Machine Learning and Deep Learning A-Z: Hands-On Python
Machine Learning and Deep Learning A-Z: Hands-On Python
MP4 | Video: h264, 1280x720 | Audio: AAC, 44100 Hz
Language: English | Size: 5.79 GB | Duration: 19h 1m
What you'll learn
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, my course on Udemy will help you apply machine learning to your work.
Learn Machine Learning with Hands-On Examples
What is Machine Learning?
Machine Learning Terminology
Evaluation Metrics
What are Classification vs Regression?
Evaluating Performance-Classification Error Metrics
Evaluating Performance-Regression Error Metrics
Supervised Learning
Cross Validation and Bias Variance Trade-Off
Use matplotlib and seaborn for data visualizations
Machine Learning with SciKit Learn
Linear Regression Algorithm
Logistic Regresion Algorithm
K Nearest Neighbors Algorithm
Decision Trees And Random Forest Algorithm
Support Vector Machine Algorithm
Unsupervised Learning
K Means Clustering Algorithm
Hierarchical Clustering Algorithm
Principal Component Analysis (PCA)
Recommender System Algorithm
Python
Machine Learning
Deep Learning
Data Visualization
Requirements
Basic knowledge of Python Programming Language
Be Able To Operate & Install Software On A Computer
Free software and tools used during the course
Determination to learn and patience.
Motivation to learn the the second largest number of job postings relative program language among all others
Data visualization libraries in python such as seaborn, matplotlib
Description
Hello there,
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, my course on Udemy here to help you apply machine learning to your work.
Welcome to the "Machine Learning and Deep Learning A-Z: Hands-On Python " course.
Learn to create Machine Learning and Deep Algorithms in Python Code templates included. Python in Data Science | 2021
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 ****** 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 and Deep Learning A-Z: Hands-On Python " a straightforward course for Python Programming Language and Machine Learning.
In the course, you will have down-to-earth way explanations with projects. With this course, you will learn machine learning step-by-step. I made it simple and easy with exercises, challenges, and lots of real-life examples.
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.
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 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 the Data Science world. Here we will take a look at data literacy and data science concepts. Then we will arrive at our next stop. Numpy library. Here we learn 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 at 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
This Machine Learning course is for everyone!
My "Machine Learning with Hands-On Examples in Data Science" 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 you will 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?
More About Machine Learning
Machine Learning Terminology
Evaluation Metrics
What is Classification vs Regression?
Evaluating Performance-Classification Error Metrics
Evaluating Performance-Regression Error Metrics
Machine Learning with Python
Supervised Learning
Cross-Validation and Bias Variance Trade-Off
Use Matplotlib and seaborn for data visualizations
Machine Learning with SciKit Learn
Linear Regression Theory
Logistic Regression Theory
Logistic Regression with Python
K Nearest Neighbors Algorithm Theory
K Nearest Neighbors Algorithm With Python
K Nearest Neighbors Algorithm Project Overview
K Nearest Neighbors Algorithm Project Solutions
Decision Trees And Random Forest Algorithm Theory
Decision Trees And Random Forest Algorithm With Python
Decision Trees And Random Forest Algorithm Project Overview
Decision Trees And Random Forest Algorithm Project Solutions
Support Vector Machines Algorithm Theory
Support Vector Machines Algorithm With Python
Support Vector Machines Algorithm Project Overview
Support Vector Machines Algorithm Project Solutions
Unsupervised Learning Overview
K Means Clustering Algorithm Theory
K Means Clustering Algorithm With Python
K Means Clustering Algorithm Project Overview
K Means Clustering Algorithm Project Solutions
Hierarchical Clustering Algorithm Theory
Hierarchical Clustering Algorithm With Python
Principal Component Analysis (PCA) Theory
Principal Component Analysis (PCA) With Python
Recommender System Algorithm Theory
Recommender System 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.
This course has suitable for everybody who interested in Machine Learning and Deep Learning concepts.
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 history. Then we will arrive at our next stop. Machine Learning. Here we learn the machine learning concepts, machine learning 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. Our play garden. Here we'll make some interesting machine learning models with the information we've learned along our journey.
During the course you will learn:
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
Understanding the machine learning models
Machine Learning models and algorithms
Gathering data
Data pre-processing
Choosing the right algorithm and model
Training and testing the model
Evaluation
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
In this course, we will start from the very beginning and go all the way to the end of "Deep Learning" with examples.
Before we start this course, we will learn which environments we can be used for developing deep learning projects.
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 learn the "Machine Learning and Deep Learning A-Z: Hands-On Python " course.
Dive in now! 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 Wanting to Specialize in Anaconda Python Environment for Data Science and Scientific Computing
Students Wanting to Learn the Application of Supervised Learning (Classification) on Real Data Using Python
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