The Best 30 Machine Learning Course Online – How to Start?

Have you ever thought about how ‘Netflix’ knows about the movie of your choice or how Gmail distinguishes the spam emails from your inbox? The answer is machine learning (ML). The reason behind the automation of their system is ML. Nowadays, machine learning is used in every system. So this is high time to enrich technological skills with Machine Learning Course Online. This article is written with the description of some online ML courses that will help learners to decide where to start.

Machine learning is an essential and extraordinary part of Artificial Intelligence. The definition of this technology is hiding in its name. Mainly machines are not intelligent. Humans have to give them instructions. But by using this technology, we can automate our make this nonintelligent machines. ML is the automation of any system to make that system capable of finding a solution without any direct given instruction. This automatic finding process works with previous experience.

To develop algorithms and data models for ML, we can use several languages like ‘R,’ ‘Python’ etc. But among all this, Python is the most used and popular language for machine learning. It has a lot of amazing libraries for ML. In this article, I will guide how to learn Python for ML.

Python Courses for Machine Learning

Python Courses for Machine LearningBasic language knowledge is essential to start machine learning. Python is one of the popular languages used in this field. Python has some magical features for ML. So here, some courses about python basics have been shared below. These courses will be a helpful resource for online machine learning course.

1. Programming for Everybody (Getting Started with Python)

Offered by: University of Michigan

This course is designed with the knowledge of some basic concepts of python programming. New people can use programming to start with this course, as this course provides basic programming knowledge. The mentor of this course is Charles Russell Severance. He is working as a Clinical Professor at the University of Michigan School of Information. This course has a sequel course that will complete learners’ python knowledge.

Features offered by this course

  • This course starts with an overview of programming. So people who are beginners can also be able to learn Python from this course.
  • Then comes knowledge about the installation of Python and which IDE should be used in this course.
  • Variables and expressions also explained here. So learners will able to gather knowledge about Python syntaxes from this course.
  • Conditional statements and loops are the contents of this course too.
  • Another essential topic function in Python has covered here.

2. Machine Learning Course Online: Python Data Structures

Offered by: University of Michigan

After acquiring knowledge from the 1st course, this course will bring the completion of your python knowledge. The same person also instructs this course, as mention in the previous course. To prepare for ML, this is a very well organized course. Both of these courses will be found in Coursera.

Features offered by this course

  • This course starts with the knowledge of string and its operations. The beauty of python string is described here.
  • Then it discusses file’s i/o operations, how to read, write or append in a file, and other important things related to it.
  • List, an important data structure in Python is covered in this course
  • The concept and operations of dictionaries will be cleared to the learners here.
  • Another important data structure list is the last topic in this course.

3. Python for Beginners – Learn Python Programming

Offered by: Programming with Mosh

This is a free 6-hour crash course on YouTube. Mosh Hamedani, a software engineer, instruct this course. Previously mention two courses are not free. It might be difficult for students to get access, so this free course is discussed. This course explains the basic python concept step by step. Completion of this course may clear your python concept, but it won’t bring certification to you.

Features offered by this course

  • This course begins with the introduction, installation of Python, and also teaching how to write 1st code in this language.
  • Then comes the variables part explaining the input function, converting variable types, string, and it’s operations.
  • Mathematical and logical operations are the next explained features here.
  • Conditional statements and loops are also explained.
  • Knowledge about defining functions, it’s parameters, return types are very clearly described here.
  • Important data structures like lists, files, tuples, dictionaries, and these data structures’ operations are practically shown in this course.
  • This course also includes python OOP knowledge, including modules and classes.
  • Lastly, the mentor provides some knowledge about machine learning, Django, and other features of Python.

4. Introduction to Data Science in Python

Offered by: University of Michigan

This course is all about introducing the learners about Python in data science. Famous python libraries that are broadly used in data science are the main content of this online machine learning course. The instructor for this course Christopher Brooks from the University of Michigan. It is a base course that a learner should complete before stepping forward to ML.

Features offered by this course

  • At the start of this course, basic python features, necessary for data science have been covered.
  • Introduction to Jupyter notebook and how to use this famous tool for Python are explained.
  • The next part of this course will introduce python data processing toolkits.
  • Different and important features of python library padas are discussed in this course. These features are Series, DataFrames, etc.
  • Important statistical concepts used in data science are shown here. This knowledge is very important to start the machine learning.
  • A good thing about this course is, it teaches the learners with some real-life data processing projects.

5. Become a Python Data Analyst

Offered by: Packt Publishing

If anyone enhances knowledge about python libraries for data analysis besides clearing the basic python concept, learning machine learning will be easier for that learner. This course provides knowledge about python libraries for data analysis. The instructor for this course is Alvaro Fuentes, a data scientist. He prepares the course content with step by step description and showing practice problem. In my opinion, this course provides enough knowledge about python libraries for ML.

Features offered by this course

  • This course provides proper knowledge about anaconda and Jupiter notebook before teaching the main topics.
  • The first library described in this course id Numpy, a famous python library to working with numbers and multidimensional arrays.
  • Secondly, another famous library, Pandas, is explained here. Pandas have been built based on Numpy.
  • After providing knowledge about numbers, thirdly, it tells about images. So famous image library matplotlib is explained here.
  • At this point, a very important library for machine learning is explained, and that is Seaborn. It is a library build using previously mentioned libraries.
  • The last library explained here is Scipy. Scipy is a massive library with a lot of sub-packages. One of them is statistical sub-package focused.
  • This course ends with an introduction to predictive models.

Machine Learning Course Online

Machine Learning Course OnlineAfter the basic knowledge, you will get lots of ideas to learn ML on the internet. In our subsequent study, we are narrating different online courses for ML. When you study all the review then you will get a clear idea to choose anyone.

6. Making Predictions with Data and Python

Offered by: Packt Publishing

This online ML course was also taught by the data scientist Alvaro Fuentes, the mentor from the previous course. The main motive here is to help learners to create a predictive machine learning model using Python, and it’s the library. The duration of this course is four hours+ and also available on Udemy.

Features offered by this course

  • This course provides the python library knowledge, which is necessary to build predictive knowledge.
  • Step by step guidance to install anaconda and Jupyter notebook is given before the main content.
  • It provides theoretical knowledge for building real-life ML models for prediction.
  • During this course, different types of predictive models will appear to the learner, so that he/she can have proper knowledge about prediction.
  • The use of scikit -learn in this course is remarkable. Scikit -learn has different types of data models inside it.

7. Machine Learning Foundations: A Case Study Approach

Offered by: University of Washington

This course is an online machine learning course from Coursera with a perfect rating. Two expert instructors designed this course, such as this course can give a good overview of ML. The instructors of this course are Emily Fox and Carlos Guestrin. Both of them are Amazon Professor of Machine Learning. The attraction of this course is that it gives a balanced knowledge of python libraries and statistics.

Features offered by this course

  • The course starts merely defining what ML is and with an introduction of this entire course and instructors.
  • Clarify the MLconcept by building predictive models.
  • Describe the classification model. Here build a classifier and analyze the accuracy of that classifier instead of making an intelligent system.
  • This course explains how to create a model that can find similarities among objects and cluster those based on that similarity.
  • This course teaches how to work with data and provides knowledge about how to deal with images in data science.

8. Machine Learning Course Online: Regression

Offered by: University of Washington

This course will help a learner to acquire knowledge about one of the important and popular ML tool that is Regression. Regression makes predictions based on the relationship between data. This machine learning course is instructed by two Amazon Professor of Machine Learning named Emily Fox and Carlos Guestrin. After completing this course, learners will understand the concepts of Regression very well.

Features offered by this course

  • At the starting of this course, the basic concept of Regression is explained.
  • After clarifying the content of Regression, it describes simple linear Regression. How to build a simple regression data model and trained that with regression algorithm. At this part, only one characteristic of data has been considered here.
  • The next part is organized to discuss multiple attributes of data. So multiple Regression, it’s data model is the main content of this part of this online machine learning course.
  • Another type of Regression explained here is ridge regression. This complex concept of ML has been described easily here.
  • Nonperiodic methods like neighbor regression or kernel regression are also discussed in this course.
  • This course organized by building different regression models with real-time problems and also assessing their performance. So completion of this course will help a per to understand everything about Regression.

9. Machine Learning Course Online: Classification

Offered by University of Washington

Classification is another useful method in the machine learning field. The use of this popular model is remarkable. This course is one of the well-explained ML course for understanding classification models properly. The instructors for this course are two professors of machine learning called Emily Fox and Carlos Guestrin. This course is available in Coursera.

Features offered by this course

  • This course starts by explaining the basic concept of Classification. It describes how built classifiers are predicting about the categories of an object.
  • It has explanations about linear classifiers and Logistic Regression.
  • This course allows the learner to implement own classifier so that he/she can have a clearer understanding.
  • Thinking about real-life problems, this online ML course adds discussion on how to deal with missing data. As real-life problems consist of missing data, it has proven very helpful to the learner.
  • Building data models for the classifiers, training them, and testing them with those classifiers are also part of this course.
  • This course can provide useful knowledge related to the classification method of ML.

10. Machine Learning: Clustering & Retrieval

Offered by: University of Washington

This machine learning course is instructed by Emily Fox and Carlos Guestrin, professors from the previous three courses. This course is all about predicting similarities and Clustering for retrieval. It is one of the important concepts for ML. This course, along with previously mentioned three courses, is a specialization package. Completing these four courses will help the learner be a specialist in ML and get a specialization certificate offered by the university of machine learning. Without any doubt, this is an excellent opportunity for anyone.

Features offered by this course

  • This course discusses the similarity of the object and near neighbor search.
  • Different type of clustering algorithms are discussed with ML.
  • Big data and data mining concepts like Latent Dirichlet Allocation, k-mean have been used here.
  • Experiments on real-time data and their structure is a remarkable point for this course.
  • After learning Regression and Classification, knowledge of this course can make anyone an expert in ML.
  • To understand this online ML course correctly, knowledge of Regression and Classification is essential.

11. Python for Data Science Essential Training

Offered By: LinkedIn Learning

This LinkedIn Learning is a well-rated course by LinkedIn Learning. This course has two parts with a good number of practice problems. This course is instructed by an expert LinkedIn data science specialist named Lillian Pierson. This course is organized with step by step project guideline to prepare a learner for customizing own project. By discussing different models, this online machine learning course is one of the organized ML courses for anyone.

Features offered by this course

  • This course starts describing why a developer should choose Python than any other language for machine learning. The usefulness of Python describes shortly here.
  • Data transformation, processing, and visualization with Python is another topic of this course.
  • This course also includes data plotting and statistical analysis and creating graphs based on that analysis.
  • Basic math, linear algebra, correlation, and multivariate analysis with Python are also described here.
  • Another exciting thing about this course is it provides an introduction to natural language processing.
  • It has an excellent clear overview of what ML is, the data models stand for it, and other essential terms’ descriptions.
  • Popular machine learning methods like linear Regression and Logistic Regression are part of this course too.
  • This course teaches about clustering models using the k-mean method and hierarchal models.
  • At last, learners can acquire knowledge about neural networks from this course.

12. Artificial Intelligence Foundations: Machine Learning

Offered By: LinkedIn Learning

This online ML course is another high rated course from LinkedIn Learning. It discusses one of the important subsets of artificial intelligence, and that is ML. The instructor for this course is Doug Rose. He is a professional trainer in LinkedIn and explained the foundation very well.

Features offered by this course

  • This ML course describes the working strategy of data and application of Machine learning.
    Different ways of starting ML have been suggested here. So it will be easy for anyone to choose the best one.
  • It not only describes various types of popular machine learning algorithms but also differentiates among them.
  • Using these algorithms and the problems that occur while using them is also a remarkable part of this course.
  • The best thing about this course is it provides knowledge that will help learners to choose proper ML algorithm as his/ her need.

13. Machine Learning Course Online With Python

Offered by: IBM

This is a beginner level online ML course from Coursera. The main motive of this course is to clarify the ML concept using famous language python. The mentors for this course are two IBM data scientists Saeed Aghabozorgi and Joseph Santarcangelo. They explain why to use ML and how it can contribute to real-life problems. Solving these problems using ML can be amazing.

Features offered by this course

  • The course introduces its learners to ML and how the application of this technology in different sections is contributing.
  • After explaining the ML concept, this course comes with the basic idea of the regression method. It discusses linear, non-linear, simple, and multiple Regression and applies those on different datasets.
  • The next topic in this course is another famous machine learning method classification. This section includes various classification algorithms like k-nearest neighbors, super vector machines, Logistic Regression, and decision trees.
  • After covering Regression and Classification now, it focuses on different types of clustering approaches like Hierarchical Clustering, Partitioned-based Clustering, and Density-based Clustering.
  • This course discusses the recommender system.
  • The main motive of this course is to help learners understand ML.

14. Applied Machine Learning in Python

Offered by: the University of Michigan.

This course is another excellent rated course from Coursera. The main concern for this online ML course is applied machine learning, a beneficial topic nowadays. Statistical analysis of data is descriptively explained in this course. The instructor of this course is Kevyn Collins-Thompson. He is an associate professor from the University of Michigan.

Features offered by this course

  • This course merely starts introducing the fundamentals of ML. Terms related to machine learning are discussed here with examples.
  • The introduction of the famous python library scikit-learn and its implementation are discussed in this course.
  • Supervised and unsupervised learning, two categories for ML has been covered in course.
  • It supports various machine learning methods like vector ML.
  • After learning the basic concept of the Support Vector Machine method, there some advanced level knowledge is provided by this course.
  • This course requires some basic python and data science knowledge to understand the course contents properly.

15. Python for Data Science and Machine Learning Bootcamp

Offered by: Jose Portilla

This is a highly rated and quality online machine learning course available in Udemy. This course is a complete package of data science in Python. It includes data analysis, visualization with Python, and the use of powerful ML algorithms. This course designed such a way that both beginners and developers can be helped through this course. The instructor for this course is Jose Portilla.

Features offered by this course

  • This online ML course starts discussing the introduction of Python programming power in machine learning.
  • How to analyze, visualize, and use data with Python in data science explained here.
    Famous python library NumPy is has been described here.
  • If it covers essential concepts of python library pandas. DataFrames, series, handling CSV file with pandas.
  • For data visualization, the use of matplotlib and seaborn’s used have been shown here.
    Different ML methods linear Regression, K Nearest Neighbors, K Means Clustering, Support Vector Machines are adequately explained in this course.
  • Famous python library scikit-learn used in these algorithms given in the previous point.
    Natural Language Processing has been discussed in this course.
  • This online ML course provides an introduction to neural networks and deep learning.

16. Machine Learning A-Z™: Hands-On Python & R In Data Science

Offered by: Udemy

This online ML course guided the learner step by step to the field of ML. Two data scientists Kirill Eremenko and Hadelin de Ponteves, are the instructors for this course. This is one of the bestsellers and a highly rated course of machine learning in Udemy. The beauty of this course is the completion of a new section can add a new skill to learners. SuperDataScience Team and Support are also connected with this course.

Features offered by this course

  • This course starts with explaining what ML and its application effects in real life.
  • The best part of this course is that it can introduce learners with data science in two languages Python and R. Data processing in R and Python is available here.
  • Different types of Regression, including simple Linear Regression, Multiple Linear Regression, etc. are discussed here.
  • Another famous method classification is also part of this course. Logistic Regression, K-NN, SVM, and other methods of Classification are introduced here.
  • This online machine learning course discusses clustering methods like K-Means, hierarchical Clustering.
  • The introduction of natural Language Processing and deep learning is a bonus for this course.

17. Machine Learning, Data Science, and Deep Learning with Python

Offered by Sundog Education by Frank Kane.

Sundog Education offers another high rated online ML course in Udemy. Anyone having programming or scripting language knowledge will help to enhance the experience beautifully withml or data science knowledge. The instructor for this course is Frank Kane, the founder of Sundog Education. This course is for developers, data analysts, and others who want to know more about data science and ML.

Features offered by this course

  • In the beginning, the installation process of Python has been shown to the learners.
    Basic Python and its library knowledge are explained step by step. So the learner can be more.
  • Relevant statistics and probability concepts for data science have been discussed using python programming.
  • After covering the mentioned basic knowledge, different predictive models like Regression are shown in this online machine learning course.
  • Then essential core concepts of ML have been introduced here.
  • This course has content on data mining and data science or ML techniques in data mining.
  • Machine learning in big data is also mentioned here.
  • The beautiful part of this course is this course emphasizes real-world data and experiments.
  • Lastly, there is part of the neural network and deep learning.

18. Data Science: Machine Learning

Offered by: Harvard University

This package is a useful online ML course for anyone offered by Harvard University. Though this course has a regional restriction, it is available in Bangladesh. Professor Rafael Irizarry has instructed this course. He is a Professor of Biostatistics, Harvard University. This course explains the science behind different magical data science technologies.

Features offered by this course

  • This course successfully introduces the beginner with the fundamentals of ML technologies.
  • This course distinguishes between ML and other data science methodologies.
  • It explains why ML is the most popular methodology in data science.
  • Popular ML algorithms and components analysis are discussed here.
  • It helps the learner to create their recommendation system with their own acquired knowledge from this course.
  • This course has a discussion on training and testing data for building predictive models using ML.

19. Machine Learning with Python: A Practical Introduction

Offered by: IBM

The course from the Edx learning site is an educative course on ML that covers both supervised and unsupervised machine learning knowledge. This is a highly rated online machine learning course. Saeed Aghabozorgi, a data scientist from IBM, is the moderator for this course. The basic ML concepts with Python are explained here beautifully.

Features offered by this course

  • An overview introduction of the ML fundamental concept is the starter for this course.
  • Python libraries that are mostly used in ML have been explained here.
  • The applications that are blessed with ML are also found here.
  • It provides knowledge about supervised and unsupervised machine learning.
  • Different types of regression models like Linear Regression, non-linear Regression, model evaluation methods are the content of this course.
  • Another popular method classification, including K-nearest neighbor, decision trees, logistic
  • Regression, support vector machine algorithms, is described here.
  • Different types of clustering methods are also explained here.

20. Machine Learning Fundamentals

Offered by: The University of California, San Diego

Another online ML course from learning site Edx is this. This course is about discussing the fundamental concepts of ML. The instructor for this course is
Sanjoy Dasgupta, a professor from The University of California, San Diego. Beyond the regional restriction, this course is available in Bangladesh. It is a useful course for the beginner of ML.

Features offered by this course

  • This course holds knowledge about supervised and unsupervised machine learning algorithms.
  • It has covered the concept of Classification, Regression, conditional probability estimation, Clustering, and other important concepts.
  • The real-world case studies enhance the ML knowledge of this course.
    This course focuses on analyzing different types of data.
  • Different types of descriptive and predictive models are built based on data analysis.
    This course has also worked on images.

21. Machine Learning with Python: from Linear Models to Deep Learning

Offered by: MIT

Machine learning has been proven as one of the most remarkable and extraordinary modern technologies. This course wants to bring out all the techniques and algorithms behind this course quickly to the learners. The instructors for this course are Regina Barzilay, Tommi Jaakkola, and Karene Chu. All of them from MIT. This online ML course contains knowledge from linear models to deep learning.

Features offered by this course

  • The introduction of ML is the first concept covered by this course.
    This online ML course covers some supervised and unsupervised algorithms for learners.
  • Different types of models are discussed here. Which model is suitable for a specific application has been explained here.
  • Deep learning, neural network, natural language processing concepts are also discussed in this course.
  • Here several projects are planned to enhance learners’ knowledge about ML.

22. Applied Machine Learning: Foundations

Offered By: LinkedIn Learning

Applied MLone of the more exceptional technology nowadays; people from all over the world are focusing. This course is an online machine learning course to learn and explore MLmore. The instructor of this course is Derek Jedamski, a skill data scientist. This course can be proved helpful to both beginner and intermediate level of learners.

Features offered by this course

  • A learner will be able to answer what ML is after the completion of this online ML course.
  • This course can be helpful for learners to distinguish between machine learning and deep learning.
  • The common challenges are faced, usually while working with MLis also discussed here.
  • Data overfitting and underfitting concepts are included in this course.
    This course has content about evaluating models.

23. Machine Learning & AI Foundations: Linear Regression

Offered By: LinkedIn Learning

Linear Regression has been known as one of the most popular ML methods. This course is all about this popular method. It is a well-reviewed course from LinkedIn Learning. This course instructed by a data miner called Keith McCormick. This course will help the learners to identify different regression techniques used in our daily life applications.

Features offered by this course

  • Different types of regression techniques are the main concern for this online ML course.
  • Building effective scatter plots for regression techniques have been shown in this course.
    This course discusses the challenges and assumptions that are usually faced with multiple regression techniques.
  • It guided the learner in such a way that he/she could solve real-life problems.
    In the end, the instructor has mentioned some alternative strategies over the regression technique.

24. Machine Learning Course Online by Stanford University

Offered by: Stanford University.

Machine learning brings the capability of working without instruction on a computer. So nowadays the use of this technology is remarkable to everywhere. Stanford University offers the highest rated online ML course at Coursera. This course instructed by one of the top instructors Andrew Ng. He is a professor from Stanford University.

Features offered by this course

  • The first topic in this course is ML introduction to the learners.
  • Different types of linear regressions are explained descriptively in this online ML course.
  • Classifying method logistic regression and its applications have been shown with examples in this course.
  • This course contains some neural network concepts.
  • This course advises about applying this technology and also designing systems using it.
  • The popular ML algorithm discussed in this course.

25. Complete Machine Learning and Data Science: Zero to Mastery

Offered By: Udemy

If anyone doesn’t have any basic knowledge but wants to start with data science, then it is a perfect course he/she is looking for. This course begins with basics and slowly go deeper to make any learner expert in data science. The instructors for this course are Andrei Neagoie and Daniel Bourke. They both are popular Udemy instructors.

Features offered by this course

  • Like other online ML courses, this course also starts with an introduction to machine learning.
  • It discusses different data science working environments and installation and setup on those environments.
  • Data analysis descriptively explained in this course.
  • Numpy and Pandas, two famous libraries, have been used to provide knowledge about these libraries’ works on numbers and strings.
  • Plotting graphs or images and data visualization have taken place here. Matplotlib is used for that
  • Different supervised algorithms, like Regression and Classification, are discussed descriptively in this course.
  • This course contains advanced statistics and mathematics knowledge for data science.
  • It has something about neural networks and deep learning.

26. Introduction to Machine Learning for Data Science

Offered by: The Backyard Data Scientist

Machine learning has been proven as the most popular data science methodology. This online machine leaning course is all about introducing this technology for data science. The instructor for this course is David Valentine from The Backyard Data Scientist. The core concepts of data science have been discussed in this course. It is a preferable course to understand ML concepts for data science.

Features offered by this course

  • At the beginning of this course, some core concepts of data science like AI, ML, big data, etc. have been clarified.
  • The effect of ML in our life and also its importance are mentioned here.
    How we can apply ML in data science with preferable languages are discussed here.
  • It is mainly a theory base course than practical work. This course will help the learner gain knowledge about some important concepts that can help understand data science appropriately.
  • It discusses ML with Python and installation regarding the topic.

27. Machine Learning for All

Offered by: University of London

This course aims to help all learners to start with ML, even though having no previous basic knowledge before. The instructor for this course is Dr. Marco Gillies from the University of London. Some programming concepts have been covered by him so that people having no programming knowledge can start through this course. It is a short course to understand the only basic concept of ML.

Features offered by this course

  • Artificial intelligence and ML necessary explained at the start.
    Effect of data representation discussed in this course.
  • Real-life applications of ML and its practice have been described in this course.
  • This course guided its learners from collecting data to completing the first ML project.
  • This course emphasizes practical experiments for training data.

28. Learn Machine Learning By Building Projects

Offered By: Eduonix Learning Solutions
The main focus of this online machine learning course is to clear the concept of this technology by building projects. Eduonix Learning Solutions have instructed this course. Basic Python programming knowledge is essential for this course. It explains different ML methods by showing how to use these methods in various types of projects.

Features offered by this course

  • The most attractive part of this course is that it has covered 12 different machine learning courses here.
  • These projects are based on various machine learning algorithms.
  • It can provide hands-on experience of ML to its learners.
  • This course starts with very beginner level concepts, and in the end, it reaches an expert level of ML knowledge.
  • People who don’t like the theory can choose this course to become an expert in this technology.

29. Machine Learning Certification Training using Python

Offered By: Edureka

This course can be proven as one of the great online ML courses for understanding data science. Data science is nowadays days holding the most important in different technological sides. It is a course to help anyone in this field. Python fundamental and data analysis knowledge is necessary to start this course. This certification course has been designed for professionals to make a remarkable change in their data science skills.

Features offered by this course

  • At the start, the basic concepts of data science have been explained in this course.
    Then come the machine learning’s introduction with popular programming language Python.
  • Supervised machine learning methods are correctly defined in this online ML course.
  • It contains a description of unsupervised learning with relevant explanations.
    How to prepare ML that guidelines are provided in this course.

30. Understanding Machine Learning with Python

Offered By: Pluralsight

Building future predicting models using ML is often noticed in technology. This course from Pluralsight has been designed to help learners in this topic. The instructor for this course is Jerry Kurata. He has explained easily to learners how anyone can perform ML using Python.

Features offered by this course

  • After completion of this course, anyone will be able to explain what ML is.
    Installations regarding this topic, like the Jupyter notebook and Python installation, are shown for the learners.
  • How to prepare or process data for ML has been explained here. The use of GitHub makes this course unique.
  • It has guidelines regarding perfect machine learning algorithm selection.
  • It provides knowledge on how to test and train data for a proper ML model.

Final Thought

We have tried to cover almost all the Machine Learning Course Online that will help a learner start. You may have a lot of queries to clarify that you may contract with me. If I miss any other essential course, please let me inform you of the comment. In my next update, I will address all of your queries.

Hawlader's passion for technology has driven him to be an avid writer for over 16 years. His vast knowledge of the Windows and Android operating systems is a testament to his proficiency in the field. In addition to his expertise in open source software, he also possesses an extensive understanding of the open-source platform, making him a valuable resource for technology enthusiasts. His contributions to FossGuru writers with research-based articles have helped readers to stay up-to-date with the latest trends in the tech industry. Furthermore, Hawlader's curiosity for scientific breakthroughs has led him to be a keen reader of science blogs, keeping him informed about the latest developments in the field.

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