Python for Machine Learning

BY
Great Learning

Lavel

Beginner

Mode

Online

Duration

2 Hours

Fees

Free

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based

Course and certificate fees

Type of course

Free

certificate availability

Yes

certificate providing authority

Great Learning

The syllabus

Module 1 - Intro to Numpy

  • In this section, you will be introduced to the NumPy library to add functions and methods to the program without actually writing the code.

Module 2 - Joining NumPy Arrays

  • There are three different modules to join Numpy arrays: vstack, hstack, and column stack. You will learn all these methods one by one in this module.  

Module 3 - Numpy Intersection & Difference

  • We shall understand what sect difference and intersection are at the beginning of this section. We shall then understand how to extract the exact elements by excluding other elements from the array with demonstrated code snippets.

Module 4 - Numpy Array Mathematics

  • This section will explain and demonstrate working with various mathematical operations using arrays like sum, increment, mean and median.

Module 5 - Saving and Loading Numpy Array

  • We will understand how to load and store a NumPy array in this section. We shall learn to work with arrays, starting from creating a NumPy array to storing and loading it with demonstrated sample codes.

Module 6 - Intro to Pandas

  • We shall understand Panel Data for data manipulation and analysis in Pytho

Module 7 - Pandas Series Object

  • We shall understand one-dimensional labeled arrays in this section. We will learn to import the Pandas library, create a series object using the inbuilt data type, and work with it with demonstrated code snippets.

Module 8 - Intro to Pandas Dataframe

  • After understanding the basics of Pandas Dataframe, you will learn to work with it through a sample demonstration in this section.  

Module 9 - Pandas Functions

  • We shall discuss the Pandas functions like mean, median, maximum, and minimum in this section. We shall also understand working with different methods for each of these functions.

Instructors

Mr Bharani Akella
Data Scientist
Great Learning

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