The Concept of Machine learning includes data science
1. In general ml is applied to create an algorithm, we don’t give instruction Instead we provide an algorithm that lets an ml learn itself.
2. Machine learning is a process of trial and error.
Types of Machine Learning :
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Supervised Learning :
supervised learning deals with labelled data, it has targets eg: giving data and expecting possible outcomes.
The Widely utilized algorithm in supervised learning are :
- SVM — Support Vector Machines.
- NN — Neural Networks.
- Deep Learning (has high accuracy).
- Random Forests.
- Bayesian Networks.
Unsupervised Learning :
It deals with unlabelled data. If we have large data we don’t have enough time to process it. To overcome this we first go for unsupervised learning and go to supervised learning.
The most applied algorithm in unsupervised learning are :
- K-means algorithm.
- Deep Learning.
Reinforcement Learning :
In each process providing a reward to the model only if positive outcomes are obtained. It is similar to supervised learning but maximizing reward. This learning aims to increase objective function(mathematical equation).
The most applied algorithm in Reinforcement learning is:
- Deep Learning.
Deep Learning algorithm solves all the three major types of ml in different ways
Necessary programming languages and software :
- Python — suitable for mathematical and statistical computations.
- py are easily adaptable.
- Matlab, SQL, Programming with R.
- Software: Excel for big data, Apache Hadoop, MongoDB.
Job role in data science :
- Traditional Data: Data Architect, Data Engineer, DB Administration.
- Big Data: Big Data Architect, Big Data Engineer.
- Bi: Bi analyst, Bi Consultant, Bi Developer.
- Data Science: Data Scientist, Data Analyst.
- Machine Learning: Data Scientist, Machine Learning Engineer.
