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Skillshare

Data Science and Machine Learning with Python – Intermediate Hands-On Coding

via Skillshare

Overview

Are you ready to learn the skills that are in high demand across all industries?

Machine Learning and data science are essential for making informed decisions in today's business world. And the best way to learn these skills is through the power of Python.

Follow the free code resource book on: Data-Science-Gui.de (no e-mail)

Python is the go-to language for data science, and in this course, we will dive deep into its capabilities. This class is designed for both beginners and intermediate students, so don't worry if you're new to programming. This class assumes some Python knowledge, but if you'd prefer a high-level introduction without programming application to data science, I have another class: The No-Code Data Science Master Class.

We will start by covering the basics of Python syntax and then move on to the full data science workflow, including:

  • Loading data from files and databases
  • Cleaning and preparing data for analysis
  • Exploring and understanding the data
  • Building and evaluating machine learning models
  • Analyzing customer churn and validating models
  • Visualizing data and creating reports

We will be using popular and freely available Python libraries such as Jupyter, NumPy, SciPy, Pandas, MatPlotLib, Seaborn, and Scikit-Learn.

By the end of this class, you will not only have a solid understanding of data science and analytics but also be able to quickly learn new libraries and tools. So, don't wait any longer and enroll in this Python Data Science Master Class today!

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Who am I?

Jesper Dramsch is a machine learning researcher working between physical data and deep learning.

I am trained as a geophysicist and shifted into Python programming, data science and machine learning research during work towards a PhD. During that time I created educational notebooks on the machine learning contest website Kaggle (part of Alphabet/Google) and reached rank 81 worldwide. My top notebook has been viewed over 64,000 times at this point. Additionally, I have taught Python, machine learning and data science across the world in companies including Shell, the UK government, universities and several mid-sized companies. As a little pick-me-up in 2020, I have finished the IBM Data Science certification in under 48h.

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Other Useful Links:

My website & blog - https://dramsch.net
The weekly newsletter - https://dramsch.net/newsletter

Twitter - https://twitter.com/jesperdramsch
Linkedin - https://linkedin.com/in/mlds

Youtube - https://www.dramsch.net/youtube
Camera gear - https://www.dramsch.net/r/gear

Syllabus

  • Introduction to Data Science with Python
  • Class Project
  • What is Data Science?
  • Tool Overview
  • How To Find Help
  • | Data Loading |
  • Loading Excel and CSV files
  • Loading Data from SQL
  • Loading Any Data File
  • Dealing with Huge Data
  • Combining Multiple Data Sources
  • | Data Cleaning |
  • Dealing with Missing Data
  • Scaling and Binning Numerical Data
  • Validating Data with Schemas
  • Encoding Categorical Data
  • | Exploratory Data Analysis |
  • Visual Data Exploration
  • Descriptive Statistics
  • Dividing Data into Subsets
  • Finding and Understanding Relations in the Data
  • | Machine Learning |
  • Linear Regression for Price Prediction
  • Decision Trees and Random Forests
  • Machine Learning Classification
  • Data Clustering for Deeper Insights
  • Validation of Machine Learning Models
  • Machine Learning Interpretability
  • Intro to Machine Learning Fairness
  • | Visuals & Reports |
  • Visualization Basics
  • 52 Geospatial new
  • Exporting Data and Visualizations
  • Creating Presentations directly in Jupyter
  • Generating PDF Reports from Jupyter
  • Conclusion and Congratulations!

Taught by

Jesper Dramsch

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