This article is an Introduction to Machine Learning also abbreviated as Mach.Learn.. Artificial Intelligence (AI) is always incomplete without Machine Learning. As we can observe, Machine learning is everywhere around us. For example, the software we use for our social interactions, is suggesting us the friends we may know. This is all data analysis and then presentation of results. All such decision making activities usually involve the machine learning algorithms and AI techniques. Hence, we may consider Machine Learning an important aspect of modern day scientific discoveries.
Eric Schmidt (Google Chairman) have said “Google`s self-driving cars and robots get a lot of press, but the company`s real future is in machine learning, the technology that enables computers to get smarter and more personal”
Humans always need the best experience of the latest technology and therefore We have seen machines performing the daily human being activities more efficiently and precisely.
The introduction to machine learning can be better understood by learning the concept of Artificial Intelligence. The concept, that machines are making the decisions on the basis of training data. Such data is learned from the human behavior. Then this behavioral data is analyzed by the machine with minimal human intervention. That leads to some logical and meaningful results that helps in impactful decision making.
Scientifically, the machine learning is the study of the algorithms, and statistical models. These models are programmable using the computer. therefore, these help the computer to perform different task. In the earlier times, programming algorithms and techniques were simpler. However, now scientists and researchers have paved their way through extensive and more complex data analysis and representation techniques.
Types of Machine Learning Algorithms?
- Supervised Learning:
Supervised Learning is a type that contains the target. Analysis is carried out on the basis of given data. Machine takes this data as input, processes it and then produces the output. Sensors are usually used as Input devices. For example: Regression, Decision Tree, KNN, Logistic Regression etc.
- Unsupervised Learning:
This type defines the class of the problems. Which differentiates the data in the inter-relationship of the data. Unsupervised learning works only on the inputs and therefore, have no working with the output like the supervised learning. For example Apriori algorithm and K-means.
- Reinforcement learning:
In this type of learning, the machine is capable of making decisions . It is a Hit and Trial method in which different environmental factors are tackled by the machine and the machine is made habitual as per the required outputs. As a result of this practice we get the certain outputs. For example as in Markov decision Process.
Different Machine Learning Projects
Following are some Machine Learning and AI based projects:
- Sentiment Analyzer
- Classification of iris Flowers
- Identifying Products Bundles from sales Data
- A music Recommendation System (Spotify, Sound Cloud, Patari)
- A machine Learning Gladiator
- Tensor Flow
- Predict Wine Quality
- Walmart Sales Forecasting
- Disease Prediction
- Recommending systems (Netflix, YouTube)
Artificial Intelligence and Machine Learning are very important subjects of this era. Therefore, people are investing their resources in these domains.
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