Exploratory Data Analysis

AI for Every One

Machine Learning with Python

Machine learning helps computers act without explicitly programmed. Machine learning has helped us develop self-driving cars, speech recognition algorithms, and other useful applications that we use them dozens of times a day without knowing them.

In this course, you will learn the most effective machine learning techniques. You will get opportunity to implement them through hands-on projects and get them to work for yourself. In this course, you will not only learn the theoretical underpinnings of learning but also learn to apply the techniques to solve new problems.

Today, machine learning is one of the most in-demand skills for job. The hiring in the machine learning field has grown 74% annually for the last four years. This course will help all those who are interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning and Supervised Learning.

Python AI

Data Analysis with Python

In this course, you will learn how to analyze data using Python. During this course, you will learn starting from the basics of Python to exploring different types of data. You will also learn, how to load data from different sources such as CSVs.

You will also learn how to prepare data for analysis, perform statistical data analysis, and create meaningful data visualization. You will learn libraries such as Numpy, Pandas, Matplotlib, and Seaborn to process and visualize data.

We will start with the installation of Python using Anaconda distribution. You will learn how to use Spyder, iPython, and Jupyter notebook.

We will cover the fundamentals of Python that are critical for data analysis and machine learning projects. You will learn NumPy which includes multidimensional arrays with broadcasting capabilities, mathematical functions, linear algebra functionalities, and tools to read and write array data to disk.

You will also learn pandas library that includes series, DataFrame, Indexing, summarizing, and computing descriptive statistics.