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In the data domain, machine learning is growing in popularity. However, there’s a common misconception that an advanced degree is required to work as a machine learning engineer. But this isn’t entirely accurate. Because experience and abilities usually outweigh degrees.
If you’re reading this, you most likely want to work as a machine learning engineer since you’re new to the subject of data. It is possible that you are now employed as a BI analyst or data analyst and would like to transition to a machine learning position.
We’ve put up a collection of machine learning courses that are totally free to help you become proficient in the field, regardless of your professional objectives. We’ve included courses that will assist you in developing machine learning models as well as understanding the theory.
Master Machine Learning for All
Machine Learning for Everyone is a great option if you’re searching for accessible machine learning training.
Master Machine Learning course, taught by Kylie Ying, uses Google Colab to create engaging machine learning models by starting with the code. Learning just enough theory and creating models in your own notebooks is an excellent approach to become acquainted with machine learning. This course covers the following subjects and makes machine learning ideas understandable:
- Overview of machine learning
- K-Nearest Neighbors
- Naive Bayes
- Regression using logic
- Regression in line
- K-Means grouping
- Analysis of Principal Components (PCA)
Courses on Kaggle Machine Learning
Kaggle is an excellent site for practicing your model building techniques, participating in real-world data challenges, and developing your data science portfolio. The Kaggle team also offers a number of micro courses to help you brush up on the principles of machine learning.
The following are available as micro-courses. It usually takes a few hours to finish each course and go through the exercises:
- Overview of Master Machine Learning
- Advanced Feature Engineering and Machine Learning
The following subjects are covered in the Intro to Machine Learning course:
How machine learning models operate
- Data investigation
- Validation of the model
- Excessive and insufficient fitting
- haphazard forests
what you’ll study in the Intermediate Machine Learning course:
- Managing absent values
- Utilizing categorical variables
- ML workflows
- Verification by cross-validation
- XGBoost
- Data exposure
The course on feature engineering includes:
- mutual knowledge
- Making features
- K-Means grouping
- Analysis of Principal Components
- Encoding of the target
Using Scikit-Learn for Python Machine Learning
The creators of the scikit-learn core team have published a free self-paced course titled “Machine Learning in Python with Scikit-Learn” on the FUN MOOC platform.
It covers a wide range of subjects to assist you in learning how to use scikit-learn to create machine learning models. Every module includes Jupyter notebooks that go along with the video lessons. To get the most out of the course, you should be somewhat familiar with Python programming and Python data science libraries.
- Among the course material are:
- pipeline for predictive modelling
- Assessing the performance of the model
- Tuning of hyperparameters
- Choosing the ideal model
- model lines
- Models of decision trees
- group of models
Crash Course in Machine Learning
Another excellent resource for studying machine learning is Google’s Machine studying Crash Course. This course will teach you how to create machine learning models with the TensorFlow framework, covering everything from the fundamentals of model construction to feature engineering and beyond.
This course is divided into three main components. Master Machine Learning concepts portion contains the majority of the course material.
- Master Machine Learning Ideas
- Machine Learning
- Real-World Applications of ML Systems
You must be comfortable with Python programming, the command line, and high school arithmetic in order to enroll in this course.
The following are included in the section on ML concepts:
- Master Machine Learning fundamentals
- Overview of TensorFlow
- Engineering features
- Regression using logic
- Normalization
- Networks of neurons
Section on ML Engineering addresses
Machine Learning (CS229)
Thus far, we have observed classes that emphasize model creation while providing a taste of theoretical ideas.
Although this is a fantastic place to start, you will need to have a deeper understanding of how machine learning algorithms operate. This is crucial if you want to succeed in technical interviews, advance professionally, and pursue ML research.
Stanford University’s CS229: Machine Learning is one of the most well-liked and highly rated machine learning courses. You will gain the same technical depth in this course as you would in a university course lasting a semester.
The lectures and lecture notes are available online. The following general topics are covered in this course:
- supervised instruction
- Unsupervised education
- profound understanding
- Regularization and generalization
- Control and reinforcement learning
Final Thoughts on cources
I hope you were able to find useful materials to aid in your machine learning endeavours! You will get a solid foundation in both theoretical ideas and hands-on model creation with these courses.
If time is of the essence and you are already acquainted with machine learning, I suggest delving into Master Machine Learning in Python with scikit-learn for a thorough exploration of scikit-learn and CS229 for fundamental theoretical underpinnings.
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