There are many case studies of ML which we can refer to. Since we are only at the basic Machine Learning tutorial, we will take one for an overview.
So, let’s talk about Tesla.
Tesla is now a big player in the electric automobile industry. It is widely known for its advanced and futuristic cars. The company says that the cars have their own AI hardware. Tesla is using AI for making self-driving cars. At the moment, cars are not completely autonomous. The company is working on the thinking algorithm for cars. It is currently working with NVIDIA on an unsupervised ML algorithm.
This step by Tesla would be a game-changer for many reasons. The cars send data directly to tesla’s cloud.
The car sends the driver’s seating position, traffic of area, and other valuable info. The car is equipped with various internal and external sensors that detect things.
This data is used for making highly detailed maps that are used for navigation. The map will show slow traffic movements, areas to avoid, etc.
The cloud of tesla holds the data of all of its cars. So, the ML algorithm is applied in the cloud server. It can train all the vehicles to go according to the map. Also, cars use edge computing to decide the car movements and other actions.
Tesla, however, came under pressure from various firms. Many still think that the system might cause more accidents.
But, this is not true. With the advancement in systems, cars are adapting fast. The data collected from the car is really important.
In terms of money, it could generate a revenue of 750 to 800 billion dollars by 2030. If all goes well, Tesla might become one of the most profitable companies in the future.
Hence, in this Machine Learning tutorial, we studied what is machine learning. Why is it necessary in today’s time and how does it work. We also looked at various ML algorithms and approaches and studied them in detail. We saw various leading companies that are using ML at the moment. Then, at last, we looked at a case study of tesla.
The case study helped to understand various innovative and economic aspects of ML. From this Machine Learning tutorial, we can conclude that ML has now become a really important part of today’s world. Therefore, we must learn about it in detail to make further progress
This is a basic Machine Learning tutorial Case Study.Therefore, the explanation will be in a way that it is simpler to understand.
Machine Learning or ML is one of the most popular fields in today’s technology market. Having knowledge about ML can be really good because of its huge demand.
In this Machine Learning tutorial, we will be looking at what exactly Machine Learning is. We will also be studying about its needs, it’s working. The various types and approaches to Machine Learning will mark the end of the case study.
Machine Learning or ML is a field that makes predictions using algorithms. It helps to make the machines learn from the data given to them.
Machine Learning uses various statistical approaches for making predictions. It also has a major role in pattern finding in data, that is, it can find various patterns in complex data given to it.
Machine Learning is a part of its larger domain, which is Artificial Intelligence (AI). It deals with algorithms that learn from given data and make predictions.
Why Do We Need Machine Learning?
Machine Learning has made the analysis of large amounts of data very efficient. Normal algorithms are not capable of doing complex tasks, which is why ML is in use.
The iterative aspect of machine learning is vital because as models are exposed to new data, they’re ready to independently adapt. They learn from previous computations to supply reliable, repeatable decisions and results. It is a science that’s not new – but one that has gained fresh momentum.
ML has made work a lot more easier in many areas today. It is widely used in fields like banking, healthcare, science and many more.
Machine Learning is a very important part in today’s technology. Without it, many tasks will not be possible to achieve. Tasks like pattern recognition, prediction of future data would not be possible without ML. Even your internet searches use Machine Learning methods.
For every Google search you do, the algorithm learns from the previous searches. Based on your search history, it provides you the most desired results in your next search.
How Does Machine Learning Work?
Machine Learning has a basic working pattern. The algorithm takes both the input and output of the program. Then it trains a model using the given data. The algorithm looks for various patterns in the given data.
These patterns help in future predictions. The results obtained from training the model will help in improving the model’s working.
For a better understanding, take the example of Google searches. If you search for something on Google, you get a result. Here the search is the input data and the result is the output data. If you make more than one search, you will get many results.
Google has a search algorithm. This algorithm takes the searches and search results as input and output data.
The algorithm trains the model using this data. After this, if you search for the same thing again you might get even better results. Again, this new result helps to train the model for future searches. This is how Machine Learning works.
Machine Learning Approaches
There are various approaches when it comes to implementing Machine Learning algorithms.
Machine Learning has been under research for several decades now. Hence, there are many approaches and algorithms in use and under research.
The approaches in ML are now classified into two categories. These are:
a. Grouping of the algorithms by their learning style.
b. Grouping of the algorithms by their similarity.