Machine Learning and Data science

Machine Learning and Data Science


Companies like Facebook, Google and Amazon have got a lot of data about us. Even the small companies have got a lot of data like signup information, number of logins, Product purchase, products that we are looking for. All this data can be processed and can give any company a boost in productivity and increase in sale.
That is why machine learning is growing so fast.

Companies can offer amazing features like quick replies that are context based in Gmail, Uber driver arrival time or time to reach at the destination via Google maps, self-driving cars etc. This is just a start of machine learning and power of data science.

Welcome to data science and machine learning course!
One of the best online resource to understand and implement Machine learning and data science concepts. Usually, people think that data science can only be learned by Ph.D. but that not true, anyone can learn data science and machine learning.

What will you learn in this course?

We will start with python installing and getting a refresher on it. We will take some challenges like printing patterns, multiplication table, and web scrapping to refresh python memories.
After that, we will move to Anaconda distribution so that we don’t have to worry about installation anymore.

Numpy
First we will deal with Numpy library. This is one library that every data science and Machine learning student should master.

Pandas
The real fun begins with pandas. Bring data from HTML, css, xls or any database, pandas can handle almost everything.

Matplotlib
One of the key aspect of data science is to understand the data and it’s pattern. This all is done using graphs. Matplotlib helps you to plot variety of graphs and deduce results from it.

Seaborn
Seaborn is another plotting library. But it is comparatively it is much easier and powerful. We will be analyzing 911 call dataset, iris flower dataset and Fat consumption through this library.

Machine Learning

After we will learn about supervised and unsupervised and reinforcement learning. These are the core pillars and division criteria for all machine learning algorithms.

Machine Learning Algorithms
After that we will take on most common machine learning algorithms. We will take mathematics and code part of every algorithm like,

Linear Regression Algorithm
Decision Tree Regression Algorithm
K-nearest neighbors Algorithm
Support Vector Machine classifier aka SVM
Naive Bayes Algorithm

The main goal of this course is to make sure that you understand Machine learning and data science. Catch you inside the course.

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