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Machine Learning & Data Science A-Z: Hands-on Python 2024
Rating: 4.2 out of 5(1,251 ratings)
69,362 students

Machine Learning & Data Science A-Z: Hands-on Python 2024

Learn NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, Scipy and develop Machine Learning Models in Python
Created byNavid Shirzadi, PhD, P.Eng.
Last updated 5/2024
English

What you'll learn

  • Understanding the basic concepts
  • Complete tutorial about basic packages like Numpy and Pandas
  • Data Visualization
  • Data Preprocessing
  • Understanding the concept behind the algorithms
  • Developing different kinds of Machine Learning models
  • Knowing how to optimize your models' hyperparameters
  • Learn how to develop models based on the requirement of your future business

Course content

9 sections • 76 lectures • 14h 27m total length

  • Course Content5:27
  • What is Machine Learning? Some Basic Terms6:16
  • Python Installation0:18
  • Python IDE2:24
  • IDE Installation2:49
  • Installation of Required Libraries7:31

    Hi,

    In this session, we are going to install the required libraries that we need during the course.

    We are going to use pip installation. You can find the related codes for this installation below:


    pip install numpy

    pip install pandas

    pip install -U scikit_learn

    pip install scipy


    Note1! If you receive an error with the above commands, just google the pip install (name of the package) and you should see the last updated pip installation commands. IT'S VERY EASY :)


    Note2! If you are using anaconda distribution, please use the below commands


    conda install numpy

    conda install pandas

    conda install -c conda-forge scikit-learn

    conda install -c anaconda scipy




  • Spyder Interface7:00

    Hi,


    In case you do not have Python on your computer as we discussed, please go to the below link and download any version of the Python that you prefer and then select it as an interpreter as we discussed in the video:


    https://www.python.org/downloads/


    Thanks

Requirements

  • Python's basic syntax

Description

Are you interested in data science and machine learning, but you don't have any background, and you find the concepts confusing?

Are you interested in programming in Python, but you always afraid of coding?

I think this course is for you!

Even if you are familiar with machine learning, this course can help you to review all the techniques and understand the concept behind each term.

This course is completely categorized, and we don't start from the middle! We actually start from the concept of every term, and then we try to implement it in Python step by step. The structure of the course is as follows:

Chapter1: Introduction and all required installations

Chapter2: Useful Machine Learning libraries (NumPy, Pandas & Matplotlib)

Chapter3: Preprocessing

Chapter4: Machine Learning Types

Chapter5: Supervised Learning: Classification

Chapter6: Supervised Learning: Regression

Chapter7: Unsupervised Learning: Clustering

Chapter8: Model Tuning

Furthermore, you learn how to work with different real datasets and use them for developing your models. All the Python code templates that we write during the course together are available, and you can download them with the resource button of each section.

Remember! That this course is created for you with any background as all the concepts will be explained from the basics! Also, the programming in Python will be explained from the basic coding, and you just need to know the syntax of Python.

Who this course is for:

  • Anyone with any background that interested in Data Science and Machine Learning with at least high school knowledge in mathematic
  • Beginners, intermediate and even advanced students in the field of artificial intelligence, Data Science and Machine Learning
  • Students in college that looking for securing their future jobs
  • Employees that look forward to excel their job level by learning machine learning
  • Anyone who afraid of coding in Python but interested in Machine Learning Concepts
  • Any one who wants to create a new business using machine learning
  • Graduate students and researchers that want to apply machine learning models in their thesis and projects

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