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The Data Science & Machine Learning Bootcamp in Python

Posted by: tuongyda14 on 24-09-2020, 03:08
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The Data Science & Machine Learning Bootcamp in Python
The Data Science & Machine Learning Bootcamp in Python
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 277 lectures (12h 5m) | Size: 4.94 GB


Learn Python,NumPy,Pandas,Matplotlib,Seaborn,Scikit-learn,Dask,LightGBM,XGBoost,CatBoost,Streamlit,Power BI & much more 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
How Artificial Neural Networks Work
How Artificial Neural Networks Learn
Loss Functions used in Artificial Neural Networks
Activation Functions used in Artificial Neural Networks
Cost Functions used in Artificial Neural Networks
Optimizer Functions used in Artificial Neural Networks
What Backpropagation is
Different Types of Gradient Descent
How to Choose an Activation Function
Preparing your Data for Deep Learning Models
Monitoring Loss Functions
Monitoring Model Metrics
Use of CallBacks in Deep Learning
Fighting overfitting in TensorFlow
Convolutional Neural Networks
Natural Language Processing
Support Vector Machines
KNearest Neighbors
T-Test
Chi-square Test
K-Means Clustering
Principal Component Analysis
Flask
Association Rule Mining - Apriori
Building Dashboards in Power BI

Requirements
A great sense of curiosity!

Description
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 the items we'll 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

Association Rule Mining - Apriori

Deep Learning & Next Steps

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

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


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