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Best Free Machine Learning Courses Online

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Best Free Machine Learning Courses 2022
Best Free Machine Learning Courses 2022

Here is the Collection of best free online courses to learn Machine Learning for beginners from Udemy, Coursera, and other online portals

Below is the list of some best Free Machine Learning Courses. These Best Free Machine Learning Courses are not only beneficial for beginners but its also good for advanced learners. In the next section, we will be discussing in detail about the Best Free Machine Learning Courses.

If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit.

List of Best Free Machine Learning Courses in 2022

Machine Learning – Coursera

Rating: 4.9

In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include:

  • Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
  • Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
  • Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Skills you will gain:

  • Logistic Regression
  • Artificial Neural networks
  • ML Algorithms
  • Machine Learning

Best for: Beginners | Course Duration: 61hrs | Instructor: Andrew ng

Price: Free aduit course

Info: Visit this course and get amazing offers on the certification course

Machine Learning and Data Science with AWS (Free) – Udemy

This course consists of multiple topics that are arranged in multiple sections. In the first few sections you would learn cloud services related to Data Science and Analysis on AWS with hands on practical examples. There you would be learning about creating a crawler in Glue, Analyzing dataset using SQL in Amazon Athena. After that you would learn to prepare a dataset for creating Data Visualization charts and reports that can be used for finding critical insights from the dataset that can be used in decision making process. You will learn to create calculated fields, excluded lists and filters on AWS Quicksight, followed by some advanced charts such as Word cloud and Funnel chart.

After that in Machine Learning section, you will learn about Natural language processing and it’s application with the help of AWS Comprehend and Translate. AWS Comprehend is used to identify the language of the text, extract key phrases, places, people, brands, or events, understand sentiment about products or services, and identify the main topics from a library of documents. AWS Translate is used for translating language from one language to another.

Things you will learn:

  • You could prepare your dataset using AWS Glue, and Quicksight
  • Perform Data Analysis using Athena
  • Could Create Data Visualization Charts with Quicksight
  • You could create and develop machine learning models using Natural Language Processing

Best for:

  • Anyone who is curious to learn Data Science and Machine Learning on AWS
  • Student and IT professionals curious to learn AWS cloud services for Machine learning and Data Science
  • People interested in learning AWS Glue, Athena, Quicksight, Amazon NLP and Computer Vision

Info: Visit this course and get amazing offers on the certification course

Supervised Machine Learning Regression Course – Coursera

This Superviced Machine Learning Regression course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.

This course targets aspiring data scientists interested in acquiring hands-on experience  with Supervised Machine Learning Regression techniques in a business setting.

Skills you will learn from this course:

  • Regression Analysis
  • Supervised Learning
  • Linear Regression
  • Ridge Regression
  • ML Algorithms

Course Duration: 11 hrs | Provider: IBM

Price: Free aduit course

Info: Visit this course and get amazing offeres on certification course

Machine Learning and Artificial intelligence with Python (Free) – Udemy

This course covers all Python has to offer, from the fundamentals to more advanced subjects.

A great blend of theory and practice, jam-packed with real-world examples, exercises, and step-by-step answers – devoid of “fluff” and long explanation!

Learn how to use Python to automation, web development, and machine learning.

Machine learning specialized libraries and frameworks are available in a large number of Python distributions, making the development process easier and decreasing development time. Python’s straightforward syntax and readability enable it to be used for fast testing of complicated algorithms while also making it accessible to those who are not programmers.

Data science with Python is made simpler by the availability of a plethora of libraries, such as NumPy, Pandas, and Matplotlib, which facilitate data cleaning, data analysis, data visualization, and machine learning activities.

Skills you will gain:

  • Use Python for Data Science and Machine Learning
  • Learn basic machine learning
  • Understand the uses of Machine Learning
  • Apply Machine learning in the real world

Info: Visit this course and get amazing offeres on certification course

Supervised Machine Learning Classification Course – Coursera

Rating: 4.9

This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.

This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques

In this Supervised Machine Learning Course you will learn

  • Differentiate uses and applications of classification and classification ensembles
  • Describe and use logistic regression models
  • Describe and use decision tree and tree-ensemble models
  • Describe and use other ensemble methods for classification
  • Use a variety of error metrics to compare and select the classification model that best suits your data
  • Use oversampling and undersampling as techniques to handle unbalanced classes in a data set.

Course Duration: 25 hrs | Provider: IBM

Price: Free aduit course

Info: Visit this course and get amazing offeres on certification course

Practical Machine Learning with Scikit-Learn (Free) – Udemy

n this course, we will learn multiple machine learning algorithms, along with data preprocessing, all in under an hour. We will go over regression, classification, component analysis and boosting all in scikit-learn, one of the most popular machine learning libraries for python.

Algorithms we’ll go over (in order):

  • Linear Regression
  • Polynomial Regression
  • Multiple Linear Regression
  • Logistic Regression
  • Support Vector Machines
  • Decision Trees
  • Random Forest
  • Principle Component Analysis
  • Gradient Boosting
  • XGBoost

Best for:

  • People looking to get into AI but don’t know where to start
  • People who want to build accurate models as quickly as possible

Info: Visit this course and get amazing offeres on certification course

Unsupervised Machine Learning – Coursera

Rating: 4.7

This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.

This course is mainly for data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques.

Skills you will gain from this course are:

  • Dimensionality Reduction
  • Unsupervised Learning
  • Cluster Analysis
  • K Means Clustering
  • Principal Component Analysis

Course Duration: 13 hrs | Provider: IBM

Price: Free aduit course

Info: Visit this course and get amazing offeres on certification course

Python for Data science and Machine Learning Master Class – Udemy

This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Data Science and Machine Learning. The typical starting salary for a data scientists can be over $150,000 dollars, and we’ve created this course to help guide students to learning a set of skills to make them extremely hirable in today’s workplace environment.

We’ll cover everything you need to know for the full data science and machine learning tech stack required at the world’s top companies. Our students have gotten jobs at McKinsey, Facebook, Amazon, Google, Apple, Asana, and other top tech companies! We’ve structured the course using our experience teaching both online and in-person to deliver a clear and structured approach that will guide you through understanding not just how to use data science and machine learning libraries, but why we use them. This course is balanced between practical real world case studies and mathematical theory behind the machine learning algorithms.

  • Programming with Python
  • NumPy with Python
  • Deep dive into Pandas for Data Analysis
  • Full understanding of Matplotlib Programming Library
  • Deep dive into seaborn for data visualizations
  • Machine Learning with SciKit Learn, including:
    • Linear Regression
    • Regularization
    • Lasso Regression
    • Ridge Regression
    • Elastic Net
    • K Nearest Neighbors
    • K Means Clustering
    • Decision Trees
    • Random Forests
    • Natural Language Processing
    • Support Vector Machines
    • Hierarchal Clustering
    • DBSCAN
    • PCA
    • Model Deployment
    • and much, much more!

Best for Beginner Python developers curious about Machine Learning and Data Science with Python

Course Duration: 44 hrs | Provider: Udemt

Price: Get amazing offers

Info: Visit this course and get amazing offeres on certification course

Mathematics for Machine Learning: Linear Algebra – Coursera

Rating: 4.7

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then will learn about vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally  Finally you will learn how to use these to do fun things with datasets – like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.

At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.

Skills you will Gain:

  • Eigenvalues And Eigenvectors
  • Linear Algebra
  • Transformation Matrix

Course Duration: 13 hrs | Provider: Imperial College London

Price: Free aduit course

Info: Visit this course and get amazing offeres on certification course

Mathematics for Machine Learning: Multivariate Calculus – Coursera

Rating: 4.7

This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. You will start with formulation of a slope, before converting this to the formal definition of the gradient of a function. Then learn to build up a set of tools for making calculus easier and faster.

Next, you will learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. You will know how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. You wiill laeran about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models.

Skills you will gain:

  • Linear Regressions
  • Vector Calculus
  • Multivariable Calculus
  • Gradient Descent

Course Duration: 18 hrs | Provider: Imperial College London

Price: Free aduit course

Info: Visit this course and get amazing offeres on certification course

Python for Data Science and Machine Learning Bootcamp – Udemy

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!

You will learn how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:

  • Programming with Python
  • NumPy with Python
  • Using pandas Data Frames to solve complex tasks
  • Use pandas to handle Excel Files
  • Web scraping with python
  • Connect Python to SQL
  • Use matplotlib and seaborn for data visualizations
  • Use plotly for interactive visualizations
  • Machine Learning with SciKit Learn, including:
  • Linear Regression
  • K Nearest Neighbors
  • K Means Clustering
  • Decision Trees
  • Random Forests
  • Natural Language Processing
  • Neural Nets and Deep Learning
  • Support Vector Machines

Course Duration: 25 hrs | Provider: Udemy

Info: Visit this course and get amazing offeres on certification course

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