Udemy

Complete Data Science & Machine Learning Bootcamp in Python

立即報名
  • 899 名學生
  • 更新於 10/2022
  • 可獲發證書
4.2
(83 個評分)
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課程資料

報名日期
全年招生
課程級別
學習模式
修業期
18 小時 4 分鐘
教學語言
英語
授課導師
Derrick Mwiti, Namespace Labs
證書
  • 可獲發
  • *證書的發放與分配,依課程提供者的政策及安排而定。
評分
4.2
(83 個評分)
2次瀏覽

課程簡介

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.

課程章節

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

課程內容

  • 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


評價

  • 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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