manage such a large amount of complex data, machine learning technology comes into play. Its algorithm can be used for multiple purposes such as data cleaning, data preprocessing and data visualization to generate valuable outcomes that will help to enhance business workflow. After running through data, the insights generated by the ML algorithm can help predict the future situation of any organization. This whole process is done by using the concept of feature engineering or feature selection which is very complex and lengthy.


The quality of data inserted into the system is refined by using a feature selection process that also impacts the overall result quality of the machine learning model. This improvisation of data quality is essential for the model to give clear visuals about future business situations. The process of features selection mainly includes five steps: the acquisition of data, data cleaning, features engineering, generating a machine learning model, training and testing the model to make a future prediction. This whole process can be carried out multiple times to get clarity about the results.  


Let's make the knowledge acquisition process of this technology faster and more accessible by understanding each aspect with different slides and appropriate graphical representation.

Template 1: What is Machine Learning?

As we discussed before, it is the subset of artificial intelligence in which data is fed to the machine to make it learn by self-analyzing technique. Various computer algorithms are used to study and analyze the data and help the machine get an automated experience to make predictions. The data which is used to train the machine is called training data. In this process, no explicit programming is done to make a decision. Instead, it is entirely based on the outcomes served by the machine itself. Computation statistics and statistical modelling are two of the significant aspects of this process. 


The Machine learning algorithms are pretty complex but can be used for multiple purposes in the same process to make different predictions through the same data sets by modifying a bit in every go. Data mining and deep learning are also sub-parts of machine learning, focusing mainly on extensive and complex data sets.


Overall, machine learning is an expanded field with many attributes that must be learned and understood by using the appropriate approach. SlideTeam comes with an intelligent method that will enhance your learning and make things easily understandable. This method contains innovatively designed PPTs that have all the relevant information about the Machine learning concept and even define the practical aspects concisely and clearly. Please do not wait for long and garb these slides super quickly to make the best use of them.


Machine learning overview


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Template 2: Types of machine learning

Types - It mainly defines the approaches used by any machine to learn after the insertion of data. Machine learning is primarily of three types, that are-


Supervised learning: In this learning, the machine is fed with two types of labelled data, one is to train the model, and the other is to test the model. These are called training data and testing data, respectively. Both these datasets are used to teach models to the desired output and help in the decision-making process. The data is passed through the algorithms, minimizing error and using loss function to generate an accurate outcome. Classification and regression are two types of problems most commonly solved using supervised learning techniques.


Unsupervised learning: Unlike supervised learning, it uses unlabeled data to run an analysis algorithm. No human intervention is required while performing unsupervised ML algorithms as it makes the machine capable of discovering hidden patterns and data grouping within the datasets. Problems related to data clustering are most commonly solved using unsupervised learning techniques.


Reinforcement learning:  A sequence of decisions is generated through a reinforcement learning model to achieve an objective in a highly complex and uncertain environment to get the best outcomes. In this process, the computer recursively performs the trial function and learn by throwing error to generate an appropriate solution for any problem.


All these algorithms are based on earlier types and can be differentiated through data and analysis techniques. It can be understood broadly by using these PPT slides mainly designed to clear the difference between these methods in the best way. Enhance your learning and grasp the concept clearly by downloading these slides.


Machine learning types


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Template 3: Use cases of machine learning

Machine learning is recently the most trending technology and is getting more popular due to convenient features and extensive use cases. It has a wide range of applications in the real world. Let's put a light on the most frequently used application of machine learning.


Facial and speech recognition: Machine learning algorithms are primarily used for facial and speech recognition, especially in defence to identify objects, persons, weapons and places. Search by voice feature of google is one of the most common examples of machine learning speech recognition applications.


Self-driving cars: They are another most popular uses case of machine learning. In this process, the driving data is inserted into the automobile to learn to drive by itself. The unsupervised machine learning algorithms are used in this process to train the cars.


Virtual assistants: Virtual assistants such as Alexa, google assistant and Cortana are also based on machine learning techniques. In this process, the machine is filled with voice recognition data and responds accordingly when something is asked to be done.


Some other uses cases of machine learning include traffic prediction, loan prediction and product recommendation. You can have a better understanding of this whole concept. All you need to do is download and use these machine learning slides specially developed to make your learning more accessible and fast.


Machine learning applications


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Futuristically, Machine learning is bringing a drastic change in almost every sector with its ultimate capabilities and extensive uses. But the best utilization of any technology can only be done by getting sufficient knowledge of it. PPTs created by SlideTeam will help in every aspect to grasp the concept deeply in less time possible. So do not wait any longer and download these slides to enhance your understanding of this disruptive technology.