Machine Learning (Part1)

The primary goal of this course is to introduce you to Pandas, Numpy and Clustering. By the end of this course, you’ll know how to creating dataframe with CSV file, checking null values, Cleaning and organizing data, K-means clustering, Numpy array creation and operations. By the time you reach the end of this course, you’ll be able to see your data manipulation skills set to dive deeper into advanced machine learning topics.

  • Create a list of n numbers. Find the sum of numbers in a list. 
  • Find the value with a given  index .
  • Change values with indexes. 
  • Dataframe imports and operations
  • Using Jupyter notebook to practice data manipulation.
  • Use of built-in function in Python for data science
  • Real life project to test learnings.

What is the target audience?

The course is designed for complete beginners to be able to follow it. This can be is intimidating for beginners, but you will see it is quite detailed for beginner to intermediate levels. There are a lot of tutorials, documentation and advice already out but how do you start and proceed with learning ? We are here to help!

Starting Course

1
Learn Data Science at PyCademy
3min

In this two-day workshop, we will help you get started learning how to program in Python 3, one of the most popular languages for quick scripts, production software, and doing data science

Pandas

1
Use of dataframe
2
Creating dataframe from csv
3
Skiprows, specifying separator, specifying column names
4
Data exploration
5
Checking null value,Dropping column or rows, Filling in null values
6
Selecting columns and rows
7
Select rows in a dataframe which column is a certain value
8
One-hot encoding
9
Project

Numpy

1
Numpy array creation, operation
2
Basic functions in Numpy
3
numpy.random

Clustering

1
K-means
2
Hierarchical
3
Project
Faq Content 1
Faq Content 2

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Enrolled: 44 students
Duration: 10 hours
Lectures: 16
Video: 4 hours
Level: Beginner

Archive

Working hours

Monday 9:30 am - 6.00 pm
Tuesday 9:30 am - 6.00 pm
Wednesday 9:30 am - 6.00 pm
Thursday 9:30 am - 6.00 pm
Friday 9:30 am - 5.00 pm
Saturday Closed
Sunday Closed