New research shows that machine learning abilities are in high demand. In part, this is because machine learning is becoming more relevant in a wide range of businesses. Free machine learning courses may help you keep up to speed with industry standards.
As a beginner in machine learning, this blog is perfect for you! The following selection of free machine learning courses will get you started on your path to mastery.
The best free online courses in machine learning
In recent years, the need for machine learning has grown dramatically. Free machine learning courses may help you go ahead in your job by enhancing your skills. This selection of free online courses on machine learning can help you learn more about the field and improve your existing abilities. We are ready to go now.
1. A brief introduction to machine learning
Today, machine learning is extensively applied across a broad range of businesses. Machine learning is utilized in various applications, from recommendation systems to spam filtering. Businesses may gain a competitive advantage by making sense of the ever-increasing quantity of data accessible. Enrolling in this free online course on machine learning principles will help you better grasp the subject matter.
- Machine learning: a brief introduction
- Linear regression
2. Python for Machine Learning
As one of the most widely used programming languages, Python is known for its high-level syntax and ease of usage. In Machine Learning, Python is a strong programming language. Starting with the NumPy library, we’ll learn about Python’s machine learning capabilities. Additionally, we’ll learn about a Python library called Pandas. A breakdown of the course’s content is as follows:
- The basics of NumPy
- A NumPy Array Combiner
- Differences in NumPy’s Intersection
- Array Math using NumPy
- NumPy Array saves and load
- An introduction to Pandas
- Pandas Series Object
- Pandas Dataframe Overview
- Pandas Functions
3. Machine Learning Statistics
Analysis, interpretation, presentation, and organization are all aspects of statistical work. It is possible to build a solid basis for data analysis by using statistics to machine learning. This free online statistics for machine learning course covers the fundamentals of descriptive statistics and data visualization.
The course syllabus:
- Descriptive statistics: an overview
- Histograms and data
- The three M’s and the central tendency
- Standard deviation
- Coefficient of variation
- Insights into Data
- Analysis of Correlation
- Python-based descriptive statistics
4. Machine Learning Algorithms
Today, machine learning can be found in practically every business, and it’s growing in popularity. The strength and efficiency of a well-tuned machine learning model may be really impressive. However, this is only achievable if the algorithms are provided with data. Understanding machine learning algorithms can help you thoroughly apply supervised and unsupervised learning ideas. Among the topics covered in this free course on machine learning algorithms are the following:
- Machine learning: a brief introduction
- Types of machine learning
- How does a machine learning model acquire knowledge?
- Algorithms for linear regression
- Naive Bayes algorithms
- Algorithms KNN
- Vector support machines
- The random forest algorithm
5. Machine Learning in Finance
The application of machine learning is becoming more and more commonplace in almost every business. By integrating machine learning in finance, we can improve operational efficiency in various areas, from risk management and trading to insurance underwriting. Machine learning has had a positive impact on the banking industry. If you’d want to learn more about the many applications of machine learning in finance, you may take this free online course. Beginners who want to learn more about the topic should take this course.
6. K-means-based Machine Learning
Unlabeled datasets are often studied using the k-means method. It’s common practice in machine learning to use unsupervised learning. K-means clustering, NumPy, pandas, and scikit-learn are just a few of the unsupervised learning models you’ll encounter in this free online course on machine learning and unsupervised learning using k-means.
7. Supervised Machine Learning using Logistic Regression and Nave Bayes
In supervised machine learning, a collection of training data is used to educate a computer to detect patterns. Each time a training data set is submitted to the machine; it receives feedback on its performance from its supervisor. This free online supervised machine learning course will teach you about logistic regression and naive Bayes techniques.
8. Python Machine Learning Libraries
It is possible to execute specialized tasks in your Python code by using libraries, which are just collections of modules. For example, you may do mathematical operations using the modules in the math library. Learn about some of the most extensively used Python libraries such as NumPy, seaborn, and matplotlib in this free course on Python libraries for machine learning.
9. Interview Questions for Machine Learning
This online machine learning interview questions and answers course is perfect if you need to brush up on your interview skills. You will better understand the types of interview questions by taking this course. It will give you a refresher on machine learning topics and help you obtain the job of your dreams.
10. Tree-Based Models for Supervised Machine Learning
Tree-based classifiers may be used to get insights from raw data and make significant judgments. This online supervised machine learning using tree-based models course is designed to help you learn about their significance. Guided learning methods like random forests and decision trees are covered and how to put them into practice in Python.
11. AWS Machine Learning Overview
This introductory AWS machine learning training is free and open to the public. This course will teach about cloud computing, its purposes, and the major cloud providers, including Amazon Web Services (AWS). This course will discover how AWS and machine learning works together. Data science, machine learning, cloud computing, and AWS services will all be learned by the end of the course.
There is a great need for those who are skilled in machine learning. You may get a well-paying career by taking advantage of free machine learning classes. Alternatively, you might pursue a master’s degree in machine learning and learn more about the subject.