Data Science A-Z, Python for Data Science, R for Data Science, Statistics for Data Science, Math for Deep Learning

**This course includes:**

- 23 hours on-demand video
- 2 articles
- Full lifetime access
- Access on mobile and TV
- Certificate of completion

**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 pre-processing, 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
- Multi-Plots 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!

**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 self-learning 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 open-source.

R for statistical analysis and Python as a general-purpose programming language. For anyone interested in machine learning, working with large datasets, or creating complex data visualizations, they are absolutely essential.

With my full-stack 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
- Multi-Plots 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 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.

- 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.

**Also See : Full Stack Data Science with Python, Numpy and R Programming**

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