Machine learning is one of the most fascinating, business intelligence course, and quickly developing areas of computer science, with origins deeply rooted in statistics. Numerous industries and applications may benefit from machine learning by becoming more intelligent and productive.
Among the many applications of machine learning models in daily life are chatbots, spam filtering, ad serving, search engines, and fraud detection. Finding patterns and developing mathematical models for things that are often hard for people to achieve is made feasible by machine learning.
Data analytics courses online concentrate only on teaching machine learning algorithms, how they work mathematically, and how to use them in a programming language, as opposed to data science courses, which also cover topics like exploratory data analysis, statistics, communication, and visualization techniques. It’s time to take your turn now.
The article contains the top five data analytics courses online.
Best 5 Machine Learning Courses
- Two Superb Book Partners
- Specialization in Deep Learning
- A Crash Course in Machine Learning
- Python-based Machine Learning
- Python with Machine Learning
Why Is A Machine Learning Course Excellent?
The article compiled the greatest machine learning courses accessible after watching the e-learning landscape for several years and enrolling in a tonne of courses from several platforms, including Coursera, Edx, Udemy, Udacity, and DataCamp.
Criteria
- The courses on the list must meet the following requirements.
- Concentrate only on machine learning.
- Use open-source, free programming languages like R or Python.
- Use libraries for those languages that are free and open-source. These courses are not taken into consideration since some teachers and providers utilize commercial software.
- Programming exercises to help you get to practice and real-world experience
- Justify the mathematical operation of the algorithms.
- Be accessible every month or so, on-demand, or at your speed.
- Have engrossing lecturers and teachers
- Have ratings and reviews that are above average
- Two Superb Book Partners
If you’re just getting started with machine learning, you might think about reading the following books in addition to enrolling in any of the video courses below:
- Statistical Learning Overview
- Interactive Machine Learning
- The top five courses in machine learning
- Machine Learning, first Coursera
All other machine learning courses are evaluated against this one. For the tasks, the course employs the open-source programming language Octave rather than Python or R. For those who find this to be a deal-breaker, Octave offers a straightforward approach to mastering the foundations of machine learning.
Courses include:
- Review of linear algebra
- Review of linear algebra
- Support vector machines
- Dimensionality reduction
- Anomaly detection
- Recommender systems
- Large-scale machine learning
- Regularisation
- Neural networks for representation and learning
- Advice for using machine learning
- Machine learning system design
Eleven weeks are used to cover everything. In around four months, if you can commit to finishing the entire course, you’ll have a solid foundation in machine learning. Then, with ease, you may go on to a subject that is more complex or specialized, such as Deep Learning, ML Engineering, or anything else that catches your attention.
- Specialization in Deep Learning
Each course uses the TensorFlow library for neural networks and the Python programming language for assignments and lectures. Being exposed to Python for machine learning while yet receiving a comparable lecture format makes this a suitable follow-up to a business intelligence course.
Courses include:
- Shallow Neural Networks
- Low Neural Networks
- A Crash Course in Machine Learning
This course is offered by Google AI Education, a platform that offers articles, videos, and interactive information for free. The subjects covered in the Machine Learning Crash Course are essential for quickly resolving ML issues. Python is the programming language of choice, and TensorFlow is introduced, just like in the prior course. A Google Collab-hosted interactive Jupyter notebook is included in each major portion of the program.
Courses include:
- Tensor flow, overfitting
- Tensor flow, overfitting
- Python-based Machine Learning
This course is offered by Google AI Education, a platform that offers articles, videos, and interactive information for free. The subjects covered in the Machine Learning Crash Course are essential for quickly resolving ML issues. Python is the programming language of choice, and TensorFlow is introduced, just like in the prior course. A Google Collab-hosted interactive Jupyter notebook is included in each major portion of the program.
Courses include:
- Tensor Flow
- Tensor Flow; Overfitting
- Training set,
- Splitting and validation
- Feature Engineering and data cleaning
- Feature Crosses
- Regularization – L1 and L2,
- Lambda; Model performance measures;
- Neural Networks – single and multi-class;
- Embeddings;
- ML Engineering;
- Reducing Loss – Gradient Descent, Learning Rates
- Python with Machine Learning
Another introductory course, but this one is entirely concerned with the most basic machine learning methods. The combination of the teacher, slide animations and algorithm explanations works quite well to give you a natural sense of the fundamentals.
This course makes use of Python and is less heavy on the algorithms’ underlying mathematics. You will have the opportunity to launch an interactive Jupyter notebook in your browser for each module to practice the new ideas you’ve just learned.
Wrapping Up
It’s a lot of fun and thrilling to learn about and play with machine learning, and hope you were able to find a business intelligence course that suits your path into this fascinating area. One element of data science is machine learning. A guide that resembles this one’s style is the greatest data science course if you’re also interested in studying statistics, visualization, data analysis, and other topics.