Python Numpy: Machine Learning & Data Science Course
MP4 | Video: AVC 1280×720 | Audio: AAC 44KHz 2ch | 8 hours 58 minutes | 66 lectures | 1.89 GB
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Welcome to
Python Numpy: Machine Learning & Data Science Course
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OAK Academy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies Whether you’re interested in machine learning, data mining, or data analysis, Udemy has a course for you
Data science is everywhere Better data science practices are allowing corporations to cut unnecessary costs, automate computing, and analyze markets Essentially, data science is the key to getting ahead in a competitive global climate
Python Numpy, Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels
Are you ready for a
Data Science
career?
Do you want to learn the Python Numpy from Scratch? or
Are you an experienced Data scientist and looking to improve your skills with Numpy!
In both cases, you are at the right place! The number of companies and enterprises using Python is day by day The world we are in is experiencing the age of informatics Python and its
Numpy library
will be the right choice for you
Numpy
is a library for the
Python
programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays Moreover,
Numpy
forms the foundation of the
Machine Learning
stack
NumPy
aims to provide an array object that is up to 50x faster than traditional Python lists The array object in
NumPy
is called ndarray , it provides a lot of supporting functions that make working with ndarray very easy Arrays are very frequently
used
in data science, where speed and resources are very important
In this 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 library
Numpy
step by step with hands-on examples Most importantly in Data Science, you should know how to use effectively the Numpy library Because this library is limitless
Throughout the course, we will teach you how to
use Python in Linear Algebra, and Neural Network concept
Machine Learning with NumPy and Python Data Science
course
In this course you will learn;
How to use Anaconda and Jupyter notebook,
Fundamentals of Python
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
Numpy functions
Numexpr module
How to do indexing and slicing on Arrays
Linear Algebra
Using numpy in Neural Network
Numpy python
data science
Python Numpy
Python data science
python numpy: machine learning & data science
machine learning python
python
And we will do some exercises Finally, we will also do a
neural network project with Numpy
What is data science?
We have more data than ever before But data alone cannot tell us much about the world around us We need to interpret the information and discover hidden patterns This is where
data science
comes in
Data science python
uses algorithms to understand raw data The main difference between data science and traditional data analysis is its focus on prediction
Python data science
seeks to find patterns in data and use those patterns to predict future data It draws on machine learning to process large amounts of data, discover patterns, and predict trends
Data science using python
includes preparing, analyzing, and processing data It draws from many scientific fields, and as a
python for data science
, it progresses by creating new algorithms to analyze data and validate current methods
What does a data scientist do?
Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems This requires several steps First, they must identify a suitable problem Next, they determine what data are needed to solve such a situation and figure out how to get the data Once they obtain the data, they need to clean the data The data may not be formatted correctly, it might have additional unnecessary data, it might be missing entries, or some data might be incorrect Data Scientists must, therefore, make sure the data is clean before they analyze the data To analyze the data, they use machine learning techniques to build models Once they create a model, they test, refine, and finally put it into production
What are the most popular coding languagws for data science?
Python for data science
is the most popular programming language for data science It is a universal language that has a lot of libraries available It is also a good beginner language R is also popular; however, it is more complex and designed for statistical analysis It might be a good choice if you want to specialize in statistical analysis You will want to know either Python or R and SQL SQL is a query language designed for relational databases Data scientists deal with large amounts of data, and they store a lot of that data in relational databases Those are the three most-used programming languages Other languages such as Java, C++, JavaScript, and Scala are also used, albeit less so If you already have a background in those languages, you can explore the tools available in those languages However, if you already know another programming language, you will likely be able to pick up
How long does it take to become a data scientist?
data science projects
using open data The more you practice, the more you will learn, and the more confident you will become Once you have several projects that you can point to as good examples of your skillset as a data scientist, you are ready to enter the field
How can I learn data science on my own?
It is possible to learn
data science projects
Does data science require coding?
The jury is still out on this one Some people believe that it is possible to become a data scientist without knowing how to code, but others disagree A lot of algorithms have been developed and optimized in the field You could argue that it is more important to understand how to use the algorithms than how to code them yourself As the field grows, more platforms are available that automate much of the process However, as it stands now, employers are primarily looking for people who can code, and you need basic programming skills The
data scientist
role is continuing to evolve, so that might not be true in the future The best advice would be to find the path that fits your skillset
What skills should a data scientist know?
Is data science a good career?
The demand for data scientists is growing We do not just have data scientists; we have data engineers, data administrators, and analytics managers The jobs also generally pay well This might make you wonder if it would be a promising career for you A better understanding of the type of work a data scientist does can help you understand if it might be the path for you First and foremost, you must think analytically
Data science from scratch
is about gaining a more in-depth understanding of info through data Do you fact-check information and enjoy diving into the statistics? Although the actual work may be quite technical, the findings still need to be communicated Can you explain complex findings to someone who does not have a technical background? Many data scientists work in cross-functional teams and must share their results with people with very different backgrounds
What is python?
Machine learning python
Python bootcamp
Python vs R: What is the Difference?
Python and R are two of today’s most popular programming tools When deciding between Python and R in
data science
, you need to think about your specific needs On one hand, Python is relatively easy for beginners to learn, is applicable across many disciplines, has a strict syntax that will help you become a better coder, and is fast to process large datasets On the other hand, R has over 10,000 packages for data manipulation, is capable of easily making publication-quality graphics, boasts superior capability for statistical modeling, and is more widely used in academia, healthcare, and finance
What does it mean that Python is object-oriented?
Python is a multi-paradigm language, which means that it supports many
data analysis
programming approaches Along with procedural and functional programming styles, Python also supports the object-oriented style of programming In object-oriented programming, a developer completes a programming project by creating Python objects in code that represent objects in the actual world These objects can contain both the data and functionality of the real-world object To generate an object in Python you need a class You can think of a class as a template You create the template once, and then use the template to create as many objects as you need Python classes have attributes to represent data and methods that add functionality A class representing a car may have attributes like color, speed, and seats and methods like driving, steering, and stopping
What are the limitations of Python?
Python is a widely used, general-purpose programming language, but it has some limitations Because Python in
machine learning
is an interpreted, dynamically typed language, it is slow compared to a compiled, statically typed language like C Therefore, Python is useful when speed is not that important Python’s dynamic type system also makes it use more memory than some other programming languages, so it is not suited to memory-intensive applications The Python virtual engine that runs Python code runs single-threaded, making concurrency another limitation of the programming language Though Python is popular for some types of game development, its higher memory and CPU usage limits its usage for high-quality 3D game development That being said, computer hardware is getting better and better, and the speed and memory limitations of Python are getting less and less relevant
How is Python used?
What jobs use Python?
How do I learn Python on my own?
What is machine learning?
What is machine learning used for?
Machine learning is being applied to virtually every field today That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use Machine learning is often a disruptive technology when applied to new industries and niches Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions
Does machine learning require coding?
It’s possible to use machine learning without coding, but building new systems generally requires code For example, Amazon’s Rekognition service allows you to upload an image via a web browser, which then identifies objects in the image This uses a pre-trained model, with no coding required However, developing machine learning systems involves writing some Python code to train, tune, and deploy your models It’s hard to avoid writing code to pre-process the data feeding into your model Most of the work done by a machine learning practitioner involves cleaning the data used to train the machine They also perform “feature engineering” to find what data to use and how to prepare it for use in a machine learning model Tools like AutoML and SageMaker automate the tuning of models Often only a few lines of code can train a model and make predictions from it An introductory understanding of Python will make you more effective in using machine learning systems
Why would you want to take this course?
We have prepared this course in the simplest way for beginners and have prepared many different exercises to help them understand better
No prior knowledge is needed!
In this course, you need no previous knowledge about Python or Numpy
This course will take you from a beginner to a more experienced level
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
Python Numpy: Machine Learning & Data Science Course
We offer
full support
, answering any questions
See you in the course!
