10 Best Machine Learning Courses to Go For

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10 Best Machine Learning Courses to Go For

Pankaj Bhadwal
Last updated on September 23, 2022

    Machine learning is that area of artificial intelligence that deals with granting machines the ability to learn on their own. It is a complex field that involves statistics, calculus, algebra, and more. Today, it has become one of the most lucrative career options. As such, more and more professionals and students want to enter it.

    There are several great ways to learn machine learning, and one of them is courses. Here, in this blog post, we are going to discuss ten of the best machine learning courses that you can leverage to advance your journey with ML.

    Top 10 Machine Learning Courses

    We have curated the following list of the top 10 machine learning courses that will help you learn and champion machine learning.

    1. Machine Learning, Data Science and Deep Learning With Python

    Machine Learning, Data Science and Deep Learning With Python

    By : Udemy

    Level : Beginner

    Duration: 15 hours and 36 minutes

    Instructor: Sundog Education Team by Frank Kane

    Certificate: Yes

    This is a comprehensive course that covers data science , deep learning, and Python, in addition to machine learning. At the time of this write-up, over 165k students have enrolled for the course.

    Data Science, Machine learning and Deep Learning With Python is one of the most popular ML courses on Udemy, with an average rating of 4.5 stars with over 27k reviews. The machine learning course explains a lot many things, including:

    • Building artificial neural nets with TensorFlow and Keras.
    • Designing and evaluating A/B tests using T-Tests and P-Values.
    • Making predictions using linear regression, polynomial regression, and multivariate regression.
    • Using Apache Spark’s MLlib to implement machine learning at a massive scale.

    In addition to explaining many of the core concepts of ML, the course includes developing two projects, namely a Pac-man bot for better understanding reinforcement learning and a movie recommendation system with item-based and user-based collaborative filtering.

    The complete course is segregated into 13 sections and 115 video lectures.

    Key highlights :

    • In addition to English, the ML course is available in 7 other languages, namely French, German, Indonesian, Italian, Polish, Portuguese, and Spanish.
    • You can access the machine learning course on mobile and TV.

    Sign up here to start learning the course.

    2. Feature Selection for Machine Learning

    Feature Selection for Machine Learning

    By : Udemy

    Level : Intermediate

    Duration: 5 hours and 49 minutes

    Instructor: Soledad Galli

    Certificate: Yes

    Feature Selection for Machine Learning is an intermediate-level course from Udemy. The course educates learners about embedded, filter, and wrapper methods for feature selection in ML. It is an ideal ML course to learn developing simpler, faster, and more reliable machine learning models.

    The course provides a detailed explanation of why fewer features are better than having more features in an ML model. The ML course also explains implementing various methods of feature selection using the Python programming language.

    This course consists of a total of 12 sections and 87 video lectures. In addition to that, the course on machine learning comes with 20 articles and 1 downloadable resource.

    As this is an intermediate-level course, learners need to meet some prerequisites that include Python and Jupyter Notebook installed on their systems. Additionally, interested candidates must have familiarity with Python programming, ML algorithms, and scikit-learn. Some experience with NumPy and Pandas is also required.

    Key highlights :

    • You can access the course on mobile and TV.
    • It includes a certificate of completion.
    • The ML course features full lifetime validity.

    To begin with this course, you can sign up here .

    3. Supervised Machine Learning: Regression and Classification

    Supervised Machine Learning: Regression and Classification

    By : Coursera (and DeepLearning.AI and Stanford University)

    Level : Beginner

    Duration: 33 hours

    Instructor: Andrew Ng, Aarti Bagul, Eddy Shyu, and Geoff Ladwig

    Certificate: Yes

    This beginner-level machine learning course specifically focuses on supervised learning. In this course, learners will develop supervised machine learning models for prediction and binary classification tasks.

    The course curriculum is divided into 3 weeks, as follows:

    • First Week: It focuses on the fundamentals of machine learning.
    • Second Week: It covers performing regression with multiple input variables.
    • Last Week: It focuses on classification.

    This complete course has 41 video lectures and comes replete with exercises. The course is put together by four instructors:

    1. Andrew Ng - He is the founder of DeepLearning.AI and a co-founder of Coursera.
    2. Aarti Bagul - She is a Curriculum Engineer and a top instructor on Coursera.
    3. Eddy Shyu - He is a Curriculum Architect and the Product Lead at DeepLearning.AI.
    4. Geoff Ladwig - He is a Curriculum Engineer at DeepLearning.AI and a top instructor on Coursera.

    The ML course explains building machine learning models using two of the most popular Python libraries , namely NumPy and scikit-learn. Moreover, it is one of the three courses in Machine Learning Specialization offered by DeepLearning.AI.

    Key highlights :

    • Financial aid is available for the course.
    • Over 30k students have enrolled for the ML course so far.
    • It comes with flexible deadlines.

    You can register here to start with this course.

    4. Machine Learning with Python

    Machine Learning with Python

    By : Coursera (and IBM)

    Level : Intermediate

    Duration: 23 hours

    Instructor: Saeed Aghabozorgi and Joseph Santacangelo

    Certificate: Yes

    Machine Learning with Python is an intermediate-level course on machine learning from Coursera. In addition to covering a diverse range of topics, the course comes replete with exercises and project work.

    This course is part of two certificates offered by IBM; IBM AI Engineering Professional Certificate and IBM Data Science Professional Certificate. The course is divided into 6 sections:

    1. Introduction to Machine Learning
    2. Regression
    3. Classification
    4. Clustering
    5. Recommender Systems
    6. Final Project

    In addition to machine learning fundamentals, the course covers Python libraries, hierarchical clustering, K-Means clustering, and regression. The instructors of this course are Saeed Aghabozorgi and Joseph Santacangelo. Both are data scientists at IBM.

    Thus far, the course has been taken by 271k+ students and has a 4.7-star rating from more than 12.5 reviews.

    Key highlights :

    • The course is available in 10 languages, including Arabic, French, Italian, and Spanish.
    • It has flexible deadlines.

    You can enroll in this course here to get started.

    5. Machine Learning Essentials for Business and Technical Decision Makers

    Machine Learning Essentials for Business and Technical Decision Makers

    By : AWS Skill Builder

    Level : Beginner

    Duration: 90 minutes

    Certificate: No

    Another popular ML course is Machine Learning Essentials for Business and Technical Decision Makers from AWS Skill Builder. The beginner-level ML course is intended for non-technical business leaders and business decision-makers involved in machine learning projects.

    Moreover, AWS designed this course for learners of the AWS Machine Learning Embark program and Machine Learning Solutions Lab (MLSL) discovery workshops. The course necessitates learners to have some basic knowledge of computers and machine learning.

    With this ML course, learners will be able to develop a sound understanding of the fundamentals of machine learning and evaluate the benefits and risks associated with adopting ML in different business scenarios. The course is divided into three parts:

    1. Introduction to Machine Learning: Art of the Possible
    2. Planning a Machine Learning Project
    3. Building a Machine Learning Ready Organization

    In addition to video lectures, the course on machine learning comes with presentations and knowledge assessments.

    Key highlights :

    • It is a completely free course.
    • It is a compact ML course with a total run time of only 90 minutes.

    Get started with this course freely by enrolling here .

    6. Machine Learning with Scikit-Learn

    Machine Learning with Scikit-Learn

    By : LinkedIn Learning

    Level : Advanced

    Duration: 44 minutes long

    Instructor: Michael Galarnyk

    Certificate: Yes

    This machine learning course focuses on Scikit-learn, which is among the leading Python libraries for predictive data analysis. The course details using the popular Python library for supervised and unsupervised forms of machine learning.

    This 44-minute long can be accessed on a tablet or phone. The course covers many machine learning concepts, including k-means clustering and principal component analysis . The following are the five different parts of the course:

    1. Introduction
    2. Input and Loading Data
    3. Supervised Learning
    4. Unsupervised Learning
    5. Conclusion

    The course on machine learning and scikit-learn is created by Madecraft, which is a full-service learning content company. The course instructor is Michael Galarnyk. He is a Python instructor and blogger.

    Key highlights :

    • It is one of the most compact courses to learn Scikit-learn.
    • You get a free option for this machine learning course.

    You can begin this course by signing up here .

    7. Machine Learning for Data Science and Analytics

    Machine Learning for Data Science and Analytics

    By : edX (and Columbia University)

    Level : Beginner

    Duration: 5 weeks (7 to 10 hours weekly)

    Instructor: Ansaf Salleb-Aouissi, Cliff Stein, and David Blei

    Certificate: Yes

    Machine Learning for Data Science and Analytics is an introductory ML course from Columbia University and Coursera. The course focuses on explaining the principles of machine learning and the significance of various ML algorithms.

    The course in machine learning requires candidates to have a good understanding of high-school mathematics and the basics of computer programming. With this ML course, students will learn:

    • How is machine learning related to data analysis and statistics?
    • Preparing data and dealing with missing data.
    • The most commonly used algorithmic techniques such as dynamic programming, greedy algorithms, and searching and sorting.
    • Using computer algorithms to search for patterns in data.

    The course is developed by 3 instructors; Ansaf Salleb-Aouissi, Cliff Stein, and David Blei. The trio is professors at the Department of Computer Science of Columbia University.

    Key highlights :

    • A free option for the course is available.
    • It is a part of the curriculum for the Professional Certificate in Data Science for Executives offered by Columbia University.

    For this course to begin, you must first register here .

    8. Introduction to Machine Learning

    Introduction to Machine Learning

    By : Udacity

    Level : Intermediate

    Duration: 10 weeks

    Instructor: Katie Malone and Sebastian Thrun

    Certificate: Yes

    Introduction to Machine Learning is a free ML course from Udacity. As this is an intermediate-level course, candidates are required to have a good experience in programming and a sound understanding of the basics of statistics.

    This course is part of the Data Analyst nanodegree offered by Udacity. The course educates learners on machine learning with real-world problems and use cases. The ML course combines instructor videos with exercises and a capstone project.

    The capstone project involves studying the Enron case, which was one of the biggest corporate frauds in America. Learners will mine email inboxes and financial data of Enron.

    With the successful completion of this course, candidates will be ready to analyze data using ML techniques. The entire course is divided into 10 lessons:

    1. Welcome to Machine Learning
    2. Naive Bayes
    3. Support Vector Machines
    4. Decision Trees
    5. Choose your own Algorithm
    6. Datasets and Questions
    7. Regressions
    8. Outliers
    9. Clustering
    10. Feature Scaling

    The intermediate-level machine learning course is delivered by Katie Malone and Sebastian Thrun.

    Key highlights :

    • It is a completely free ML course to avail.
    • The course comes with rich learning content and interactive quizzes.

    You can begin with this course by registering here .

    9. Machine Learning With Python Full Course 2022

    Machine Learning With Python Full Course 2022

    By : YouTube (and Simplilearn)

    Level : Beginner

    Duration: 10 hours

    Certificate: No

    This course is a YouTube video from Simplilearn. It is a comprehensive beginner ML course that educates learners about the various concepts in machine learning and how to leverage Python for accomplishing various ML tasks.

    The course covers a wide variety of machine learning concepts, including:

    • Fundamentals of machine learning.
    • Popular applications of machine learning.
    • Types of machine learning.
    • Linear algebra, calculus, and statistics.
    • Decision trees.
    • Regularization in ML.
    • Principal component analysis (PCA).

    To enhance the learning experience, the ML course performs a prediction analysis on the 2021 United States elections. The course is up-to-date, and so far, the video has accumulated almost 47k views and 1.2k likes. However, because it is offered as a YouTube video, there is no certificate of completion available.

    Key highlights :

    • It is completely free to take.
    • You need no prior experience with Python programming to get started with this course.

    There is no need to sign up for this course to get started.

    10. Machine Learning Course for Beginners

    Machine Learning Course for Beginners

    By : YouTube (and freeCodeCamp.org)

    Level : Beginner

    Duration: 9 hours and 52 minutes

    Certificate: No

    This is a comprehensive beginner-friendly course that combines the fundamentals of machine learning with practical examples. Hence, it is ideal for developing an in-depth understanding of ML.

    Offered by freeCodeCamp.org, Machine Learning Course for Beginners is available as a YouTube video.

    The course starts with the basics of machine learning and then goes into supervised and unsupervised learning, regression, and hierarchical clustering. The course comes with a total of 4 projects that ensure that the learners self-assess how much they’ve learned so far. These are:

    1. House Price Predictor
    2. Stock Price Predictor
    3. Heart Failure Prediction
    4. Spam/Ham Detector

    The course is fully flexible, and you can choose your own way to traverse it. However, note that as it is a YouTube video course, there is no certificate of completion available.

    Key highlights :

    • It is completely free to have.
    • So far, the course has accumulated 751k+ views and over 27k likes.

    You do not have to sign up to begin with this course.

    Conclusion

    That concludes our list of the 10 best machine learning courses. Although there are thousands of ML courses available, these are some of the best ones.

    However, note that there are other great media too to learn machine learning. This includes books, webinars, tutorials, and how-to guides. Therefore, to get the most out of your learning experience, you need to combine several different learning media. All the best!

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    FAQs


    The article consists of a list of the best machine learning courses available on different platforms, including Udemy, Coursera, LinkedIn Learning, Udacity, and edX. You can choose the course that fits your budget and learning requirements.

    Yes, learning Python for machine learning is beneficial because it provides numerous libraries, such as TensorFlow, Keras, Theano, Scikit-Learn, etc., that help you build ML and AI projects. Also, the simple and easy-to-understand syntax of Python makes it a go-to language for machine learning.

    Yes, machine learning is hard to learn because you need to have profound knowledge of a variety of computer science and mathematics concepts. Also, you have to pay attention to ML algorithms in detail to identify inefficiencies in them.

    Yes, you need a little bit of coding to learn machine learning and artificial intelligence.

    Yes, machine learning is definitely a good career in terms of personal growth and financial gain. In addition, the immense use of ML-powered devices or systems, like self-driving cars, recommendation engines, and many others, has resulted in the need for more skilled ML engineers.

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