The Data Science & Machine Learning Bootcamp in Python
Learn Python for Data Science, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, Dask, LightGBM, XGBoost, CatBoost and more
Development,Data Science,Bootcamp
Lectures -248
Resources -8
Duration -11 hours
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Course 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
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
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
Goals
- Get acquainted with Python for Data Science
- Understand the Data Science Process
- Perform Numerical Computation with NumPy
- Manipulate Data using Pandas
- Visualize using Matplotlib
- Build interactive visuals with Plotly
- Perform Statistical Analysis
- Build beautiful visuals using Seaborn
- Implement Machine Learning Models
- Load in Big Data using Dask
- Handle Imbalanced Data
- Understand the Intuition Behind Popular Machine Learning Algorithms
- Implement Cross-Validation to improve model performance
- Search for the best model parameters using Grid Search CV
- Use experimental algorithms from Scikit-Learn
- Solve Time Series Problems using Prophet
- Predict Price of a Commodity using Linear Regression
- Host a Machine Learning Model on Heroku
- Build classification and regression models using LightGBM
- Implement classification and regression models using XGBoost
- Build classification and regression models using CatBoost
- Classify data using Logistic Regression
- Build models using Decision Trees & Random Forests
- Perform customer segmentation using KMeans Clustering
- Solve problems using Support Vector Machines
- ...and much more
Prerequisites
- A great sense of curiosity!
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Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
5 Lectures
-
Welcome 01:20 01:20
-
Assignment: Introduce Yourself
-
Install Anaconda 01:01 01:01
-
Understand the Data Science Process 02:12 02:12
-
About Reviews
Understand Python for Data Science
20 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Manage Packages in Python
3 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Perform Numerical Computation with NumPy
9 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Manipulate Data with Pandas
12 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Pandas Project Solutions
7 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Data Visualization in Matplotlib
10 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Data Visualization in Seaborn - Categorical Plots
10 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Data Visualization in Seaborn - Visualizing Distributions
5 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Seaborn with Matplotlib Subplots
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Matrix Visualization in Seaborn
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Visualize Linear Relationships in Seaborn
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Seaborn Multi-Plot Grids
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Word Cloud
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Seaborn & Word Cloud Exercise and Solutions
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Build Interactive Visuals with Plotly
10 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Building Data Science Applications with Streamlit
6 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Building Dashboards in Power BI Desktop
27 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Supervised Machine Learning
60 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
K-Means - Unsupervised Machine Learning
10 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Feature Ranking with Recursive Feature Elimination
8 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Association Rule Mining - Apriori
4 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Natural Language Processing
16 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Deep Learning & Next Steps
11 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Automated Machine Learning
4 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
File Resources
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Instructor Details
![Derrick Mwiti](https://www.tutorialspoint.com/assets/profiles/235614/profile/200_48334-1598516933.jpg)
Derrick Mwiti
Derrick Mwiti is a data scientist who has a great passion for sharing knowledge. He is an avid contributor to the data science community.
Experienced in data science, machine learning, and deep learning with a keen eye for building machine learning communities.
Derrick works as a machine learning developer advocate, where he helps companies build products that developers want. It involves getting feedback to the companies as well as getting feedback to developers.
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