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UdemyMachine LearningPaid courseAll LevelsCertificate

Complete Data Science & Machine Learning Bootcamp in Python (Udemy.com)

Learn Python,NumPy,Pandas,Matplotlib,Seaborn,Scikit-learn,Dask,LightGBM,XGBoost,CatBoost,Streamlit,Power BI & much more

Created by: Derrick Mwiti

Last updated October 2022

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What you will learn

  • Python for Data Science
  • The Data Science Process
  • NumPy for Numerical Computation
  • Pandas for Data Manipulation
  • Matplotlib for Visualization
  • Seaborn for Beautiful Visuals
  • Plotly for Interactive Visuals
  • Introduction to Machine Learning
  • Dask for Big Data
  • LightGBM
  • XGBoost
  • CatBoost
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • Deep Learning using Keras and TensorFlow
  • Artificial Neural Networks
  • Convolutional Neural Networks
  • Natural Language Processing
  • Support Vector Machines
  • KNearest Neighbors
  • Statistical Testing
  • K-Means Clustering
  • Principal Component Analysis
  • Association Rule Mining - Apriori
  • Building Dashboards in Power BI
  • Data Science Applications with Dash
  • Apache Spark in Python
  • Google Data Studio

How it compares

Against the other 155 rated Udemy courses in Machine Learning on CourseDuck:

  • Rating4.1 out of 5higher than 20% of them
  • Reviews85#128 of 156 by number of reviews
  • Length18.1 hourslonger than 80% of them
  • Last updatedOctober 2022earlier than 50% of them

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Course Description

Obtain skills in one of the most sort after fields of this centuryIn this course, you'll learn how to get started in data science. You don't need any prior knowledge in programming. We'll teach you the Python basics you need to get started. Here are some of the items we will cover in this courseThe Data Science ProcessPython for Data ScienceNumPy for Numerical ComputationPandas for Data ManipulationMatplotlib for VisualizationSeaborn for Beautiful VisualsPlotly for Interactive VisualsIntroduction to Machine LearningDask for Big DataPower BI DesktopGoogle Data StudioAssociation Rule Mining - AprioriDeep Learning Apache Spark for Handling Big DataFor the machine learning section here are some items we'll cover :
How Algorithms WorkAdvantages & Disadvantages of Various AlgorithmsFeature ImportancesMetricsCross-ValidationFighting OverfittingHyperparameter TuningHandling Imbalanced DataTensorFlow & KerasAutomated Machine Learning(AutoML)Natural Language ProcessingThe course also contains exercises and solutions that will help you practice what you have learned. By enrolling in this course, you'll have lifetime access to the videos and Notebooks. Purchasing the course also comes with a 30-day money-back guarantee, so you can try it at no risk at all. Let's now add Data Science, Machine Learning, and Deep Learning to your CV. See you inside the course. Who this course is for:
People who would like to get started with data science and machine learning without any prior knowledgePersons who would like to refresh their data science and machine learning knowledge

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Instructor Details

Derrick Mwiti

Derrick Mwiti is a data scientist who has a great passion for sharing knowledge. He is an avid contributor to the data science community via blogs such as Heartbeat, Towards Data Science, Datacamp, Neptune AI, KDNuggets just to mention a few. Derrick is also an author and online instructor. He also trains and works with various institutions to implement data science solutions as well as to upskill their staff.
Derricks studied Mathematics and Computer Science from the Multimedia University, he also is an alumnus of the Meltwater Entrepreneurial School of Technology.
When Derrick is not staring at his computer, he enjoys reading and traveling

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Reviews

4.1

85 ratings on Udemy

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By Hippolyte KENGNI on 12/13/2020

I've not yet completed the course but what I can say so far is that the course is well structured and I have found that the trainer knows what he is talking about. Nevertheless, I think that in the requirements, it should have been mentioned that the learner should have notions of programming (programming logic) and descriptive statistics. In the visualisation section, the trainer does not explain the result or the graph obtained, which suggests that the learner should be able to interpret a graph...

By Kev Kim on 12/6/2020

Great

By Stefano Ciotti on 10/21/2020

I found a really complete landscape of the data science approach. The examples until now I saw are simple, clear, concise, and understandable. Great job.

By Ramy Kaddo on 10/5/2020

Great course, everything is well explained and it is easy to follow the instructor

By Dennis Chang'ach on 9/22/2020

The course is well structured. I found it very helpful when I was preparing for a coding interview.

By Mallory Simpson on 9/3/2020

The parts are short and to the point.

By Maurice Obala on 8/14/2020

Concise and easy to follow!

By Patrick Nzomo on 7/22/2020

The course is an amazing guide for beginners in Data Science. It is easy to follow through and understand. I unreservedly recommend it for all beginners and any person who has an interest in Data Science. Great work Mr. Derrick Mwiti.

By Betsy Naa Abeka Nunoo on 7/19/2020

I am new to this and but its been good so far. Its been an in sighting journey so far.

By Dennis Kevogo on 7/1/2020

This is an amazing course on a world class level, I would recommend anyone who would want to start or advance their careers in Data Science.

Showing all 10 reviews on CourseDuck

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Overall Score : 82 / 100