Develop Python-based machine learning models and AI applications while exploring automation and data prediction techniques.
Master Python for data analysis, machine learning, and automation. Build predictive models, create dynamic dashboards, and unleash the power of data visualization. Launch your career in data science and Python engineering, equipped with Python, NumPy, Pandas, and Matplotlib.
Learn Python, SQL, automation, and machine learning to become a Data Scientist. Gain Python programming, data analysis, SQL querying, and predictive modeling skills. Perfect for beginners, this program prepares you for entry-level data science and Python engineering roles. Unlock high-paying job opportunities in the field of data science.
Learn the fundamentals of machine learning, including regression analysis and classification algorithms, in this practical, hands-on course. Gain the skills needed to solve real-world problems using machine learning, with a focus on Python programming and data science libraries.
Super passionate, outstanding instructor. The format of using notebooks is great vs. PowerPoint and moving between PowerPoint and Visual Studio or Google Colab.
Outstanding class from Brian, once again. 5 stars.
This bootcamp was exactly what I needed to demystify machine learning. Brian’s teaching style, real-world examples, and signature ‘Brianisms’ kept me fully engaged all week. The pace was fast, the content was spot on, and I walked away wanting more.
A great course. I gained an incredible amount of knowledge in only 5 days.
Noble Desktop and Brian McClain hit it out of the park again! Fasten your seatbelts and hold on, because this class is a rocket ship of learning. It brilliantly takes foundational Python and expands it naturally to show you how to develop working machine learning models. It is a lot of information for a 30-hour bootcamp, but the flow ensures understanding through carefully crafted exercises and real-world examples. A+
The instructor explains material well, will answer questions, and work with students experiencing any issues.
It was great having this course with Collin, who also taught us Python for DS. Collin's sense of humor and patience kept the class engaged in a very dense topic. Great work, Noble Desktop!
This is a great class for anyone looking to sharpen their Python skills and get a clear intro to machine learning. It's easy to follow and well worth it!
Nice teacher.
This was an amazing course overall and I absolutely loved it!
I would highly recommend this bootcamp to anyone who ever wanted to really understand ML models.
This class offers you the opportunity to refine your Python skills and introduces you to new ways to visualize and, more importantly, summarize data. The project you complete by the end of it will reflect the progress you've made learning Python.
I recommend this course, this teacher, and this school to everyone wanting to learn machine learning. It was an amazing experience.
Excellent coursework and professor. It helped me grasp the basics of machine learning quickly and effectively. The class was hands-on and helped me implement Python concepts and apply them to build machine learning models. It was a great learning experience!
The experience was great - I learned a lot, was able to practice, and really enjoyed having Boris as our instructor. He knows his subject matter incredibly well and has been able to answer all of our questions quickly and thoroughly.
This course is a MUST HAVE and the instructor is GREAT. I recommend taking this course after the Python for Data Science Bootcamp if you never dealt with Python before, or without it if you need a project for your portfolio on GitHub. Both courses were taught by the same great instructor, Art Yudin. Art explains everything very clearly and helps everyone. Right after the course, I got an invitation to interview as a data science intern, and the first thing they asked was to see my projects on GitHub. So I downloaded my final project from this course, and they liked it. They gave me a new one to do, and I found it easy after this course!!!
It was great to see my progress during the duration of this bootcamp. I started with no prior knowledge of Python and by the end of the course, I was able to complete a machine learning project using Python.
Art transfers his knowledge very successfully. The Python Machine Learning Bootcamp included a lot of examples, practice, and possibilities for learning.
Art is a very helpful and patient instructor. I'm amazed at what I can build now.
Even if you are a beginner in Python programming, don't worry - all teaching is done in a way students can understand thoroughly.
Excellent. Art fine-tunes his teaching style to our level of understanding. Efficient, patient, current examples of data topics, overall great for me as a beginner - never coded at all prior!
Art Yudin is a fantastic instructor. During the course, he was very helpful with providing links, explaining certain methods, and providing really good examples.
Very useful instructors and good content. Would recommend the Python Machine Learning Bootcamp.
EXCELLENT! I was surprised by just how 'inspiring' Patrick turned out to be. He made the material interesting and provided a model of how to establish a good mind-frame necessary to mastery. I loved it. Felt like I got personal direction as well as technical skill.
Excellent class that immerses me in a deep and valuable understand of Python and data science!
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Machine learning (ML) algorithms are programs that learn from massive amounts of past data and experiences to optimize themselves in finding the best solution. It is a subset of artificial intelligence because programmers do not need to explicitly tell ML algorithms how to deal with every possible scenario they could encounter. They can “think” for themselves by relying on past experiences and data to generate new solutions. ML programs are becoming increasingly common and present in all aspects of our lives. It is highly probable you interacted with ML programs without even thinking about it.
There are many different types of machine learning algorithms that you use daily. Streaming services like Spotify and Netflix use recommendation engines, search engines power Google, and voice assistants like Siri and Alexa use natural language processing. When you use these services, they are trying to collect as much data from you as possible to feed their ML algorithms. The data points they collect will help ML programs make better predictions on what you are most likely to watch, search, or listen to in the future.
ML algorithms are trained in three different ways: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is the most common way to train a model, and it uses labeled data to tell algorithms which patterns to look out for. Even though it sounds simple, it can still be a powerful predictor under certain circumstances, and programmers can see the model’s logic clearly. In unsupervised learning, the data has no labels, and the models are free to find patterns as they see fit. The drawback to unsupervised learning is that programmers often cannot see the model’s logic. It is akin to a black box that you can feed data in and see the output but not the transformation in between. Finally, reinforcement learning is a method in which the model learns through trial and error. It will try various methods to classify or predict outcomes but reward the methods it took to achieve desirable results while penalizing steps that performed worse. Google’s AlphaGo is based on reinforcement learning, and it arguably became the best Go player in the world by playing itself.
Within the machine learning umbrella, there is another subset called deep learning (DL) that was invented in 1986 that is making a fashionable comeback. Although it is superb at finding patterns in unstructured and unlabeled data, it recently became practical to use due to advances in computing. DL’s design was inspired by the human brain and relies on layered nodes (which could be considered neurons in a human brain) to find patterns in an iterative process. DL models learn distinct combinations of features that are associated together in each node and use this knowledge to test against data they haven’t seen before. The programs measure how well they did in identifying new objects and adjust accordingly to improve themselves. DL excels at finding patterns in unstructured data, which makes it very useful in real-world applications.
Machine learning programs affect our daily lives from detecting fraudulent financial transactions, recommending which movies and songs we’ll like next, to interpreting our voices into commands computers will understand. Fortunately, it’s never been easier to learn how these programs work and build them ourselves. It’s just a matter of trial and error, right?