Introduction to Machine Learning

Introduction to Machine Learning - Machine Learning, Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Markov Models, HMM

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Description

Course Description:

Unlock the power of machine learning with this comprehensive course designed for beginners and intermediate learners. You will be guided through the essential concepts, algorithms, and techniques driving machine learning today, building a solid understanding of how machines learn from data and solve real-world problems. This course is designed to help you grasp the theoretical underpinnings of machine learning while applying your knowledge through solved problems, making complex concepts more accessible.

What You'll Learn:

Core Principles of Machine Learning: Gain a deep understanding of how systems learn from data to make intelligent decisions.

Supervised Learning: Explore predictive modeling using algorithms like Linear Regression, and Support Vector Machines (SVM).

Unsupervised Learning: Master clustering techniques like k-Means and Hierarchical Clustering to discover patterns in data.

Regression and Classification: Learn how to model continuous outcomes (regression) and classify data into distinct categories (classification).

Clustering: Group similar data points to uncover hidden structures within large datasets.

Markov Models & Hidden Markov Models (HMMs): Understand probabilistic models that predict future states and learn how they are used to model sequences and temporal data. Through solved problems, you'll explore how these models work in practice, gaining insights into the theoretical foundation and practical application of HMMs in time-series data and sequential decision-making processes.

Machine learning is transforming industries by enabling systems to learn and make intelligent decisions from data. This course will equip you with a strong foundation in machine learning, focusing on problem-solving and theoretical understanding without the need for hands-on implementation.

Practical Application Through Solved Problems:

This course includes solved problems to illustrate how each algorithm and technique works in practice. These examples will help you apply theoretical concepts to real-world situations, deepening your understanding and preparing you to solve similar problems in your professional or academic career.

Through detailed explanations of algorithms, real-world examples, and step-by-step breakdowns of machine learning processes, you'll develop a solid grasp of the models and techniques used across various industries. This course is perfect for learners who want to master the core concepts of machine learning and engage with practical applications without diving into programming or technical implementation.

Course Highlights:

No Programming Required: Focus on understanding the theory behind machine learning algorithms and models.

Solve Real-World Problems: Work through practical examples to understand how to apply machine learning techniques to everyday challenges.

Evaluate Model Performance: Learn to assess, interpret, and refine machine learning models effectively.

Build a Strong Conceptual Foundation: Prepare for future practical applications in machine learning or data-driven fields.

Who Should Take This Course:

Students and Professionals: Ideal for those seeking an in-depth introduction to machine learning theory.

Enthusiasts with Basic Knowledge of Math and Programming: Perfect for those interested in machine learning concepts through solved problems and real-world examples.

Who this course is for:

• Beginners for Machine learning

Machine Learning with Javascript

Machine Learning with Javascript, Master Machine Learning from scratch using Javascript and TensorflowJS with hands-on projects, Created by Stephen Grider

If you're here, you already know the truth: Machine Learning is the future of everything.

In the coming years, there won't be a single industry in the world untouched by Machine Learning.  A transformative force, you can either choose to understand it now, or lose out on a wave of incredible change.  You probably already use apps many times each day that rely upon Machine Learning techniques.  So why stay in the dark any longer?

There are many courses on Machine Learning already available.  I built this course to be the best introduction to the topic.  No subject is left untouched, and we never leave any area in the dark.  If you take this course, you will be prepared to enter and understand any sub-discipline in the world of Machine Learning.

A common question - Why Javascript?  I thought ML was all about Python and R?

The answer is simple - ML with Javascript is just plain easier to learn than with Python.  Although it is immensely popular, Python is an 'expressive' language, which is a code-word that means 'a confusing language'.  A single line of Python can contain a tremendous amount of functionality; this is great when you understand the language and the subject matter, but not so much when you're trying to learn a brand new topic.

Besides Javascript making ML easier to understand, it also opens new horizons for apps that you can build.  Rather than being limited to deploying Python code on the server for running your ML code, you can build single-page apps, or even browser extensions that run interesting algorithms, which can give you the possibility of developing a completely novel use case!

Does this course focus on algorithms, or math, or Tensorflow, or what?!?!

Let's be honest - the vast majority of ML courses available online dance around the confusing topics.  They encourage you to use pre-build algorithms and functions that do all the heavy lifting for you.  Although this can lead you to quick successes, in the end it will hamper your ability to understand ML.  You can only understand how to apply ML techniques if you understand the underlying algorithms.

That's the goal of this course - I want you to understand the exact math and programming techniques that are used in the most common ML algorithms.  Once you have this knowledge, you can easily pick up new algorithms on the fly, and build far more interesting projects and applications than other engineers who only understand how to hand data to a magic library.

Don't have a background in math?  That's OK! I take special care to make sure that no lecture gets too far into 'mathy' topics without giving a proper introduction to what is going on.

A short list of what you will learn:

• Advanced memory profiling to enhance the performance of your algorithms
• Build apps powered by the powerful Tensorflow JS library
• Develop programs that work either in the browser or with Node JS
• Write clean, easy to understand ML code, no one-name variables or confusing functions
• Pick up the basics of Linear Algebra so you can dramatically speed up your code with matrix-based operations. (Don't worry, I'll make the math easy!)
• Comprehend how to twist common algorithms to fit your unique use cases
• Plot the results of your analysis using a custom-build graphing library
• Learn performance-enhancing strategies that can be applied to any type of Javascript code
• Data loading techniques, both in the browser and Node JS environments

Who is the target audience?

• Javascript developers interested in Machine Learning

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Introduction to Machine Learning Models (AI) Testing

Introduction to Machine Learning Models (AI) Testing - From Scratch, Learn testing types and Strategies involved in all the phases of ML Models (AI) with real time examples

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Description

This course will introduce you to the World of Machine Learning Models Testing.

As AI continues to revolutionize industries, many companies are developing their own ML models to enhance their business operations. However, testing these models presents unique challenges that differ from traditional software testing. Machine Learning Model testing requires a deeper understanding of both data quality and model behavior, as well as the algorithms that power them.

This Course starts with explaining the fundamentals of the Artificial Intelligence & Machine Learning concepts and gets deep dive into testing concepts & Strategies for Machine Learning models with real time examples.

Below is high level of Agenda of the tutorial:

Introduction to Artificial Intelligence

Overview of Machine Learning Models and their Lifecycle

Shift-Left Testing in the ML Engineering Phase

QA Functional Testing in the ML Validation Phase

API Testing Scope for Machine Learning Models

Responsible AI Testing for ML Models

Post-Deployment Testing Strategies for ML Models

Continuous Tracking and Monitoring Activities for QA in Production

By the end of this course,

you will gain expertise in testing Machine Learning Models at every stage of their lifecycle.

Please Note:

This course highlights specialized testing types and methodologies unique to Machine Learning Testing, with real-world examples.

No specific programming language or code is involved in this tutorial.

Who this course is for:

• QA Testers
• Software Engineers
• Software Testers
• Data Engineers
• Developers
• Test Managers

Data Science, AI, and Machine Learning with R

Data Science, AI, and Machine Learning with R - Gain practical experience in R for Data Analysis, Machine Learning and Artificial Intelligence. Become a Data Scientist.

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What you'll learn

• Grasp the core concepts of data science and its applications in various industries.
• Set up and navigate the R programming environment effectively.
• Master R programming fundamentals, including data types, structures, operators, and control flow.
• Understand essential statistical and probability concepts for data analysis.
• Collect data from diverse sources (flat files, databases, web, APIs).
• Clean, manipulate, and preprocess data to ensure its quality and suitability for analysis.
• Conduct exploratory data analysis to uncover patterns and insights using visualizations.
• Analyze and interpret data effectively using R's powerful statistical and visualization tools.
• Build and evaluate various machine learning models for: Prediction (regression), Classification, Clustering, Association rule mining.
• Apply dimensionality reduction methods like PCA and LDA.
• Utilize ensemble methods (bagging and boosting) to improve model performance.
• Build and deploy machine learning models using R to solve real-world problems.
• Think critically about data and apply data science techniques in a variety of contexts.
• Complete an end-to-end capstone project to solidify learning and demonstrate practical skills in data science and machine learning using R.
• Explore re

Hands-On Machine Learning with Python: Real Projects

Hands-On Machine Learning with Python: Real Projects - Master Machine Learning with Python: Build, Train & Deploy Models with Real-World Projects

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What you'll learn

• Implement Machine Learning algorithms in Python using libraries like scikit-learn and TensorFlow.
• Preprocess and analyze datasets to build predictive models.
• Evaluate model performance and select the best algorithms for various problems.
• Develop and deploy real-world machine learning applications from scratch.

Python for Linear Regression in Machine Learning

 

Python for Linear Regression in Machine Learning - Linear & Non-Linear Regression, Lasso & Ridge Regression, SHAP, LIME, Yellowbrick, Feature Selection & Outliers Removal

I found a course on Udemy called “2023 Python for Linear Regression in Machine Learning” 1. The course teaches how to do in-depth analysis of various forms of Linear and Non-Linear Regression. It also covers techniques for building and evaluating machine learning models, such as feature selection, feature engineering, and model evaluation techniques

What you'll learn

• Analyse and visualize data using Linear Regression
• Plot the graph of results of Linear Regression to visually analyze the results
• Learn how to interpret and explain machine learning models
• Do in-depth analysis of various forms of Linear and Non-Linear Regression
• Use YellowBrick, SHAP, and LIME to interact with predictions of machine learning models
• Do feature selection and transformations to fine tune machine learning models
• Course contains result oriented algorithms and data explorations techniques

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Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]

Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024] - Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

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Mastering Machine Learning in Artificial Intelligence

Mastering Machine Learning in Artificial Intelligence

Unlock the potential of AI with our Machine Learning course – master essential techniques and algorithms today!

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DeepFakes & Voice Cloning: Machine Learning The Easy Way

Stable Diffusion, Midjourney, Generative AI, Fake News, +More for SEO, Internet Marketing, & Social Engineering!

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Master DeepFakes & Voice Cloning: Machine Learning Made Simple

Curious about the world of DeepFakes and voice cloning? Our latest blog post, DeepFakes & Voice Cloning: Machine Learning The Easy Way, is your gateway to understanding and implementing these cutting-edge technologies with ease!

DeepFakes and voice cloning are revolutionizing how we interact with media and technology. While these technologies offer exciting possibilities, they can seem complex at first. That’s why our blog post is designed to demystify them and show you how to leverage machine learning for impressive results.

Here’s what you’ll discover in our comprehensive guide:

Introduction to DeepFakes and Voice Cloning: Get a clear understanding of what DeepFakes and voice cloning are, and explore their applications in various fields, from entertainment to security.

Step-by-Step Implementation: Learn how to create DeepFakes and clone voices using easy-to-follow instructions and Python code examples. We break down the process so you can start experimenting with these technologies right away.

Practical Use Cases: See real-world examples of how DeepFakes and voice cloning are being used, and explore ethical considerations and best practices to ensure responsible use.

Tools and Libraries: Discover the most effective tools and libraries available for working with DeepFakes and voice cloning, including TensorFlow, PyTorch, and specialized packages that simplify the process.

Optimization Tips: Gain insights into how to optimize your models for better quality and performance, ensuring your projects meet high standards.

Machine Learning Primer with JS: Regression (Math + Code)

Explore practical coding, data analysis, and visualization with JavaScript and React JS, plus get Math background.

REDEEM

Dive into the world of machine learning with Machine Learning with JS: Regression Tasks (Math + Code). This course offers a focused look at linear regression, blending theoretical knowledge with hands-on coding to teach you how to build and apply linear regression models using JavaScript.

What You Will Learn:

Core Principles of Linear Regression: Begin with the fundamentals of linear regression and expand into multiple regression techniques. Discover how these models can predict future outcomes based on past data.

Hands-On Coding: Engage directly with practical coding examples, utilizing JavaScript. You'll use Node.js for the computational aspects and React.js for dynamic data visualization.

Simplified Mathematics: We make the essential math behind the models accessible, focusing on concepts that allow you to understand and implement the algorithms effectively.

Project-Based Learning: Build a React application from scratch that not only plots data but also computes regression parameters and visualizes these computations in real-time. This hands-on approach will help solidify your learning through actual development experience.

Real-World Applications: Learn to forecast real-world outcomes using the models you build. Understand the importance of residuals and how to quantify model accuracy with statistical measures such as R-squared, Mean Absolute Error (MAE), and Mean Squared Error (MSE).

Advanced Topics in Depth: Go beyond basic regression with sessions on handling complex data types through multiple regression analysis, matrix operations, and model selection techniques.

Course Structure:

This course includes over 80 detailed video lectures that guide you through every step of learning machine learning with JavaScript:

Introduction and Setup: Start with an overview of the necessary tools and configurations. Understand the foundational terms and concepts in regression.

Interactive Exercises: Each new concept is paired with practical coding exercises that reinforce the material by putting theory into practice.

In-Depth Projects: Apply what you've learned in extensive, real-world projects. Predict salary ranges based on job data or estimate car prices with sophisticated regression models.

Why Choose This Course?

Targeted Learning: We focus on linear regression to provide a thorough understanding of one of the most common machine learning techniques.

Practical JavaScript Use: By using JavaScript, a language familiar to many developers, this course demystifies the process of integrating machine learning into web applications and backend services.

Project-Driven Approach: The projects are designed to reflect real industry problems, preparing you for technical challenges in your career.

Who this course is for:

• Beginners curious about the field of machine learning.
• Software developers interested in adding machine learning capabilities to their skillset.
• Students and professionals who prefer a hands-on, practical approach to learning data analysis and statistical modeling.

AI Application Boost with NVIDIA RAPIDS Acceleration

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High-speed and high-performance GPU and CUDA computing! Build Data Science pipelines 50 times faster!

What you'll learn
Understand the differences between processing data using CPU and GPU
Use cuDF as a replacement for pandas for GPU-accelerated processing
Implement codes using cuDF to manipulate DataFrames
Use cuPy as a replacement for numpy for GPU-accelerated processing
Use cuML as a replacement for scikit-learn for GPU-accelerated processing
Implement a complete machine learning project using cuDF and cuML
Compare the performance of classic Python libraries that run on the CPU with RAPIDS libraries that run on the GPU
Implement projects with DASK for parallel and distributed processing
Integrate DASK with cuDF and cuML for GPU performance

Machine Learning - Fundamental of Python Machine Learning

Learn The Most Effective Machine Learning Techniques in Python

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What you'll learn

• The Machine Learning Process
• Standard Deviation
• Linear Regression
• Polynomial Regression
• Multiple Regression
• Hierarchical Clustering
• Logistic Regression
• Bootstrap Aggregation
• Cross Validation

Are you ready to learn on a journey into the captivating world of machine learning using Python? Welcome to "Machine Learning - Fundamentals of Python Machine Learning," your gateway to understanding and applying the core principles of machine learning.

Machine learning is transforming industries, from healthcare to finance, and Python is at the forefront of this revolution. Whether you're a budding data scientist, aspiring machine learning engineer, or simply curious about the potential of AI, this course will equip you with the foundational knowledge and practical skills to harness the power of Python for machine learning.

Key Learning Objectives:

Introduction to Machine Learning: Get a comprehensive overview of machine learning, its significance, and the Python ecosystem's role in the field.

Python for Machine Learning: Learn the basics of Python programming, data structures, and libraries essential for machine learning.

Model Evaluation and Selection: Discover techniques for evaluating machine learning models and selecting the best model for your tasks.

Feature Engineering: Master the art of feature selection and engineering to enhance the performance of your machine learning models.

Why Choose This Course?

Comprehensive Curriculum: This course is designed to take you from a machine learning novice to a proficient practitioner, ensuring you have a deep understanding of the fundamentals.

Hands On Learning: Practice your skills with coding exercises, hands-on projects, and machine learning challenges that replicate real-world scenarios.

Expert Instruction: Benefit from the guidance of experienced instructors who have worked on machine learning projects and are passionate about sharing their knowledge.

Lifetime Access: Enroll once and have lifetime access to the course materials, ensuring your skills stay up to date with the latest developments in machine learning.

Unlock the potential of Python in the world of machine learning. Enroll today in "Machine Learning - Fundamentals of Python Machine Learning" and acquire the knowledge and skills you need to excel in the exciting field of machine learning. Don't miss this opportunity to become a proficient machine learning practitioner.

Your journey to mastering machine learning with Python starts now!

Who this course is for:

• Anyone interested in Machine Learning
• Beginners Who want to Learn Machine Learning

Complete Machine Learning & Data Science with Python | A-Z

 Complete Machine Learning & Data Science with Python | A-Z  - Use Scikit, learn NumPy, Pandas, Matplotlib, Seaborn and dive into machine learning A-Z with Python and Data Science.

The “Complete Machine Learning & Data Science with Python | A-Z” course on Udemy is a comprehensive course that covers Scikit, NumPy, Pandas, Matplotlib, Seaborn, and dives into machine learning A-Z with Python and Data Science. The course is designed to teach you how to use machine learning for predictive texting or smartphone voice recognition. The course is constantly being updated with new content and is available on Udemy’s website

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.
• 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 instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective.
• Python is a general-purpose, object-oriented, high-level programming language.
• Python is a multi-paradigm language, which means that it supports many programming approaches. Along with procedural and functional programming styles
• Python is a widely used, general-purpose programming language, but it has some limitations. Because Python is an interpreted, dynamically typed language
• Python is a general programming language used widely across many industries and platforms. One common use of Python is scripting, which means automating tasks.
• Python is a popular language that is used across many industries and in many programming disciplines. DevOps engineers use Python to script website.
• Python has a simple syntax that makes it an excellent programming language for a beginner to learn. To learn Python on your own, you first must become familiar
• Machine learning describes systems that make predictions using a model trained on real-world data.
• Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing.
• It's possible to use machine learning without coding, but building new systems generally requires code.
• Python is the most used language in machine learning. Engineers writing machine learning systems often use Jupyter Notebooks and Python together.
• Machine learning is generally divided between supervised machine learning and unsupervised machine learning. In supervised machine learning.
• Machine learning is one of the fastest-growing and popular computer science careers today. Constantly growing and evolving.
• Machine learning is a smaller subset of the broader spectrum of artificial intelligence. While artificial intelligence describes any "intelligent machine"
• A machine learning engineer will need to be an extremely competent programmer with in-depth knowledge of computer science, mathematics, data science.
• Python machine learning, complete machine learning, machine learning a-z

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The Complete Visual Guide to Machine Learning & Data Science

 The Complete Visual Guide to Machine Learning & Data Science - Explore Data Science & Machine Learning topics with simple, step-by-step demos and user-friendly Excel models (NO code!)

Sure! Here’s a brief summary of “The Complete Visual Guide to Machine Learning & Data Science” course on Udemy 1:

This course is designed for everyday people looking for an intuitive, beginner-friendly introduction to the world of machine learning and data science. It aims to build confidence with guided, step-by-step demos and learn foundational skills from the ground up. Instead of memorizing complex math or learning a new coding language, this course breaks down and visualizes complex concepts in an easy-to-understand way.

The course covers topics such as:

• Introduction to Machine Learning
• Data Preprocessing
• Regression
• Classification
• Clustering
• Association Rule Learning
• Reinforcement Learning
• Natural Language Processing
• Deep Learning

The course also includes practical exercises and quizzes to help you test your knowledge.

What you'll learn

• Build foundational machine learning & data science skills WITHOUT writing complex code
• Play with interactive, user-friendly Excel models to learn how machine learning techniques actually work
• Enrich datasets using feature engineering techniques like one-hot encoding, scaling and discretization
• Predict categorical outcomes using classification models like K-nearest neighbors, naïve bayes, and decision trees
• Build accurate forecasts and projections using linear and non-linear regression models
• Apply powerful techniques for clustering, association mining, outlier detection, and dimensionality reduction
• Learn how to select and tune models to optimize performance, reduce bias, and minimize drift
• Explore unique, hands-on case studies to simulate how machine learning can be applied to real-world cases

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Automated Machine Learning with AutoGluon Library in Python

 

Automated Machine Learning with AutoGluon Library in Python - Discover how to easily automate entire machine learning pipelines with the extremely powerful Autogluon library from AWS

AutoGluon is an open-source AutoML library that allows you to train and deploy high-accuracy machine learning and deep learning models on image, text, time series, and tabular data with just a few lines of code1. It automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications2.

You can learn how to install and set up the Autogluon Python library in your local or cloud-based environment by taking this course on Udemy1.

Mathematical Foundations of Machine Learning

What you'll learn

• Understand the basics of the Autogluon Python library and its capabilities for automating machine learning tasks.
• Learn how to install and set up the Autogluon Python library in your local environment.
• Develop skills in data preparation and cleaning processes that are critical for successful machine learning outcomes using Autogluon.
• Discover best practices for selecting and configuring machine learning models to achieve optimal results with minimal effort.
• Explore how to use Autogluon to create high-accuracy models for image classification tasks, including object detection, segmentation, and classification.
• Understand how to use Autogluon to perform natural language processing (NLP) tasks such as sentiment analysis.
• Learn how to train and deploy time series models using Autogluon to make accurate predictions for future events or trends.
• Gain hands-on experience in using Autogluon to analyze tabular data and build predictive models for business applications and financial forecasting.

Python for Machine Learning & Data Science Masterclass

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Machine Learning and Deep Learning A-Z: Hands-On Python

 Machine Learning and Deep Learning A-Z: Hands-On Python - Python Machine Learning and Python Deep Algorithms in Python Code templates included. Python in Data Science | 2021

Sure! Here’s a brief article on Machine Learning and Deep Learning A-Z: Hands-On Python course on Udemy 1. This course teaches you how to learn NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, Scipy and develop Machine Learning Models in Python. It has a rating of 4.7 out of 5 and over 1106 reviews 1.

If you’re looking for more information on Machine Learning and Deep Learning, you can check out these articles on Coursera 23. They provide a beginner’s guide to Deep Learning vs. Machine Learning and a course on Machine Learning: Theory and Hands-on Practice with Python.

Mastering Time Series Forecasting with Python

What you'll learn

• Machine learning isn’t just useful for predictive texting or smartphone voice recognition.
• Learn Machine Learning with Hands-On Examples
• What is Machine Learning?
• Machine Learning Terminology
• Evaluation Metrics for Python machine learning, Python Deep learning
• 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, python machine learning and deep learning
• Machine Learning, machine learning A-Z
• Deep Learning, Deep learning a-z
• Data Visualization
• Machine learning is constantly being applied to new industries and new problems. Whether you’re a marketer, video game designer, or programmer
• Machine learning describes systems that make predictions using a model trained on real-world data.
• Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing
• It's possible to use machine learning without coding, but building new systems generally requires code.
• 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.
• Machine learning is generally divided between supervised machine learning and unsupervised machine learning.
• Python instructors on Udemy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction
• What are the limitations of Python? Python is a widely used, general-purpose programming language, but it has some limitations.
• How is Python used? Python is a general programming language used widely across many industries and platforms.
• What jobs use Python? Python is a popular language that is used across many industries and in many programming disciplines
• How do I learn Python on my own? Python has a simple syntax that makes it an excellent programming language for a beginner to learn.
• Data science is everywhere. Better data science practices are allowing corporations to cut unnecessary costs, automate computing, and analyze markets.
• What is data science? We have more data than ever before. But data alone cannot tell us much about the world around us.
• 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.
• What are the most popular coding languages for data science? Python is the most popular programming language for data science.

Machine Learning Real World projects in Python

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Machine learning y data science con scikit-learn y pyspark

Machine learning y data science con scikit-learn y pyspark, Aprende las principales técnicas de machine learning y ciencia de datos para aplicarlas en proyectos con python

• Created by José Manuel Ortega
• Spanish
• Spanish [Auto-generated]

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What you'll learn

• Investigar que con Python también se puede hacer ciencia de datos y machine learning.
• Aplicar técnicas de machine learning y ciencia de datos en proyectos con python
• Que el alumno descubra el potencial de las técnicas de Machine Learning para el análisis de datos y sobre todo para extracción de información a partir de los datos. Es decir, sacar valor a los datos.
• Presentar con casos prácticas las técnicas de Machine Learning que actualmente se utilizan en soluciones de análisis de datos, tanto en Big Data como en Data Science en general.
• Dar a conocer una de las herramientas más fáciles de utilizar para aplicar Machine Learning a problemas reales de una manera sencilla, como es Python, Numpy y Scikit-Learn.
• Requirements
• Es necesario tener conocimientos básico de python.
• Es necesario tener instalada la distribución de Python de Anaconda, preferentemente la versión de Python3. Se usarán principalmente las librerías numpy, scipy, pandas, scikit-learn y pyspark.
• Es necesario tener instalado python.Trabajaremos con python 3.6

Mathematical Foundation For Machine Learning and AI

Udemy Online Courses - Mathematical Foundation For Machine Learning and AI, Learn the core mathematical concepts for machine learning and learn to implement them in R and python

• Created by Eduonix Learning Solutions, Eduonix-Tech .
• Last updated 12/2018
•  English
• 4.5 hours on-demand video
• 1 article
• 6 downloadable resources
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

What you'll learn

• Refresh the mathematical concepts for AI and Machine Learning
• Learn to implement algorithms in python
• Understand the how the concepts extend for real world ML problems

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A-Z Machine Learning using Azure Machine Learning (AzureML) - Online Courses Udemy

Online Courses Udemy - A-Z Machine Learning using Azure Machine Learning (AzureML)

Hands on Machine Learning using Azure ML: Azure Machine Learning Studio to Advance ML Algorithms. No Coding Required.

• Created by Jitesh Khurkhuriya
• Last updated 5/2019
•  English
• 11 hours on-demand video
• 2 articles
• 39 downloadable resources
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

What you'll learn

• Master Data Science and Machine Learning Models using Azure ML.
• Understand the concepts and intuition of Machine Learning algorithms
• Build Machine Learning models within minutes
• Choose the correct Machine Learning Algorithm using the cheatsheet
• Deploy production grade Machine Learning algorithms
• Deploy Machine Learning webservices in the simplest form possible including excel
• Bring in great value to business you manage

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Machine Learning using R and Python

Machine Learning using R and Python, Machine Learning using R Programming and Python Programming

• Created by Srinivas Reddy DataHills
•  English
•  English [Auto-generated]
• 69.5 hours on-demand video
• 84 downloadable resources
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

What you'll learn

• This course has been prepared for professionals aspiring to learn the basics of R and Python to develop applications involving machine learning techniques such as recommendation, classification, and clustering. Through this course, you will learn to solve data-driven problems and implement your solutions using the powerful yet simple programming language R and Python with its packages. After completing this course, you will gain a broad picture of the machine learning environment and the best practices for machine learning techniques.

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