Udemy

Complete Data Science & Machine Learning Bootcamp in Python

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  • 899 Students
  • Updated 10/2022
  • Certificate Available
4.2
(83 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
18 Hour(s) 4 Minute(s)
Language
English
Taught by
Derrick Mwiti, Namespace Labs
Certificate
  • Available
  • *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
4.2
(83 Ratings)
3 views

Course Overview

Complete Data Science & Machine Learning Bootcamp in Python

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

Obtain skills in one of the most sort after fields of this century

In 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 course

  • The Data Science Process

  • Python for Data Science

  • 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

  • Power BI Desktop

  • Google Data Studio

  • Association Rule Mining - Apriori

  • Deep Learning

  • Apache Spark for Handling Big Data

For the machine learning section here are some items we'll cover :

  • How Algorithms Work

  • Advantages & Disadvantages of Various Algorithms

  • Feature Importances

  • Metrics

  • Cross-Validation

  • Fighting Overfitting

  • Hyperparameter Tuning

  • Handling Imbalanced Data

  • TensorFlow & Keras

  • Automated Machine Learning(AutoML)

  • Natural Language Processing

The 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.


The 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.


The 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.

Course Content

  • 32 section(s)
  • 365 lecture(s)
  • Section 1 Introduction
  • Section 2 Understand Python for Data Science
  • Section 3 Package Management
  • Section 4 NumPy for Numerical Computation
  • Section 5 Manipulate Data using Pandas
  • Section 6 Descriptive Statistics
  • Section 7 Pandas Project Solutions
  • Section 8 Data Visualization Guide
  • Section 9 Data Visualization in Matplotlib
  • Section 10 Data Visualization in Seaborn - Categorical Plots
  • Section 11 Data Visualization in Seaborn - Visualizing Distributions
  • Section 12 Seaborn with Matplotlib Subplots
  • Section 13 Matrix Visualization in Seaborn
  • Section 14 Visualize Linear Relationships in Seaborn
  • Section 15 Seaborn Multi-Plot Grids
  • Section 16 Word Cloud
  • Section 17 Seaborn & Word Cloud - Exercise and Solutions
  • Section 18 Build Interactive Visuals with Plotly
  • Section 19 Interactive Web Applications with Dash
  • Section 20 Building Data Science Applications with Streamlit
  • Section 21 Building Dashboards in Power BI Desktop
  • Section 22 Data Visualization with Google Data Studio
  • Section 23 Supervised Machine Learning
  • Section 24 K-Means - Unsupervised Machine Learning
  • Section 25 Feature Ranking with Recursive Feature Elimination
  • Section 26 Association Rule Mining - Apriori
  • Section 27 Natural Language Processing
  • Section 28 Deep Learning with Keras and TensorFlow
  • Section 29 Automated Machine Learning
  • Section 30 Apache Spark
  • Section 31 Book Section - Get My Paid Books for Free
  • Section 32 Outro - Congratulations

What You’ll 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


Reviews

  • J
    Janusz Posluszny
    1.0

    The voice quality is horrible

  • A
    Adamu Mohammed-Kabiru Atodo
    5.0

    great

  • E
    Elizabeth Ssebuliba
    5.0

    Explained well, ample available resources both for reading and practicing. Very hands on. It’s very essential.

  • P
    Pauline Wambeti Kamwengu
    5.0

    very good

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