The basic difference between machine learning and deep learning

The basic difference between machine learning and deep learning
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In this article, we'll explore the critical features of machine learning. Along with the comparable features of machine learning and deep learning. We can utilize the capabilities afforded by machine learning to resolve various categories of problems. Now, machine learning utilizes various approaches in order to resolve and provide solutions. Some of the approaches adopted by machine learning to resolve problems includes Classification, Clustering, Forecasting, Simulation, Regression, and Optimization. With the Classification approach, the application learns from the input data. And then utilizes what it has learned to classify newly identified observations.

We can utilize Clustering algorithms to classify each data point into a specific group, depending on the similarity of the identified attributes. Now the objective of the forecasting algorithms provided in machine learning is to achieve accuracy of data against time. Simulation provides a view of simulation based machine learning, using the simulations of learned models. And Regression provides various algorithms for predictive modeling, by investigating the relationship between a dependent and independent variable. And finally, Optimization is basically an approach of extracting relevant knowledge and information from a huge volume of data. Machine learning techniques are used to generate new optimization understanding that can be utilized by various systems.

Machine Learning Phases:


Now let's identify the approach to implement applications using machine learning principles. Machine learning can be a systematic approach, once we've identified the use cases. And after we've identified the use cases we'll be executing or perform phase activities. The first activity is the data collection. There are diversified sources of data today. And during the data collection phase, we need to focus on bringing in data from multiple sources. After we've collected the data from diversified sources. We need to plan the data cleansing approach during the data cleansing phase. During this phase, we'll be focusing on detecting and removing errors and inconsistencies from the data to improve the quality of the data. Now after we've cleansed the data, we will move into the third phase, where we will be building on the training data sets for the training models. We will also need to test the datasets in order to validate the model. And it's essential that we classify the data to optimize the usage.

Finally, after we've created the training datasets, we'll apply the data to generate training models. Now, understanding data inconsistency is an important task. There are various types of data inconsistencies that can impact the outcome of the training models. And some of the data inconsistency includes under-fitting, over-fitting, unstable data, and unpredictable data formats. We can resolve these data inconsistencies by selecting or applying the right machine learning algorithms. 

Deep Learning:


Deep learning is considered as one of the subfields, or branches, of machine learning, which is based on learning data. Deep learning unifies machine learning with artificial intelligence. And it's used to build complex neural networks based on the human brain model. Now deep learning is prominently used to solve the problems that are classified under the semi-supervised algorithms of machine learning. There are various techniques of implementing deep learning. And some of the prominent techniques include convolutional neural networks, Deep belief learning, and RBM, or Restricted Boltzmann machine. 

The convolutional neural network applies feed forward artificial intelligence features that are used prominently in face and speech recognition applications. Deep belief learning is composed of multiple layers that are used to extract deep hierarchical representation of training data.

And finally, RBM, which is popularly known as Restricted Boltzmann machine, can be used for various tasks. Like dimensionality reduction, collaborative filtering, regression, and classification. Now, let's quickly examine and identify the cross-cutting relations between machine learning, deep learning, and artificial intelligence. Artificial intelligence is a border concept, which is composed of the capabilities that power machine learning, representational learning, and deep learning. To ensure that we're able to build intelligent systems, provide machine automation, and data points for decision making.

Final words:


If we can conclude it in simple terms then Machine learning basically helps computers to learn from the data and make smarter decisions like how we learn from our own past experiences. It can use classification, regression, clustering, forecasting, simulation & optimization to sort the given data or cluster them into groups, make predictions and find patterns between the huge amount of information. To make it work efficiently we first collect data, clean it to remove unwanted mistakes, and then train the machine learning's respective model using that data to get the desired results.

Whereas Deep learning is a special type of machine learning that works more likely of human brain itself. Basically deep learning uses convolutional neural networks, Deep belief learning and RBM to perform complex tasks like face recognition or NLP or (Natural language processing).  

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