Description

Build Projects with Machine Learning, Text Classification, TensorFlowNumPy, PyPlot, Pandas, and More in Google Colab

Learn everything you need to become a data scientist.

Enroll now to learn everything you need to know to get up to speed, whether you’re a developer or aspiring data scientist. This is the course for you.

Your complete Python course for image recognition, data analysis, data visualization and more.

Don’t miss the biggest Python course of the year. This is a once in a lifetime chance to enroll in a massive course.

Absolutely no experience necessary. Start with a complete introduction to Python that is perfect for absolute beginners and can also be used a review.

Jump into using the most popular libraries and frameworks for working with Python. You’ll learn everything you need to become a data scientist. This includes:

• Python Crash Course for Beginners

Learn Python with project based examples. Get up and running even if you have no programming experience. Superboost your career by masterig the core Python fundamentals.

• Data Science with NumPy

Build projects with NumPy, the #1 Python library for data science providing arrays and matrices.

• Data Analysis with Pandas

Build projects with pandas, a software library written for the Python programming language for data manipulation and analysis.

• Data Visualization with PyPlot

Build projects with pyplot, a MATLAB-like plotting framework enabling you to create a figure, create a plotting area in a figure, plot lines in a plotting area, decorate the plot with labels and much more. Learn it all in this massive course.

• Machine Learning Theory

Machine learning is in high demand and is quickly becoming a requirement on every software engineer’s resume. Learn how to solve problems with machine learning before diving into practical examples.

• Introduction to TensorFlow

Build projects with TensorFlow, the most popular platform enabling ML developers to build and deploy machine learning applications such as neural networks. Build your first linear regression model with TensorFlow. Learn how to build a dataset, model, train and test!

• Build a Sentiment Analysis Model to Classify Reviews as Positive or Negative

All source code is included for each project.

If you buy one course this year, this is it.

• 1. Python Language Basics
  • 00. Introduction

    4:48

  • 01. Intro To Python

    5:46

  • 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

• 2. Numpy 2020
  • 00. Course Intro | 2020 Numpy

    5:11

  • 01. Intro To Numpy | 2020 Numpy

    6:21

  • 02. Installing Numpy

    3:59

  • 03. Creating Numpy Arrays

    16:56

  • 04. Creating Numpy Matrices

    11:57

  • 05. Getting And Setting Numpy Elements | 2020 Numpy

    17:00

  • 06. Arithmetic Operations On Numpy Arrays | 2020 Numpy

    11:56

  • 07. Numpy Functions Part 1 | 2020 Numpy

    19:13

  • 08. Numpy Functions Part 2 | 2020 Numpy

    12:36

  • 09. Summary And Outro | 2020 Numpy

    3:02

• 3. Pandas 2020
  • 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

• 4. PyPlot 2020
  • 00. Course Intro | 2020 Pyplot

    5:30

  • 01. Intro To Pyplot | 2020 Pyplot

    5:11

  • 02. Installing Matplotlib | 2020 Pyplot

    5:52

  • 03. Basic Line Plot | 2020 Pyplot

    7:53

  • 04. Customizing Graphs | 2020 Pyplot

    10:47

  • 05. Plotting Multiple Datasets | 2020 Pyplot

    8:10

  • 06. Bar Chart | 2020 Pyplot

    6:26

  • 07. Pie Chart | 2020 Pyplot

    9:14

  • 08. Histogram | 2020 Pyplot

    10:14

  • 09. 3D Plotting | 2020 Pyplot

    6:29

  • 10. Course Outro | 2020 Pyplot

    4:10

• 5. Machine Learning Theory 2020
  • 00. Course Intro | 2020 ML Theory

    6:11

  • 01. Quick Intro To Machine Learning | 2020 ML Theory

    9:01

  • 02. Deep Dive Into Machine Learning | 2020 ML Theory

    6:02

  • 03. Problems Solved With Machine Learning Part 1 | 2020 ML Theory

    13:27

  • 04. Problems Solved With Machine Learning Part 2 | 2020 ML Theory

    16:26

  • 05. Types Of Machine Learning | 2020 ML Theory

    10:15

  • 06. How Machine Learning Works | 2020 ML Theory

    11:41

  • 07. Common Machine Learning Structures | 2020 ML Theory

    13:52

  • 08. Steps To Build A Machine Learning Program | 2020 ML Theory

    16:35

  • 09. Summary And Outro | 2020 ML Theory

    2:50

• 6. Intro to Tensorflow 2020
  • 00. Course Intro | 2020 Intro to Tensorflow

    6:10

  • 01. Intro To Tensorflow | 2020 Intro to Tensorflow

    5:33

  • 02. Installing Tensorflow | 2020 Intro to Tensorflow

    3:53

  • 03. Intro To Linear Regression | 2020 Intro to Tensorflow

    9:26

  • 04. Linear Regression Model - Creating Dataset | 2020 Intro to Tensorflow

    5:49

  • 05. Linear Regression Model - Building The Model | 2020 Intro to Tensorflow

    7:23

  • 06. Linear Regression Model - Creating A Loss Function | 2020 Intro to Tensorflow

    5:57

  • 07. Linear Regression Model - Training The Model | 2020 Intro to Tensorflow

    12:43

  • 08. Linear Regression Model - Testing The Model | 2020 Intro to Tensorflow

    5:23

• 7. Review Sentiment Analysis
  • 00. Course Intro | 2020 Review Sentiment Analysis

    6:19

  • 01. How Machines Interpret Text | 2020 Review Sentiment Analysis

    15:24

  • 02. Building The Model Part 1 - Examining Dataset | 2020 Review Sentiment Analysis

    12:28

  • 03. Building The Model Part 2 - Formatting Dataset | 2020 Review Sentiment Analysis

    15:14

  • 04. Building The Model Part 3 - Building The Model | 2020 Review Sentiment Analysis

    10:31

  • 05. Building The Model Part 4 - Training The Model | 2020 Review Sentiment Analysis

    5:42

  • 06. Building The Model Part 5 - Testing The Model.Mp4 | 2020 Review Sentiment Analysis

    9:26

  • 07. Course Summary And Outro | 2020 Review Sentiment Analysis

    3:30