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Machine Learning Python with Theoretically for Data Science

What you’ll learn

  • Machine Learning Python with Theoretically for Data Science
  • Machine learning describes systems that make predictions using a model trained on real-world data
  • Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and ne
  • Machine learning is a smaller subset of the broader spectrum of artificial intelligence. While artificial intelligence describes any “intelligent machine”
  • Machine learning is one of the fastest-growing and popular computer science careers today. Constantly growing and evolving.
  • Machine learning is generally divided between supervised machine learning and unsupervised machine learning. In supervised machine learning.
  • Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing.
  • What is Machine Learning?
  • What are Machine Learning Terminologies?
  • Installing Anaconda Distribution for Windows
  • Installing Anaconda Distribution for MacOs
  • Installing Anaconda Distribution for Linux
  • Overview of Jupyter Notebook and Google Colab
  • Classification vs Regression in Machine Learning
  • Machine Learning Model Performance Evaluation: Classification Error
  • Metrics
  • Machine Learning Model Performance Evaluation: Regression Error Metrics
  • Machine Learning with Python
  • What is Supervised Learning in Machine Learning?
  • What is Linear Regression Algorithm in Machine Learning?
  • Linear Regression Algorithm with Python
  • What is Bias Variance Trade-Off?
  • What is Logistic Regression Algorithm in Machine Learning?
  • Logistic Regression Algorithm with Python
  • machine learning, python, data science, machine learning python, python data science, machine learning a-z
  • Python is the most used language in machine learning. Engineers writing machine learning systems often use Jupyter Notebooks and Python together.

Requirements

  • Basic knowledge of Python Programming Language
  • Be able to Operate & Install Software On A Computer
  • Free software and tools used during the machine learning A-Z course
  • Determination to learn machine learning and patience.
  • Motivation to learn the second largest number of job postings relative program language among all others
  • Data visualization libraries in python such as Seaborn, Matplotlib
  • Curiosity for Machine Learning with Python
  • Desire to learn Python
  • Desire to work on Python and Machine Learning
  • Desire to learn Matplotlib library
  • Desire to learn Pandas library
  • Desire to learn Numpy library
  • Desire to work on Seaborn library
  • Desire to learn Machine Learning A-Z

Description

Hello there,
Welcome to the “Machine Learning Python with Theoretically for Data Science” course.

Machine Learning with Python in detail both practically and theoretically with machine learning project for data science

Machine learning courses teach you the technology and concepts behind predictive text, virtual assistants, and artificial intelligence. You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning training helps you stay ahead of new trends, technologies, and applications in this field.

Machine learning describes systems that make predictions using a model trained on real-world data. For example, let’s say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information around whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it’s fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that. Even fitting a line to a set of observed data points, and using that line to make new predictions, counts as a machine learning model.


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. machine learning, python, data science, machine learning python, python data science, machine learning a-z, python for data science and machine learning bootcamp, python for data science, complete machine learning, machine learning projects,

Use Scikit Learn, NumPy, Pandas, Matplotlib, Seaborn, and dive into Machine Learning A-Z with Python and Data Science.

Join this machine learning course, and develop the foundation you need to better understand and utilize machine learning algorithms. Whatever level of technology you work with from day to day, machine learning training with an experienced instructor can help you advance in your technology career.

Whether you’re a marketer, video game designer, or programmer, my course on OAK Academy here to help you apply machine learning to your work.


It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models.

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.


Whether you work in machine learning or Finance or are pursuing a career in web development or data science.

Python is one of the most important skills you can learn. Python’s simple syntax is especially suited for desktop, web, and business applications. Python’s design philosophy emphasizes readability and usability.

Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization.

The core programming language is quite small, and the standard library is also large.

In fact, Python’s large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.

Do you know data science needs will create 11.5 million job openings by 2026?

Do you know the average salary is $100.000 for data science careers!

Data Science Careers Are Shaping the Future

Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. So data science careers are in high demand.

· If you want to learn one of the employer’s most request skills?

· If you are curious about Data Science and looking to start your self-learning journey into the world of data with Python?

· If you are an experienced developer and looking for a landing in Data Science!

In all cases, you are at the right place!

We’ve designed for you “Machine Learning with Theory and Practice A-Z” a straightforward course for Python Programming Language and Machine Learning.

With this course, you will learn machine learning step-by-step. I made it simple and easy with exercises and challenges.

We will open the door of the Data Science and Machine Learning A-Z world and will move deeper.

You will learn the fundamentals of Machine Learning A-Z and its beautiful libraries such as Scikit Learn.

Throughout the course, we will teach you how to use Python to analyze data, create beautiful visualizations, and use powerful machine learning python algorithms.

This Machine Learning course is for everyone!

Our “Machine Learning with Theory and Practice A-Z” course is for everyone! If you don’t have any previous experience, not a problem! This course is expertly designed to teach everyone from complete beginners, right through to professionals (as a refresher).

Why we use a Python programming language in Machine learning?

Python is a general-purpose, high-level, and multi-purpose programming language. The best thing about Python is, it supports a lot of today’s technology including vast libraries for Twitter, Data Mining, Scientific Calculations, Designing, Back-End Server for websites, Engineering Simulations, Artificial Learning, Augmented reality and what not! Also, it supports all kinds of App development.

What will you learn?

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

Before each lesson, there will be a theory part. After learning the theory parts, we will reinforce the subject with practical examples.

During the course you will learn the following topics:

· What is Machine Learning?

· What are Machine Learning Terminologies?

· Installing Anaconda Distribution for Windows

· Installing Anaconda Distribution for MacOs

· Installing Anaconda Distribution for Linux

· Overview of Jupyter Notebook and Google Colab

· Classification vs Regression in Machine Learning

· Machine Learning Model Performance Evaluation: Classification Error

· Metrics

· Machine Learning Model Performance Evaluation: Regression Error Metrics

· Machine Learning with Python

· What is Supervised Learning in Machine Learning?

· What is Linear Regression Algorithm in Machine Learning?

· Linear Regression Algorithm with Python

· What is Bias Variance Trade-Off?

· What is Logistic Regression Algorithm in Machine Learning?

· Logistic Regression Algorithm with Python

With my up-to-date course, you will have a chance to keep yourself up-to-date and equip yourself with a range of Python programming skills. I am also happy to tell you that I will be constantly available to support your learning and answer questions.

Frequently asked questions

What is machine learning?

Machine learning describes systems that make predictions using a model trained on real-world data. For example, let’s say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information around whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it’s fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that. Even fitting a line to a set of observed data points, and using that line to make new predictions, counts as a machine learning model.

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.

What is the best language for machine learning?

Python is the most used language in machine learning. Engineers writing machine learning systems often use Jupyter Notebooks and Python together. Jupyter Notebooks is a web application that allows experimentation by creating and sharing documents that contain live code, equations, and more. Machine learning involves trial and error to see which hyperparameters and feature engineering choices work best. It’s useful to have a development environment such as Python so that you don’t need to compile and package code before running it each time. Python is not the only language choice for machine learning. Tensorflow is a popular framework for developing neural networks and offers a C++ API. There is a machine learning framework for C# called ML. NET. Scala or Java are sometimes used with Apache Spark to build machine learning systems that ingest massive data sets. You may find yourself using many different languages in machine learning, but Python is a good place to start.

What are the different types of machine learning?

Machine learning is generally divided between supervised machine learning and unsupervised machine learning. In supervised machine learning, we train machine learning models on labeled data. For example, an algorithm meant to detect spam might ingest thousands of email addresses labeled ‘spam’ or ‘not spam.’ That trained model could then identify new spam emails even from data it’s never seen. In unsupervised learning, a machine learning model looks for patterns in unstructured data. One type of unsupervised learning is clustering. In this example, a model could identify similar movies by studying their scripts or cast, then group the movies together into genres. This unsupervised model was not trained to know which genre a movie belongs to. Rather, it learned the genres by studying the attributes of the movies themselves. There are many techniques available within these two types of machine learning, for example: deep learning, reinforcement learning, and more.

Is machine learning a good career?

Machine learning is one of the fastest-growing and popular computer science careers today. Constantly growing and evolving, you can apply machine learning to a variety of industries, from shipping and fulfillment to medical sciences. Machine learning engineers work to create artificial intelligence that can better identify patterns and solve problems. The machine learning discipline frequently deals with cutting-edge, disruptive technologies. However, because it has become a popular career choice, it can also be competitive. Aspiring machine learning engineers can differentiate themselves from the competition through certifications, boot camps, code repository submissions, and hands-on experience.

What is the difference between machine learning and artifical intelligence?

Machine learning is a smaller subset of the broader spectrum of artificial intelligence. While artificial intelligence describes any “intelligent machine” that can derive information and make decisions, machine learning describes a method by which it can do so. Through machine learning, applications can derive knowledge without the user explicitly giving out the information. This is one of the first and early steps toward “true artificial intelligence” and is extremely useful for numerous practical applications. In machine learning applications, an AI is fed sets of information. It learns from these sets of information about what to expect and what to predict. But it still has limitations. A machine learning engineer must ensure that the AI is fed the right information and can use its logic to analyze that information correctly.

What skills should a machine learning engineer know?

A machine learning engineer will need to be an extremely competent programmer with in-depth knowledge of computer science, mathematics, data science, and artificial intelligence theory. Machine learning engineers must be able to dig deep into complex applications and their programming. As with other disciplines, there are entry-level machine learning engineers and machine learning engineers with high-level expertise. Python and R are two of the most popular languages within the machine learning field.

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 languages on the Udemy platform where it has over 1000 hours of video education lessons. OAK Academy both increases 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 learnnow Dive into; “Machine Learning Python with Theoretically for Data Science” course.

Machine Learning with Python in detail both practically and theoretically with machine learning project for data science

See you in the course!

Who this course is for:

  • 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. It is for everyone
  • Anyone who wants to start learning “Machine Learning”
  • Anyone who needs a complete guide on how to start and continue their career with Machine Learning
  • Software developer who wants to learn “Machine Learning”
  • Students Interested in Beginning Data Science Applications in Python Environment
  • People who want to Specialize in Anaconda Python Environment for Data Science and Scientific Computing
  • Students who want to Learn the Application of Supervised Learning on Real Data Using Python
  • Anyone eager to learn python for Data Science and Machine Learning bootcamp with no coding background
  • Anyone interested in Data Science.
  • Anyone who plans a career in Data Scientist,
  • Software developer who want to learn Python

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