Description

Complete Python & Data Science Course for Absolute Beginners 🚀📊

Do you want to learn:
• 🛣️ How to teach a self-driving car to navigate a highway?
• 🎥 How to detect objects, emotions, and colors in videos?
• 🖼️ How to restore images with code?
• 🌟 Build the next big machine-learning app!

Learn how to:
• 🏗️ Build machine learning projects
• 📈 Add machine learning and data science to your resume

This bundle:
• ❌ Does not assume any level of experience
• 👶 Is perfect for beginners
• 📂 THE COMPLETE SOURCE CODE WILL BE AVAILABLE.

No math or programming experience necessary.
• 🐍 Learn how to code in Python.
• 🛠️ Build and run your first Python projects.
• 💡 Think like a Python developer.
Learn how to use popular Python libraries:
• NumPy 📐 - fundamental package for scientific computing in Python
• Matplotlib’s Pyplot 📊 - data visualization with plots, graphs, and charts
• Pandas 🐼 - fast, powerful, flexible, and easy-to-use data analysis and manipulation tool

Learn machine learning and artificial intelligence from scratch.
• 🧠 Learn how machine learning can solve problems in all disciplines.
• 💻 Learn how to build a machine-learning program.
• 🚀 Take your skills to the next level by building a huge range of models.
• 📊 Build regression and classification models
• 🤖 Build artificial intelligence search algorithms
• 📂 Build a full portfolio with practical machine-learning projects.
• 🧪 Use Tensorflow 2.0 and Keras to build fun beginner projects.
• 📸 Classify images, species of plants, and more.
• 🔍 Dive into deep learning and master highly desirable skills.
• 📈 Add projects to your resume in no time.
• 🎓 Learn a hireable skill and powerful technology
• 📊 Help businesses find customer trends, leverage data to cut costs, and much more.

Requirements
• ❌ No programming or machine learning experience needed - We’ll teach you everything you need to know.
• 💻 A Mac, PC, or Linux computer.
• 🛠️ We’ll walk you through, step-by-step how to get all the software installed and set up
The only course you need to learn Machine Learning.

• 🌟 With over 50,000 reviews, our courses are some of the HIGHEST RATED courses online!
• 🏆 This masterclass is without a doubt the most comprehensive course available anywhere online. Even if you have zero experience, this course will take you from beginner to professional.

• 1. Python Introduction
  • 00. Introduction

    4:48

  • 01. Intro To Python

    5:46

• 2. Code Python on the Web
  • 02.01 What is Google Colab

    4:25

  • 02.02 What If I Get Errors

    2:40

  • 02.03 How Do I Terminate a Session

    2:40

• 3. Python Language Fundamentals: Learn Python from Scratch
  • 01. Variables

    19:20

  • 02. Type Conversion Examples

    10:07

  • 03. Operators

    28:54

  • 04. Collections

    8:25

  • 05. List Examples

    19:41

  • 06. Tuples Examples

    8:37

  • 07. Dictionaries Examples

    14:27

  • 08. Ranges Examples

    8:32

  • 09. Conditionals

    6:44

  • 10. If Statement Examples

    21:32

  • 11. Loops

    29:42

  • 12. Functions

    17:02

  • 13. Parameters And Return Values Examples

    13:55

  • 14. Classes And Objects

    34:12

  • 15. Inheritance Examples

    17:29

  • 16. Static Members Examples

    11:06

  • 17. Summary And Outro

    4:09

• 4. Data Visualization with Python and Matplotlib
  • 00. Course Intro

    5:31

  • 01. Intro To Pyplot

    5:11

  • 02. Installing Matplotlib

    5:52

  • 03. Basic Line Plot

    7:54

  • 04. Customizing Graphs

    10:47

  • 05. Plotting Multiple Datasets

    8:11

  • 06. Bar Chart

    6:26

  • 07. Pie Chart

    9:14

  • 08. Histogram

    10:15

  • 09. 3D Plotting

    6:29

  • 10. Course Outro

    4:09

• 5. Beginners Data Analysis with Pandas
  • 00. Panda Course Introduction

    5:44

  • 01. Intro To Pandas

    7:55

  • 02. Installing Pandas

    5:28

  • 03. Creating Pandas Series

    20:34

  • 04. Date Ranges

    11:29

  • 05. Getting Elements From Series

    19:21

  • 06. Getting Properties Of Series

    13:04

  • 07. Modifying Series

    19:02

  • 08. Operations On Series

    11:49

  • 09. Creating Pandas Dataframes

    22:57

  • 10. Getting Elements From Dataframes

    25:12

  • 11. Getting Properties From Dataframes

    17:44

  • 12. Dataframe Modification

    36:24

  • 13. Dataframe Operations

    20:10

  • 14 Dataframe Comparisons And Iteration

    15:35

  • 15. Reading CSVs

    12:00

  • 16. Summary And Outro

    4:15

• 6. Data Mining with Python! Real-Life Data Science Exercises
  • Introduction to Data Mining

    9:31

• 7. 2-1 Data Wrangling - A Complete Overview
  • Data Wrangling Demystified

    1:03:56

• 8. 2-2 Data Mining Fundamentals
  • 01. Cluster Analysis

    20:08

  • 02. Classification and Regression

    34:31

  • 03. Association and Correlation

    13:10

  • 04. Dimensionality Reduction

    25:39

• 9. 2-3 Frameworks Explained - Taming Big Data with Spark
  • 01. Apache Spark - An Overview Of The Framework

    26:36

  • 02. Spark Key Functions

    20:27

  • 03. Spark Machine Learning

    7:32

  • 04. EXAMPLES - Using The Machine Learning Pipeline

    6:17

• 10. 2-4 EXAMPLES - Mining and Storing Data
  • 01. Text Mining

    15:06

  • 02. Network Mining

    10:12

  • 03. Matrix

    7:17

  • 04. SQL

    12:36

• 11. 2-5 NLP (Natural Language Processing)
  • 01 NLP Data Cleaning

    6:55

  • 02. Count Vectorizer

    7:58

  • 03. NLP Example with Spam

    9:59

  • 04. Tweak Model with Spam Data

    5:33

  • 05. Pipeline with Spam Data

    4:48

• 12. 2-6 Conclusion and Summary
  • 06. Conclusion and Challenge

    4:40

• 13. PySpark - Build DataFrames with Python, Apache Spark and SQL
  • 00 Project Preview

    2:34

  • 02 What Are Resilient Distributed Datasets

    1:08

  • 01 What Is Apache Spark

    2:37

  • 03A What Is A Dataframe

    1:47

  • 03B What You'll Need

    1:47

• 14. PySpark - Build DataFrames from Spreadsheets
  • 04 Start A Spark Session

    3:48

  • 05 Load Data As A CSV

    6:02

  • 06 Perform Basic Dataframe Operations

    4:02

  • 07 Format Dataframe Table

    5:14

  • 08 Perform Dataframe Math Operations

    7:32

  • 09 Perform Dataframe Queries

    14:23

  • 10 Build SQL Queries With Spark

    7:24

• 15. Python Data Analysis Bootcamp with Pandas and NLTK
  • 00 Project Preview

    3:38

  • 01 Convert CSV File To A Python List

    13:50

  • 02 Tokenize Text Data

    26:25

  • 03 Find Most Popular Lemmatized Words

    11:36

  • 04 Build Dataframes Per Part Of Speech

    3:56

  • 05 Plot Word Frequency

    9:10

• 16. Exploratory Data Analysis Bootcamp with Python, Seaborn and Pandas
  • 00 Project Preview

    4:04

  • 01 Load A Dataset

    9:43

  • 02 Analyze The Main Feature

    2:47

  • 03 Analyze Numerical Features

    7:26

  • 04 Analyze Categorical Features

    9:33

• 17. Visualize - Exploratory Data Analysis Bootcamp with Python, Seaborn and Pandas
  • 01 Find Relationships Between Numerical Features

    11:27

  • 02 Find Relationships Between Categorical Features

    7:52

  • 03 Build Conditional Plots

    7:04

• 18. Overview - Introduction to Databases with Python SQL
  • 00 Course Overview

    2:16

  • 01 What You'll Need

    3:13

• 19. 01 Introduction to data
  • 01 Why You Must Know How To Work With Data

    5:22

• 20. 02 Entity Relationship Modeling (ERM)
  • 01 How To Read An ER Model

    5:32

• 21. 03 Introduction to databases and relational databases
  • 01 What Is A Database

    8:27

  • 02 What Is A Relational Database

    4:33

• 22. 04 How to build an organized database
  • 01 How To Design Columns And Data Types

    3:14

  • 02 Use Normal Forms To Check Your Design

    7:16

• 23. 05 Build a SQLite database with Python
  • 01 Build A Sqlite Database With Python

    8:02

  • 02 Add An Entry To The Table With SQL

    6:45

  • 03 Add More Records To The Table

    6:30

  • 04 Build A Second Table For Cross-Referencing

    10:57

  • 05 Select Rows That Meet Conditions

    7:15

• 24. Feature Analysis and Data Science with Stocks for Beginners
  • Course Overview

    3:51

  • 01 Load And Create Data

    9:55

  • 02 Perform Exploratory Data Analysis

    3:42

  • 03 Visualize Data With Different Plots

    11:07

  • 04 Analyze Features With More Plots

    6:18

  • 05 Build Plots With Seaborn

    4:35

  • 06 Build A Bokeh Plot

    6:17

  • 07 Build A 3D Scatter Plot

    3:46

  • 08 Rank Feature Importance

    7:13

  • 09 Compare Positive And Negative Returns

    8:07

• 25. The Definitive Python Time Series Analysis Masterclass
  • 00 Project Preview

    3:03

  • 01 Load Crypto Prices Dataset

    10:29

  • 02 Visualize Bitcoin Price Trend

    4:54

  • 03 Predict Price With Facebook Prophet

    6:23

  • 04 Analyze Model Performance

    9:38

  • 05 Visualize Model Results

    3:58

  • 06 Predict Monthly Trend

    9:28

  • 07 Predict Weekly Trend

    5:36

  • 08 Compare Final Stock Price Of Different Strategies

    6:23

• 26. 1). Stock Market Data Analysis and Visualization
  • 00 Project Preview

    3:17

  • 01 Fetch Stock Data

    9:13

  • 02 Visualize Stock Data Features

    7:32

  • 03 Calculate Daily Return

    3:28

  • 04 Compare Returns Of Different Stocks

    10:45

  • 05 Compare Closing Prices

    8:48

• 27. 2). Stock Market Data Analysis and Visualization
  • 01 Visualize Standard Deviation And Expected Returns

    5:45

  • 02 Calculate Value At Risk

    3:53

  • 03 Monte Carlo Analysis To Estimate Risk

    9:11

  • 04 Visualize Price Distribution

    9:07

• 28. Scrape the Web - Python and Beautiful Soup Bootcamp
  • 00 What Is Web Scraping

    5:39

  • 01 What You'll Need

    1:30

  • 02 Build An Html Webpage To Scrape

    12:42

  • 03 Select Data Structures From A Webpage

    5:48

  • 04 Extract URLsAnd Text

    5:25

  • 05 Work With Tags

    8:07

  • 06 Work With Attributes

    5:19

  • 07 Add Navigation To A String

    5:29

  • 08 Navigate Html Contents

    7:16

  • 09 Find All Filter

    4:52

• 29. Build Interactive Python Dashboards with Plotly and Dash
  • 01 Project Preview

    1:40

  • 02 What Is Plotly And Dash

    3:59

  • 03 What You'll Need

    2:10

  • 1-01 Build A Dash App

    11:44

  • 1-02 Build A Graph In The Dash App

    12:05

  • 2-01 Load Data From Vega Datasets

    5:34

  • 2-02 Build The Layout

    10:27

  • 2-03 Build A Chart With Altair

    11:56

• 30. Data Mining with Python and NumPy - Build a Video Recommender System
  • 00 Project Preview

    2:48

  • 01 Build A Dataset

    23:45

  • 02 Compute Support And Confidence - If A Person Watches X, They Will Watch Y

    10:06

  • 03 Compute Support And Confidence For All Channels

    14:21

  • 04 Determine Which Videos Are Best To Recommend

    9:58