We are living in a world full of data, that is enriched with more computational power and more storage resources. This data contains an enormous amount of information, within a single click u made a lot of data. This data is increasing day by day but we have to draw the real sense of it to solve the real-world problem. Organizations are dealing with it by building intelligent systems using the concepts and methodologies from Data Science, Data Mining and Machine Learning. Among them, Machine learning is the most exciting field of computer science. It would not be wrong if we call machine learning application and science of algorithms that provide sense to the data.
What is Machine Learning?
Machine Learning (ML) is the field of computer science with the help of which computer system can provide sense to data in the same way as we humans do.
In Simple Words, ML is a type of artificial intelligence that extracts patterns out of raw data by using the algorithms. The main focus of ML is to allow computer systems to learn from their experience without being explicitly programmed.
Need for Machine Learning:
Human beings, at this moment are the most intelligent and advance species on earth because they can think, evaluate and solve complex problems. On the other side, AI is still in its initial stage and haven’t surpassed human intelligence in many aspects. Then the question is that what is the need to make machine learn? The most suitable reason for doing this is, “to make decisions, based on data with efficiency and scale”.
Now, Organizations are investing heavily in new technologies like Artificial Intelligence, Machine Learning and Deep learning to get the key information from the data to perform several real-world tasks and solve problems. We can call it as data driven decisions taken by machines, particularly to automate the process. The fact is we have to solve real world problems with efficiency at a huge scale. This is why the need of Machine Learning is arising.
Challenges in Machine Learning:
While Machine Learning is rapidly evolving. making significant strides with cybersecurity and automation cars, this segment of AI as a whole still has a long way to go. The reason behind this is that ML has not been able to overcome a number of challenges. The challenges that ML is facing currently are-
- Quality of Data – Having a good quality of data for ML algorithms is one of the biggest challenges. The use of low-quality data leads to problems related to data pre-processing and feature extraction.
- Time-Consuming Task – Another Challenge faced by ML models is the consumption of time especially for data acquisition, feature extraction and retrieval.
- Lack of specialist persons- As ML technology is still in its infancy stage, the availability of expert resources is a tough job.
- No Clear Objective – Having no clear objective and well-defined goal for business problems is another key challenge for ML because this technology is not that mature yet.
- Issue of Overfitting and Underfitting – If the model is overfitting, it cannot be represented well for the problem and similarly happens if it is under fitted.
- Curse of Dimensionality – Another Challenge of ML model aces too many features of data points. This can be a real hindrance.
- Difficulty in Deployment – Complexity of the ML model makes it quite to be deployed in real life.
Applications of Machine Learning:
Machine Learning is the most rapidly growing technology and according to researchers, we are in the golden year of AI and ML. It is used to solve many real-world complex problems that cannot be solved with the traditional approach. Following are some real-world applications of ML-
- Sentiment Analysis
- Error detection and prevention
- Weather forecasting and prediction
- Stock Market analysis and forecasting
- Speech Synthesis
- Speech Recognition
- Object Recognition
- Fraud Detection
- Recommendation Systems
- Image detection
So, this was the Introduction to Machine Learning in the next Blog I will be sharing with the types of Machine Learnings