#data_cleaning

Machine Learning is nothing but we are using machines to predict results based on incoming data. Data is very precious these days. More the number of data the more options we have for trying out various things. Companies like google and facebook are very valuable these days because they are the only one that can collect huge volumes of data on a daily basis. Therefore, Machine Learning is pioneered by these companies that has huge volumes of data. Most companies do not have large data to make machine learning worth their time and effort. You need huge data to implement Machine Learning effectively. Big companies usually share their model but keep their data private. That’s what gives these companies a competitive advantage over the others.

Steps in Machine Learning :

1 Define the Goals [ what do we want to accomplish] 2.Data Gathering [the hardest part to do. More the data the more options we have.] 3.Data Parsing [Cleaning and Spliting Data into Training sets (80%) and Testing sets(20%)] 4.Model creation [Model 1, Model 2, Model 3] 5.Accuracy Testing (testing based on the testing sets and check how accurate the model is and whether it complies with our goals) 6 Improve the process [try for improved model]

Check out our master program in Data Science and ASP.NET- Complete Beginner to Advanced course and boost your confidence and knowledge.

Showcase your skills in Data Analytics by joining our comprehensive three months program, by learning all the tools, softwares, libraries, and concepts taught in a case based manner, and solve real-world problems.

We go to industries, understand their requirements and find out what they want to cover in our program, design case studies inspired by the discussions we had with our industry partners, and solve a few real world problems. For very problem, we pick up a most challenging dataset, and try to look for insights in them. Most people make a mistake of gathering data from somewhere, run some code and generate a wonderful graph from it and they are done with it. No. your job is to get the most valuable insights from the data and try to pick up slightly challenging problems and try to apply them on real world problems and that's how you build real world skills required for Data Analytics.

Basically, we will be covering the 6 phases of Data Analytics life cycle in our training programme.

What are the goals and objectives

Understanding data and the process of acquiring data.

Importance of Data Cleaning and Data Transformation

Methods of enhancing Data

Data Analytics

Data Visualization

For doing the above you need to acquire two important skillset:

a) SQL (for extracting and manipulating data)

b) Power BI (for data analytics & visualization)

Convey your data with visualizations on dashboard and reports using Power BI. We build a series of portfolios from the basic to state-of-the-art deep learning models.

JOIN OUR DATA ANALYTICS TRAINING PROGRAM AT EDUJOURNAL TODAY.