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Complete Data Science & Machine Learning Bootcamp in Python
900 students
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
Created byDerrick Mwiti, Namespace Labs
Last updated 10/2022
English
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
Explore related topics
Course content
32 sections • 365 lectures • 18h 4m total length
Requirements
- No prior programming experience required. We'll start from the basics
Description
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.
Who this course is for:
- People who would like to get started with data science and machine learning without any prior knowledge
- Persons who would like to refresh their data science and machine learning knowledge