Machine learning has rapidly gained popularity in recent years as it’s become a key driver of innovation in fields such as healthcare, finance, and technology. Python has also established itself as a dominant language in the world of machine learning. While Python is not the only language for machine learning, it is arguably the most popular and widely used. In this article, we will explore the necessity of learning Python for machine learning and discuss alternatives for those looking to venture into the world of artificial intelligence.
Digitalization, Artificial Intelligence and Big Data allows us more than ever before to make use of the data our business generates every day. Companies that want to accelerate their business are increasingly turning to such methods and technologies to help them solve complex problems, promote efficiency and improve performance and decision-making.
Benefitting from these requires not only capable data analysts and computer scientists. It also requires visionary management that is prepared to apply them to their business and adapt their organization accordingly.
TU Delft offers courses and programs that cater to the needs of a wide range of professionals, including managers and business leaders, interested in gaining new skills, tools and insights in these fields.
Python is one of the most widely used programming languages in the Machine Learning field. Python has many packages and libraries that are specifically tailored for certain functions, including pandas, NumPy, scikit-learn, Matplotlib, and SciPy. So if you want to learn Machine Learning with Python, this article is for you. In this article, you will find the 12 Best Online Courses for Machine Learning with Python.
Python: The Dominant Language of Machine Learning
Python has become the de facto programming language for machine learning for several compelling reasons. One of the primary factors contributing to its popularity is the rich ecosystem of libraries and frameworks available. Libraries like TensorFlow, Keras, and PyTorch provide a user-friendly and robust environment for building and training machine learning models. Python’s versatility and simplicity make it accessible to beginners and experts alike, which is especially important for students and professionals looking to enter the field of machine learning.
Python’s community support is another strong point. The abundance of online resources, forums, and tutorials makes it easier to learn and troubleshoot machine learning projects. Its compatibility with various platforms and systems is yet another advantage, ensuring that you can develop and deploy machine learning models with ease.
The C Machine Learning Library
While Python is the go-to language for most machine learning projects, it’s not the only option. C, a low-level programming language, is another choice for machine learning enthusiasts. Despite being less popular, C has its merits when it comes to machine learning.
C allows for faster and more efficient execution of algorithms compared to Python. This is crucial in situations where computational speed is of the essence, such as real-time applications or large-scale data processing. When building machine learning libraries or frameworks themselves, some developers prefer to use C for the performance benefits it offers.
However, it’s essential to note that using C for machine learning typically involves more complex coding and a steeper learning curve. Python’s high-level abstractions and vast library ecosystem make it more accessible for beginners, but C may be an option for those who require top-tier performance.
Online Python Course for High School Students
As machine learning becomes increasingly relevant across various industries, there is a growing demand for high-quality educational resources. High school students, in particular, are showing interest in learning machine learning with Python. Many online courses are tailored to meet the needs of students looking to gain a solid foundation in this field.
These courses often cover the basics of Python programming, data analysis, and machine learning concepts. They typically employ a project-based learning approach, allowing students to work on real-world problems and gain practical experience. By learning Python and machine learning during high school, students can develop valuable skills that will serve them well in their academic and professional careers.
Hands-On Machine Learning with Python
The best way to learn machine learning with Python is through hands-on experience. “Hands-On Machine Learning with Python” is a widely recognized book by Aurélien Géron, which has become a staple resource for aspiring machine learning practitioners. This book provides a practical approach to machine learning, guiding readers through real-world projects and challenges.
Aurélien Géron’s book covers a wide range of topics, from basic concepts to advanced techniques in machine learning. It uses Python’s popular libraries, such as scikit-learn and TensorFlow, to demonstrate key concepts. The book’s interactive and project-based approach helps readers build their machine learning skills effectively, making it a valuable resource for both beginners and experienced practitioners.
How Long to Learn Machine Learning with Python?
The time it takes to learn machine learning with Python varies from person to person, depending on prior experience, dedication, and the specific goals of the learner. Here are some general guidelines to help you understand what to expect:
- Basic Proficiency: If you already have some programming experience, you can gain a basic understanding of machine learning in a few months. Online courses and books can help you learn the fundamentals of Python and machine learning.
- Intermediate Level: To become proficient and start working on real projects, it may take around 6 to 12 months of consistent learning and practice. This stage involves mastering libraries like TensorFlow and scikit-learn and gaining experience with various machine learning algorithms.
- Advanced Expertise: Achieving advanced expertise in machine learning can take several years of continuous learning, experimenting, and working on complex projects. Becoming an expert in the field may require a deep understanding of mathematical concepts and algorithm design.
In conclusion, learning Python for machine learning is not an absolute necessity, but it is a highly recommended choice due to its popularity, versatility, and ease of use. High school students and individuals of all levels can find numerous resources to start their journey into the world of machine learning with Python. It’s a field with significant potential, and the time invested in learning it can lead to exciting opportunities and advancements in various domains.
These courses are filtered out on the following criteria-
Criteria-
- Rating of these Courses.
- Coverage of Topics.
- Engaging trainer and Interesting lectures.
- Number of Students Benefitted.
- Good Reviews from various aggregators and forums.
Now, without wasting your time, let’s start finding the Best Online Courses for Machine Learning with Python.
1. Machine Learning with Python– Coursera
| Provider- | IBM (Coursera) |
| Rating- | 4.7/5 |
| Time to Complete- | 22 hours |
This course has a 6-week study plan. In the first week, you will understand the basics of machine learning and in the next week, you will learn Regression, simple linear regression, and multiple linear regression.
The third and fourth week is all about classification where you will learn K-Nearest Neighbours, Decision Trees, Logistic Regression, Logistic regression vs Linear regression, and Support Vector Machine.
After that, you will learn clustering and k-Means clustering. The complete course has various quizzes and exercises. To excel in this course, it is good to have some previous math knowledge.
Extra Benefits-
- You will get a Shareable Certificate. Along with that, you will earn an IBM digital badge.
- Along with this, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, Graded Programming Assignments
Who Should Enroll?
- This course is good for beginners in Machine Learning, who wanna learn Machine Learning with Python.
Interested to Enroll?
If yes, then check out the details here- Machine Learning with Python
2. Intro to Machine Learning with TensorFlow– Udacity
| Provider- | Udacity |
| Rating- | 4.7/5 |
| Time to Complete- | 3 months (if you spend 10 hrs/week) |
This is a Nano-Degree Program. There are 3 courses in this program. In the first course, you will learn supervised learning and the algorithms of supervised learning such as Regression, Perceptron Algorithm, Decision Trees, Naive Bayes, SVM, etc. Along with the supervised algorithms, you will also understand the training and testing procedure and data visualization basics.
In the next course, you will understand neural network basics and learn how to implement gradient descent and backpropagation in Python.
The last course is all about unsupervised learning and covers the K-means algorithm, Single Linkage Clustering, Gaussian Mixture Models, etc.
All three courses have one project. Along with the projects, there are quizzes and practice sets throughout the program.
Extra Benefits-
- You will get a chance to work on Real-world projects.
- You will get Technical mentor support.
- Along with that, you will get Career services.
Who Should Enroll?
- Those who have Intermediate Python programming knowledge and familiar with data structures like dictionaries and lists.
- And those who have basic knowledge of probability and statistics.
- This program is especially good for those who have experience in Python but have not yet studied Machine Learning topics.
Interested to Enroll?
If yes, then check it out here- Intro to Machine Learning with TensorFlow (Udacity)
3. Machine Learning Scientist with Python– Datacamp
| Provider- | Datacamp |
| Rating- | NA |
| Time to Complete- | 93 hours |
This is a career track offered by Datacamp. There are 23 courses in this career track. The course starts with the basics of supervised learning, unsupervised learning, linear classifiers, etc.
You will also learn the basics of gradient boosting with XGBoost. There are separate courses on dimensionality reduction, time-series data in Python, NLP, feature engineering, and deep learning using Tensorflow and Keras.
After that, you will learn PySpark, Image processing, etc. In the end, you will understand how to win competitions on Kaggle. Overall, this is a detailed career track for machine learning combined with practical exercises.
Who Should Enroll?
- Those who are a beginner in Machine learning and looking for step-by-step career guidance.
Interested to Enroll?
If yes, then check out the course details here- Machine Learning Scientist with Python
4. Machine Learning A-Z™: Hands-On Python & R In Data Science– Udemy
| Provider- | Udemy (SuperDataScience Team) |
| Rating- | 4.5/5 |
| Time to Complete- | 44 hours |
This is the Bestseller Course at Udemy. I love this course. This course not only teaches you the theory related to Machine Learning but also provides the implementation of each Machine Learning algorithm.
The best part of this course is that you will find implementation in Both Languages Python and R. If you are a complete beginner in Machine Learning, then this course is best for you.
This course doesn’t cover advanced topics but covers all basic topics of Machine Learning. You will also learn the basics of Deep Learning and Natural Language Processing.
Extra Benefits-
- You will get a Certificate of Completion.
- You will also get 74 articles and 38 downloadable resources.
- Along with that, you will get lifetime access to the course material.
Who Should Enroll?
- This course is for anyone who wants to learn Machine Learning.
- Students who have at least high school knowledge in math and who want to start learning Machine Learning.
Interested to Enroll?
If yes, then check out the details here- Machine Learning A-Z™: Hands-On Python & R In Data Science
5. Become a Machine Learning Engineer– Udacity
| Provider- | Udacity |
| Rating- | 4.6/5 |
| Time to Complete- | 3 months (If you spend 10 hours per week) |
This is another Nano-Degree program but not for beginners. This program is designed for those who have a basic understanding of Machine Learning concepts and Python programming.
In this program, you will learn advanced concepts of Machine Learning such as XGBoost and AutoGluon.
There are 4 courses and 5 projects in this Nanodegree Program. That means, this program is practical in nature, which is a positive part of this Nanodegree. But the Nanodegree program is expensive compared to other platform courses. And it is worth it for intermediate learners not for beginners.
Extra Benefits-
- You will get a chance to work on Real-world projects.
- You will get Technical mentor support.
- Along with that, you will get Career services.
Who Should Enroll?
- Those who have Intermediate Python programming knowledge and Intermediate knowledge of machine learning algorithms.
