課程資料
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- *證書的發放與分配,依課程提供者的政策及安排而定。
課程簡介
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
此課程所涵蓋的技能
評價
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JJanusz Posluszny
The voice quality is horrible
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AAdamu Mohammed-Kabiru Atodo
great
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EElizabeth Ssebuliba
Explained well, ample available resources both for reading and practicing. Very hands on. It’s very essential.
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PPauline Wambeti Kamwengu
very good