Machine Learning Expert

Machine learning, which is a field of computer science and a part of Artificial Intelligence (AI) that uses statistical techniques to give computer systems the ability to "learn” progressively improve performance on a specific task with data, without being explicitly programmed. The name machine learning was coined in 1959 by Arthur amuel. Evolved from the study of pattern recognition & computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from & make predictions on data such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model
from sample inputs. 

Advantages of Machine Learning are:
It is used in so many industries of applications such as banking and financial sector, healthcare, retail, publishing and social media, etc.
It is used by Google and Facebook to push relevant advertisements based on users search history.
It allows time cycle reduction and efficient utilization of resources. Due to machine learning there are tools available to provide continuous quality improvement in large and complex process environments. If you want to become an expert of Machine learning than you must need to understand the finer details to a point where you can explain it in simple terms to just about anyone.
Below are some topics one should have basic knowledge.

  •  What is Data Science?

  •  What is Big Data?

  •  What is Machine Learning?

  •  What is Artificial Intelligence?

  •  How are the above domains different from each other and related to each other?

 

 

M L , Course Contents

1.Data Visualisation-1
2.Data Visualisation-2
3.Data Visualisation-3
4.Python-Pandas
5.Preprocessing-1
6.Preprocessing-2
7.Preprocessing-3
8.Preprocessing-4
9.Python_Data_Science-Module-1
10.Python_Data_Science Module-2
11.Introduction-1
12.Introduction-2
13.Load Data Set Sci-Kit learn part-1
14.Load Data Set Sci-Kit learn part-2
15.Load External Data Set
16.Multi Class Classification-1
17. Multi Class Classification
18.Split the Dataset-1
19.Split the Dataset-2
20.Train the Model