Machine Learning Python with Theoretically for Data Science
Machine Learning with Python in detail both practically and theoretically with machine learning project for data science
Oak Academy
Summary
- Reed courses certificate of completion - Free
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Overview
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 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.
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.
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
Curriculum
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Introduction to Machine Learning Python with Theoretically for Data Science 07:44
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Installations 37:06
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Evaluation Metrics in Machine Learning 52:44
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Supervised Learning with Machine Learning 06:12
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Linear Regression Algorithm in Machine Learning A-Z 1:21:31
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Bias Variance Trade-Off in Machine Learning 11:47
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Logistic Regression Algorithm in Machine Learning A-Z 1:03:03
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Extra 01:00
Course media
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.
Who is this course for?
- 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
- 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
Requirements
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Basic knowledge of Python Programming Language
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Be able to Operate & Install Software On A Computer
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Free software and tools used during the machine learning A-Z course
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Determination to learn machine learning and patience
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Motivation to learn the second largest number of job postings relative program language among all others
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Data visualization libraries in python such as Seaborn Matplotlib
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Curiosity for Machine Learning with Python
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Desire to learn Matplotlib library
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Desire to learn Pandas library
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Desire to learn Numpy library
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Desire to work on Seaborn library
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Desire to learn Machine Learning
Questions and answers
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Certificates
Reed courses certificate of completion
Digital certificate - Included
Will be downloadable when all lectures have been completed
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This course is advertised on reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.
