Complete Data Science Deep Learning R Data Science 2021
What you’ll learn

Python

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.

Fundamental stuff of Python and its library Numpy

What is the AI, Machine Learning and Deep Learning

History of Machine Learning

Turing Machine and Turing Test

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

What is Artificial Neural Network (ANN)

Anatomy of NN

Tensor Operations

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, I am here to help you apply machine learning to your work.

The Engine of NN

Keras

Tensorflow

Convolutional Neural Network

Recurrent Neural Network and LTSM

Transfer Learning

Machine Learning

Deep Learning

Machine Learning with Python

Python Programming

Deep Learning with Python

If you have some programming experience, Python might be the language for you

Learn Fundamentals of Python for effectively using Data Science

Data Manipulation

Learn how to handle with big data

Learn how to manipulate the data

Learn how to produce meaningful outcomes

Learn Fundamentals of Python for effectively using Data Science

Numpy arrays

Series and Features

Combining Dataframes, Data Munging and how to deal with Missing Data

How to use Matplotlib library and start to journey in Data Visualization

Also, why you should learn Python and Pandas Library

Learn Data Science with Python

Examine and manage data structures

Handle wide variety of data science challenges

Select columns and filter rows

Arrange the order and create new variables

Create, subset, convert or change any element within a vector or data frame

Transform and manipulate an existing and real data

The Logic of Matplotlib

What is Matplotlib

Using Matplotlib

Pyplot – Pylab – Matplotlib

Figure, Subplot, Multiplot, Axes,

Figure Customization

Data Visualization

Plot Customization

Grid, Spines, Ticks

Basic Plots in Matplotlib

Seaborn library with these topics

What is Seaborn

Controlling Figure Aesthetics

Color Palettes

Basic Plots in Seaborn

MultiPlots in Seaborn

Regression Plots and Squarify

Geoplotlib with these topics

What is Geoplotlib

Tile Providers and Custom Layers

R and Python in the same course. You decide which one you would go for!

R was built as a statistical language, it suits much better to do statistical learning and R is a statistical programming software favoured by many academia

Since R was built as a statistical language, it suits much better to do statistical learning. It represents the way statisticians think pretty well, so anyone with a formal statistics background can use R easily. Python, on the other hand, is a better choice for machine learning with its flexibility for production use, especially when the data analysis tasks need to be integrated with web applications. If you enroll this course you will have a chance to learn both

You will learn R and Python from scratch

Because data can mean an endless number of things, it’s important to choose the right visualization tools for the job.

you’re interested in learning Tableau, D3 js, After Effects, or Python, has a course for you.

Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Machine Learning, and more!
Requirements

No prior knowledge is required

Free software and tools used during the course

Basic computer knowledge

Desire to learn data science

Nothing else! It’s just you, your computer and your ambition to get started today
Description
Welcome to Complete Data Science, Deep Learning, R  Data Science 2021 course.
Ready for the Data Science career?
 Are you curious about Data Science and looking to start your selflearning journey into the world of data ?
 Are you an experienced developer looking for a landing in Data Science!
In both cases, you are at the right place!
The two most popular programming tools for data science work are Python and R at the moment. It is hard to pick one out of those two amazingly flexible data analytics languages. Both are free and opensource.
R for statistical analysis and Python as a generalpurpose programming language. For anyone interested in machine learning, working with large datasets, or creating complex data visualizations, they are absolutely essential.
With my fullstack Data Science course, you will be able to learn R and Python together.
If you have some programming experience, Python might be the language for you. R was built as a statistical language, it suits much better to do statistical learning with R programming.
But do not worry! In this course, you will have a chance to learn both and will decide to which one fits your niche!
Throughout the course’s first part, you will learn the most important tools in R that will allow you to do data science. By using the tools, you will be easily handling big data, manipulate it, and produce meaningful outcomes.
Throughout the course’s second part, we will teach you how to use the Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms and we will also do a variety of exercises to reinforce what we have learned in this Python for Data Science course.
We will open the door of the Data Science world and will move deeper. You will learn the fundamentals of Python and its beautiful libraries such as Numpy, Pandas, and Matplotlib step by step. Then, we will transform and manipulate real data. For the manipulation, we will use the tidyverse package, which involves dplyr and other necessary packages.
At the end of the course, you will be able to select columns, filter rows, arrange the order, create new variables, group by and summarize your data simultaneously.
Because data can mean an endless number of things, it’s important to choose the right visualization tools for the job. Whether you’re interested in learning Tableau, D3.js, After Effects, or Python, Udemy has a course for you.
In this course we will learn what is the data visualization and how does it work with python.
This course has suitable for everybody who interested data vizualisation concept.
First of all, in this course we will learn some fundamentals of pyhton, and object oriented programming ( OOP ). These are our first steps in our Data Visualisation journey. After then we take a our journey to Data Science world. Here we will take a look data literacy and data science concept. Then we will arrive at our next stop. Numpy library. Here we learn the what is numpy and how we can use it. After then we arrive at our next stop. Pandas library. And now our journey becomes an adventure. In this adventure we’ll enter the Matplotlib world then we exit the Seaborn world. Then we’ll try to understand how we can visualize our data, data viz. But our journey won’t be over. Then we will arrive our final destination. Geographical drawing or best known as Geoplotlib in tableau data visualization.
Learn python and how to use it to python data analysis and visualization, present data. Includes tons of code data vizualisation.
In this course, you will learn data analysis and visualization in detail.
Also during the course you will learn:
The Logic of Matplotlib
 What is Matplotlib
 Using Matplotlib
 Pyplot – Pylab – Matplotlib – Excel
 Figure, Subplot, Multiplot, Axes,
 Figure Customization
 Plot Customization
 Grid, Spines, Ticks
 Basic Plots in Matplotlib
 Overview of Jupyter Notebook and Google Colab
 Seaborn library with these topics
 What is Seaborn
 Controlling Figure Aesthetics
 Color Palettes
 Basic Plots in Seaborn
 MultiPlots in Seaborn
 Regression Plots and Squarify
 Geoplotlib with these topics
 What is Geoplotlib
 Tile Providers and Custom Layers
In this course you will learn;
 How to use Anaconda and Jupyter notebook,
 Fundamentals of Python such as
 Datatypes in Python,
 Lots of datatype operators, methods and how to use them,
 Conditional concept, if statements
 The logic of Loops and control statements
 Functions and how to use them
 How to use modules and create your own modules
 Data science and Data literacy concepts
 Fundamentals of Numpy for Data manipulation such as
 Numpy arrays and their features
 How to do indexing and slicing on Arrays
 Lots of stuff about Pandas for data manipulation such as
 Pandas series and their features
 Dataframes and their features
 Hierarchical indexing concept and theory
 Groupby operations
 The logic of Data Munging
 How to deal effectively with missing data effectively
 Combining the Data Frames
 How to work with Dataset files
 And also you will learn fundamentals thing about Matplotlib library such as
 Pyplot, Pylab and Matplotlb concepts
 What Figure, Subplot and Axes are
 How to do figure and plot customization
 Examining and Managing Data Structures in R
 Atomic vectors
 Lists
 Arrays
 Matrices
 Data frames
 Tibbles
 Factors
 Data Transformation in R
 Transform and manipulate a deal data
 Tidyverse and more
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 az 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 preprocessing, 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.
 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
And we will do many exercises. Finally, we will also have 4 different final projects covering all of Python subjects.
Why would you want to take this course?
Our answer is simple: The quality of teaching.
When you enroll, you will feel the OAK Academy’s seasoned instructors’ expertise.
Fresh Content
It’s no secret how technology is advancing at a rapid rate and it’s crucial to stay on top of the latest knowledge. With this course, you will always have a chance to follow the latest data science trends.
Video and Audio Production Quality
All our content is created/produced as highquality video/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
Dive in now!
We offer full support, answering any questions.
See you in the course!
Who this course is for:
 Anyone interested in data sciences
 Anyone who plans a career in data scientist,
 Software developer whom want to learn data science,
 Anyone eager to learn Data Science with no coding background
 Statisticians, academic researchers, economists, analysts and business people
 Professionals working in analytics or related fields
 Anyone who is particularly interested in big data, machine learning and data intelligence

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