Machine Learning tutorial covers basic and advanced concepts, specially designed to cater to both students and experienced working professionals.
This machine learning tutorial helps you gain a solid introduction to the fundamentals of machine learning and explore a wide range of techniques, including supervised, unsupervised, and reinforcement learning.
Machine learning (ML) is a subdomain of artificial intelligence (AI) that focuses on developing systems that learn—or improve performance—based on the data they ingest. Artificial intelligence is a broad word that refers to systems or machines that resemble human intelligence. Machine learning and AI are frequently discussed together, and the terms are occasionally used interchangeably, although they do not signify the same thing. A crucial distinction is that, while all machine learning is AI, not all AI is machine learning.
What is Machine Learning?
Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.
Features of Machine learning
- Machine learning is data driven technology. Large amount of data generated by organizations on daily bases. So, by notable relationships in data, organizations makes better decisions.
- Machine can learn itself from past data and automatically improve.
- From the given dataset it detects various patterns on data.
- For the big organizations branding is important and it will become more easy to target relatable customer base.
- It is similar to data mining because it is also deals with the huge amount of data.
Data and It’s Processing:
Supervised learning :
Unsupervised learning :
Dimensionality Reduction :
Natural Language Processing :
Neural Networks :
ML – Deployment :
ML – Applications :
Prerequisites to learn machine learning
- Knowledge of Linear equations, graphs of functions, statistics, Linear Algebra, Probability, Calculus etc.
- Any programming language knowledge like Python, C++, R are recommended.
FAQs on Machine Learning Tutorial
Q.1 What is Machine learning and how is it different from Deep learning ?
Machine learning develop programs that can access data and learn from it. Deep learning is the sub domain of the machine learning. Deep learning supports automatic extraction of features from the raw data.
Q.2. What are the different type of machine learning algorithms ?
- Supervised algorithms: These are the algorithms which learn from the labelled data, e.g. images labelled with dog face or not. Algorithm depends on supervised or labelled data. e.g. regression, object detection, segmentation.
- Non-Supervised algorithms: These are the algorithms which learn from the non labelled data, e.g. bunch of images given to make a similar set of images. e.g. clustering, dimensionality reduction etc.
- Semi-Supervised algorithms: Algorithms that uses both supervised or non-supervised data. Majority portion of data use for these algorithms are not supervised data. e.g. anamoly detection.
Q.3. Why we use machine learning ?
Machine learning is used to make decisions based on data. By modelling the algorithms on the bases of historical data, Algorithms find the patterns and relationships that are difficult for humans to detect. These patterns are now further use for the future references to predict solution of unseen problems.
Q.4. What is the difference between Artificial Intelligence and Machine learning ?
|Develop an intelligent system that perform variety of complex jobs.
||Construct machines that can only accomplish the jobs for which they have trained.
|It works as a program that does smart work.
||The tasks systems machine takes data and learns from data.
|AI has broad variety of applications.
||ML allows systems to learn new things from data.
|AI leads wisdom.
||ML leads to knowledge.