
Learn Python basics, pandas, and matplotlib for data analysis and visualization, then build interactive web apps with dash and streamlit and tackle supervised machine learning.
Learn how to Install Anaconda
Understand the Data Science Process
Lead your Udemy review by engaging with the material, reviewing videos and code, and practicing with cargo and USA machine learning datasets and cargo competitions before advancing to deep learning.
Understand Python for Data Science
Launch Jupyter Notebook on Linux
Launch Jupyter Notebook on Windows
Explore the folder structure, access installation guides and Python for data science resources, and practice with notebooks, project exercises, and solutions—remember to start the project exercise before viewing the solution.
Learn Python Operations and Comments
Understand Python Data Types
Learn Python Lists
Learn List Negative Indexing
Learn Dictionaries
Understand Python Tuples
Learn Python Sets
Understand Python Boolean Types
Understand Conditional Statements
Create Python Functions
Python for Loops
Python while loops
Using the Python Map Function
Understand the Python Range Function
Practice your Python Skills
Python Exercise Solutions
Explore the process of installing Python packages, including those not shipped with Anaconda. Learn how to add and manage dependencies to support data science and machine learning projects.
Understand Python Pip and Virtual Env
Set up Pip and virtual environment in Python
Learn how to install packages using the Anaconda Navigator
Explore numerical computation in section four of the complete data science and machine learning bootcamp in Python, and receive an introduction to core numerical methods.
Introduction NumPy for Numerical Computation
Numpy Zeros,ones, and linspace
Checking Documentation in Jupyter Notebooks
Indexing one dimension arrays
Indexing Multi-dimensional Array
Broadcasting in NumPy
Operations in NumPy
NumPy Practice Exercises
Learn to use pandas for data manipulation in Python, the tool for analysis and grouping by, as a Python alternative to Excel.
Introduction to Pandas for Data Manipulation
DataFrames in Pandas
Resetting the Index in Pandas
Deleting Columns in Pandas
Learn how to deal with null values in Pandas
How to create new columns in Pandas
Selecting Data in Pandas
Grouping Data in Pandas
Exporting a Pandas DataFrame
Loading Datasets in Pandas
Working with Excel-like Pivot tables in Pandas
Practice your Pandas skills
Import pandas and load the dataset, inspect with head and describe, review the data frame info, and note how dropna affects records while identifying null values and filling them.
Explore data handling in Python by examining regions and columns, using square bracket notation to extract data, and understanding how changes in data affect the region and related values.
Learn to extract month and day names from a date column in pandas by creating month and day columns using datetime index and name functions.
Select the country column and apply the unique function to reveal 80 unique country names, then group by sector to count loan amounts by sector and rename the id column.
Merge related data frames on the id column, inspect the Diem dataset, and compute unique loan theme types using group by and counts to identify the most prominent themes.
Begin data visualization with matplotlib in this section, and recognize that Seabourne, built on top of matplotlib, will be covered in the next section.
Matplotlib Vertical Barplots
Matplotlib Horizontal Barplots
Create Matplotlib Scatterplots
Matplotlib Histogram
Group data by sector and sum the funded amounts to build a pie chart with labeled sectors, identify 15 unique sectors, explode a slice, and render a 15 by 15 figure.
Learn Matplotlib Line Plot
Understand Matplotlib Subplots
Learn Matplotlib Figure & Axes
Create and place four plots in a 2 by 2 grid using matplotlib subplots, assigning each graph to its row and column by indexing.
Practice your matplotlib skills
Seaborn Count Plot
Seaborn Violin Plot
Learn Seaborn - Adding Hue
Understand Seaborn Strip plot
Create a Swarm plot
Order the X values in Seaborn
Use hue with a Strip plot
Create a Boxplot
Create a seaborn Boxen Plot
Create a Barplot in Seaborn
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.