Machine Learning for Absolute Beginners - Level 2
4.3 (106 ratings)
20,892 students enrolled

# Machine Learning for Absolute Beginners - Level 2

Learn the Python Fundamentals and Pandas Library for Data Science Projects
New
4.3 (106 ratings)
20,892 students enrolled
Created by Idan Gabrieli
Last updated 8/2020
English
English [Auto]
Current price: \$125.99 Original price: \$179.99 Discount: 30% off
23 hours left at this price!
30-Day Money-Back Guarantee
This course includes
• 4 hours on-demand video
• 1 article
• Access on mobile and TV
• Certificate of Completion
Training 5 or more people?

What you'll learn
• Python for Data Science and Machine Learning Projects
• Learn to use the Pandas Data Science Library
• JupyterLab Development tool
• Develop Jupyter Notebooks
• Selecting, Filtering, and Cleaning Data
• Grouping, Sorting, and Exporting Data
Course content
Expand all 42 lectures 03:59:38
+ Getting Started with Level 2!
4 lectures 24:48
Preview 02:19
Anaconda Installation
04:22
JupyterLab Overview
03:51
Working with a Jupyter Notebook
14:16
+ Python Fundamentals for Data Science
11 lectures 01:16:28
Preview 02:52
Variables and Data Types
07:22
Strings
07:44
Lists
09:44
IF and For-Loop Statements
07:08
Functions
07:59
Dictionaries
11:01
Classes, Objects, Attributes, and Methods
07:28
Importing Modules
07:34
Libraries for Data Science Projects
07:03

Hi,

Please download the provided Jupyter notebook files and open them in the JupyterLab tool. The exercise is divided into several steps correlated to the lectures in this section. Each step includes several questions, and per each question, there is an empty cell to provide your answer. In addition, please use the second Jupyter notebook file which is the solution for the exercise to validate your answers step by step.

Please let me know if you have any questions or remarks about the content of the exercise.

Good Luck!

Idan .G.

Exercise #1 - Python Fundamentals
00:33
+ Introduction to the Pandas Library
5 lectures 36:01
Overview
02:59
Series Data Structure (1D)
12:41
DataFrame Data Structure (2D)
04:52
Data Selection in a DataFrame
14:55

Hi,

Please download the provided Jupyter notebook files and open them in the JupyterLab tool. The exercise is divided into several steps correlated to the lectures in this section. Each step includes several questions, and per each question, there is an empty cell to provide your answer. In addition, please use the second Jupyter notebook file which is the solution for the exercise to validate your answers step by step.

Please let me know if you have any questions or remarks about the content of the exercise.

Good Luck!

Idan .G.

Exercise #2 – Pandas Series and DataFrame
00:34
11 lectures 58:34
Overview
01:14
Kaggle and the Titanic Dataset
05:48
06:35
13:06
Preview the DataFrame
08:14
Using Summary Statistics
04:48
Sorting and Ranking
03:13
Filtering
04:53
Grouping
04:52

Hi,

Please download the provided Jupyter notebook files and open them in the JupyterLab tool. The exercise is divided into several steps correlated to the lectures in this section. Each step includes several questions, and per each question, there is an empty cell to provide your answer. In addition, please use the second Jupyter notebook file which is the solution for the exercise to validate your answers step by step.

Please let me know if you have any questions or remarks about the content of the exercise.

Good Luck!

Idan .G.

00:34
+ Data Cleaning and Transformation
9 lectures 40:56
Overview
02:07
Removing Columns or Rows
04:17
Removing Duplicate Rows
08:37
Renaming Column Labels
03:32
Dropping Missing Values
07:29
Filling-in Missing Values
03:32
Creating Dummy Variables
08:16
Exporting Data into Files
02:39

Hi,

Please download the provided Jupyter notebook files and open them in the JupyterLab tool. The exercise is divided into several steps correlated to the lectures in this section. Each step includes several questions, and per each question, there is an empty cell to provide your answer. In addition, please use the second Jupyter notebook file which is the solution for the exercise to validate your answers step by step.

Please let me know if you have any questions or remarks about the content of the exercise.

Good Luck!

Idan .G.

Exercise #4 – Data Cleaning and Transformation
00:27
Requirements
• There are no specific prerequisites for starting this training as it is designed for absolute beginners.
• It is recommended to start with "Machine Learning for Absolute Beginners - Level 1"
Description

Unleash the Power of ML

Machine Learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and ML experts to develop products, features, and projects that will help them unleash the power of machine learning. As a result, a data scientist is one of the top ten wanted jobs worldwide!

Machine Learning for Absolute Beginners

The “Machine Learning for Absolute Beginners” training program is designed for beginners looking to understand the theoretical side of machine learning and to enter the practical side of data science. The training is divided into multiple levels, and each level is covering a group of related topics for continuous step by step learning.

Level 2 - Python and Pandas

The second course, as part of the training program, aims to help you start your practical journey. You will learn the Python fundamentals and the amazing Pandas data science library, including:

• Python syntax for developing data science projects

• Using JupyterLab tool for Jupiter notebooks

• Perform data analysis and exploration

• Perform data cleaning and transformation as a pre-processing step before moving into machine learning algorithms.

Each section has a summary exercise as well as a complete solution to practice new knowledge.

The Game just Started!

Enroll in the training program and start your journey to become a data scientist!

Who this course is for:
• Developers curious about data science projects
• Beginner Data Scientists
• AI Product Managers
• ML Engineers
• AI/ML Consultants