Python : Machine Learning, Deep Learning, Pandas, Matplotlib

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size4.6 GB
  • Uploaded Bytutsnode
  • Downloads185
  • Last checkedJul. 16th '21
  • Date uploadedJul. 13th '21
  • Seeders 26
  • Leechers13
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Infohash : 450417BF89427611F3047077D0CD85302ABC52AA


Description

Hello there,
Python instructors on Udemy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels.

Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. Whether you’re a marketer, video game designer, or programmer, this course is here to help you apply machine learning to your work.

Welcome to the “Python Programming: Machine Learning, Deep Learning | Python” course.

In this course, we will learn what is Deep Learning and how does it work.

This course has suitable for everybody who interested in Machine Learning and Deep Learning concepts in Data Science.

First of all, in this course, we will learn some fundamental stuff of Python and the Numpy library. These are our first steps in our Deep Learning journey. After then we take a little trip to Machine Learning Python history. Then we will arrive at our next stop. Machine Learning in Python Programming. Here we learn the machine learning concepts, machine learning a-z workflow, models and algorithms, and what is neural network concept. After then we arrive at our next stop. Artificial Neural network. And now our journey becomes an adventure. In this adventure we’ll enter the Keras world then we exit the Tensorflow world. Then we’ll try to understand the Convolutional Neural Network concept. But our journey won’t be over. Then we will arrive at Recurrent Neural Network and LTSM. We’ll take a look at them. After a while, we’ll trip to the Transfer Learning concept. And then we arrive at our final destination. Projects in Python Bootcamp. Our play garden. Here we’ll make some interesting machine learning models with the information we’ve learned along our journey.

In this course, we will start from the very beginning and go all the way to the end of “Deep Learning” with examples.

The Logic of Machine Learning such as Machine Learning models and algorithms, Gathering data, Data pre-processing, Training and testing the model etc.

Before we start this course, we will learn which environments we can be used for developing deep learning projects.

During the course you will learn:

Fundamental stuff of Python and its library Numpy
What is the Artificial Intelligence (Ai), Machine Learning, and Deep Learning
History of Machine Learning
Turing Machine and Turing Test
The Logic of Machine Learning such as
Understanding the machine learning models
Machine Learning models and algorithms
Gathering data
Data pre-processing
Choosing the right algorithm and model
Training and testing the model
Evaluation
Artificial Neural Network with these topics
What is ANN
Anatomy of NN
Tensor Operations
The Engine of NN
Keras
Tensorflow
Convolutional Neural Network
Recurrent Neural Network and LTSM
Transfer Learning
Reinforcement Learning

Finally, we will make four different projects to reinforce what we have learned.

Why would you want to take this course?

Our answer is simple: The quality of teaching.

OAK Academy based in London is an online education company. OAK Academy gives education in the field of IT, Software, Design, development in English, Portuguese, Spanish, Turkish and a lot of different language on Udemy platform where it has over 1000 hours of video education lessons. OAK Academy both increase its education series number by publishing new courses, and it makes students aware of all the innovations of already published courses by upgrading.

When you enroll, you will feel the OAK Academy`s seasoned developers expertise. Questions sent by students to our instructors are answered by our instructors within 48 hours at the latest.

Video and Audio Production Quality

All our videos are created/produced as high-quality video and audio to provide you the best learning experience.

You will be,

Seeing clearly
Hearing clearly
Moving through the course without distractions

You’ll also get:

Lifetime Access to The Course
Fast & Friendly Support in the Q&A section
Udemy Certificate of Completion Ready for Download

We offer full support, answering any questions.

If you are ready to learn “Python Programming: Machine Learning, Deep Learning | Python”

Dive in now! See you in the course!
Who this course is for:

Anyone who has programming experience and wants to learn machine learning and deep learning.
Statisticians and mathematicians who want to learn machine learning and deep learning.
Tech geeks who curious with Machine Learning and Deep Learning concept.
Data analysts who want to learn machine learning and deep learning.
If you are one of these, you are in the right place. But please don’t forget. You must know a little bit of coding and scripting.
Anyone who need a job transition

Requirements

Coding skills are a plus
Math skills will boost your understanding
Be able to download and install all the free software and tools needed to practice
A strong work ethic, willingness to learn and plenty of excitement about the back door of the digital world
Just you, your computer and your ambition to get started now!

Last Updated 7/2021

Files:

Python Machine Learning, Deep Learning, Pandas, Matplotlib [TutsNode.com] - Python Machine Learning, Deep Learning, Pandas, Matplotlib 16 Projects
  • 002 Project - 2.mp4 (169.3 MB)
  • 002 Project - 2.en.srt (21.6 KB)
  • 001 Project - 1.en.srt (20.9 KB)
  • 003 Project - 3.en.srt (15.5 KB)
  • 004 Project - 4.en.srt (15.2 KB)
  • 001 Project - 1.mp4 (101.6 MB)
  • 003 Project - 3.mp4 (82.3 MB)
  • 004 Project - 4.mp4 (72.3 MB)
05 Numpy Library
  • 008 Using Numpy in Linear Algebra.mp4.vtx (442.5 KB)
  • 008 Using Numpy in Linear Algebra.en.srt (31.9 KB)
  • 005 Numpy Functions.en.srt (19.9 KB)
  • 008 Using Numpy in Linear Algebra.mp4 (112.2 MB)
  • 007 Numpy Exercises.en.srt (15.4 KB)
  • 003 Array and features.en.srt (11.9 KB)
  • 006 Indexing and Slicing.en.srt (9.0 KB)
  • 009 NumExpr Guide.en.srt (8.0 KB)
  • 001 What is Numpy_.en.srt (7.6 KB)
  • 002 Why Numpy_.en.srt (5.0 KB)
  • 004 Array’s Operators.en.srt (4.4 KB)
  • 005 Numpy Functions.mp4 (78.5 MB)
  • 007 Numpy Exercises.mp4 (74.2 MB)
  • 003 Array and features.mp4 (47.9 MB)
  • 009 NumExpr Guide.mp4 (42.2 MB)
  • 006 Indexing and Slicing.mp4 (40.4 MB)
  • 001 What is Numpy_.mp4 (26.7 MB)
  • 004 Array’s Operators.mp4 (17.6 MB)
  • 002 Why Numpy_.mp4 (13.6 MB)
01 Intro to Deep Learning with Python
  • 002 Project Files and Course Documents.html (1.2 KB)
  • 001 Introduction to Deep Learning with Python.en.srt (5.9 KB)
  • 001 Introduction to Deep Learning with Python.mp4 (14.5 MB)
08 Matplotlib
  • 008 Basic Plots in Matplotlib I.en.srt (31.5 KB)
  • 005 Figure Customization.en.srt (14.5 KB)
  • 004 Figure, Subplot and Axes.en.srt (18.2 KB)
  • 009 Basic Plots in Matplotlib II.en.srt (16.2 KB)
  • 007 Grid, Spines, Ticks.en.srt (8.5 KB)
  • 002 Using Matplotlib.en.srt (7.9 KB)
  • 003 Pyplot – Pylab - Matplotlib.en.srt (7.5 KB)
  • 006 Plot Customization.en.srt (6.9 KB)
  • 001 Data Visualization with Python Masterclass.en.srt (3.7 KB)
  • 008 Basic Plots in Matplotlib I.mp4 (104.4 MB)
  • 004 Figure, Subplot and Axes.mp4 (65.7 MB)
  • 005 Figure Customization.mp4 (59.1 MB)
  • 009 Basic Plots in Matplotlib II.mp4 (51.5 MB)
  • 003 Pyplot – Pylab - Matplotlib.mp4 (26.6 MB)
  • 002 Using Matplotlib.mp4 (26.5 MB)
  • 006 Plot Customization.mp4 (25.8 MB)
  • 007 Grid, Spines, Ticks.mp4 (22.4 MB)
  • 001 Data Visualization with Python Masterclass.mp4 (17.9 MB)
17 BONUS
  • 001 BONUS.html (30.2 KB)
11 Machine Learning
  • 012 Unsupervised Machine Learning Methods.en.srt (27.6 KB)
  • 011 Supervised Machine Learning Methods - 4.en.srt (19.5 KB)
  • 015 Choosing the right algorithm and model.mp4 (149.0 MB)
  • 007 Machine Learning Methods.en.srt (16.6 KB)
  • 010 Supervised Machine Learning Methods - 3.en.srt (16.4 KB)
  • 009 Supervised Machine Learning Methods - 2.en.srt (15.8 KB)
  • 005 Learning representations from data.en.srt (13.9 KB)
  • 003 Turing Machine and Turing Test.en.srt (13.8 KB)
  • 006 Workflow of Machine Learning.en.srt (11.1 KB)
  • 008 Supervised Machine Learning Methods - 1.en.srt (10.8 KB)
  • 015 Choosing the right algorithm and model.en.srt (9.2 KB)
  • 002 History of Machine Learning.en.srt (8.1 KB)
  • 017 Evaluation.en.srt (7.7 KB)
  • 004 What is Deep Learning.en.srt (7.4 KB)
  • 014 Data pre-processing.en.srt (6.5 KB)
  • 016 Training and testing the model.en.srt (6.5 KB)
  • 013 Gathering data.en.srt (5.9 KB)
  • 001 AI, Machine Learning and Deep Learning.en.srt (5.7 KB)
  • 012 Unsupervised Machine Learning Methods.mp4 (87.9 MB)
  • 016 Training and testing the model.mp4 (83.9 MB)
  • 011 Supervised Machine Learning Methods - 4.mp4 (70.3 MB)
  • 010 Supervised Machine Learning Methods - 3.mp4 (56.1 MB)
  • 009 Supervised Machine Learning Methods - 2.mp4 (55.4 MB)
  • 007 Machine Learning Methods.mp4 (45.5 MB)
  • 003 Turing Machine and Turing Test.mp4 (40.9 MB)
  • 005 Learning representations from data.mp4 (34.9 MB)
  • 006 Workflow of Machine Learning.mp4 (31.7 MB)
  • 008 Supervised Machine Learning Methods - 1.mp4 (30.9 MB)
  • 014 Data pre-processing.mp4 (26.0 MB)
  • 017 Evaluation.mp4 (24.4 MB)
  • 002 History of Machine Learning.mp4 (23.9 MB)
  • 004 What is Deep Learning.mp4 (20.5 MB)
  • 013 Gathering data.mp4 (17.6 MB)
  • 001 AI, Machine Learning and Deep Learning.mp4 (16.2 MB)
07 Data Frame with Pandas
  • 006 Missing Data and Data Munging Part I.en.srt (22.9 KB)
  • 010 Combining Data Frames Part – II.en.srt (18.1 KB)
  • 009 Combining Data Frames Part – I.en.srt (18.0 KB)
  • 008 Dealing with Missing Data.en.srt (16.3 KB)
  • 001 Data Frame Attributes and Methods Part – I.en.srt (16.2 KB)
  • 005 Groupby Operations.en.srt (12.8 KB)
  • 004 Multi index.en.srt (12.5 KB)
  • 011 Work with Dataset Files.en.srt (12.1 KB)
  • 002 Data Frame attributes and Methods Part – II.en.srt (11.9 KB)
  • 007 Missing Data and Data Munging Part II.en.srt (11.2 KB)
  • 003 Data Frame attributes and Methods Part – III.en.srt (9.8 KB)
  • 009 Combining Data Frames Part – I.mp4 (103.6 MB)
  • 010 Combining Data Frames Part – II.mp4 (84.2 MB)
  • 001 Data Frame Attributes and Methods Part – I.mp4 (79.8 MB)
  • 006 Missing Data and Data Munging Part I.mp4 (79.3 MB)
  • 011 Work with Dataset Files.mp4 (70.7 MB)
  • 008 Dealing with Missing Data.mp4 (69.2 MB)
  • 002 Data Frame attributes and Methods Part – II.mp4 (57.0 MB)
  • 005 Groupby Operations.mp4 (52.6 MB)
  • 004 Multi index.mp4 (50.8 MB)
  • 003 Data Frame attributes and Methods Part – III.mp4 (48.1 MB)
  • 007 Missing Data and Data Munging Part II.mp4 (40.8 MB)

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