The Top 5 Machine Learning Libraries in Python

Recent Review from Similar Course:

"This was one of the most useful classes I have taken in a long time. Very specific, real-world examples. It covered several instances of 'what is happening', 'what it means' and 'how you fix it'. I was impressed."  Steve

Welcome to The Top 5 Machine Learning Libraries in Python.  This is an introductory course on the process of building supervised machine learning models and then using libraries in a computer programming language called Python.

What’s the top career in the world? Doctor? Lawyer? Teacher? Nope. None of those.

The top career in the world is the data scientist. Great. What’s a data scientist?

The area of study which involves extracting knowledge from data is called Data Science and people practicing in this field are called as Data Scientists.

Business generate a huge amount of data.  The data has tremendous value but there so much of it where do you begin to look for value that is actionable? That’s where the data scientist comes in.  The job of the data scientist is to create predictive models that can find hidden patterns in data that will give the business a competitive advantage in their space.

Don’t I need a PhD?  Nope. Some data scientists do have PhDs but it’s not a requirement.  A similar career to that of the data scientist is the machine learning engineer.

A machine learning engineer is a person who builds predictive models, scores them and then puts them into production so that others in the company can consume or use their model.  They are usually skilled programmers that have a solid background in data mining or other data related professions and they have learned predictive modeling.

In the course we are going to take a look at what machine learning engineers do. We are going to learn about the process of building supervised predictive models and build several using the most widely used programming language for machine learning. Python. There are literally hundreds of libraries we can import into Python that are machine learning related.

A library is simply a group of code that lives outside the core language. We “import it” into our work space when we need to use its functionality. We can mix and match these libraries like Lego blocks.

Thanks for your interest in the The Top 5 Machine Learning Libraries in Python and we will see you in the course. 

--- **Course Review for "A Gentle Introduction to Python's Machine Learning Libraries"** **Overall Rating:** 4.18/5 (based on recent reviews) ### Course Pros: - **High Quality Overview:** The course provides a high-level overview of essential machine learning libraries in Python, making it an excellent starting point for beginners or those looking to refresh their knowledge. (Multiple Reviews) - **Educational Approach:** The approach to teaching one or two methods from each library is appreciated for its manageable scope, allowing learners to grasp the concepts without feeling overwhelmed. (Several Reviews) - **Engaging Content:** The course content is engaging and informative, with good use of examples and a presentation style that's easy to follow. (Various Reviews) - **Instructor Knowledge:** Mike, the instructor, is highly knowledgeable and delivers the course in an understandable manner. (Several Reviews) - **Quick Reference:** The course serves as a quick refresher for data science skills, which is beneficial for learners who need to brush up on their knowledge of Python's data science libraries. (Several Reviews) - **Practical Application:** The course includes practical examples and real-world applications of the machine learning libraries discussed. (Several Reviews) ### Course Cons: - **Audio Quality Concerns:** Some reviews mention that audio quality issues are a notable drawback, potentially affecting the learning experience. (One Review) - **Misspellings in Presentation:** The instructor's difficulty with spelling simple words is a recurring issue, which detracts from the professionalism of the course. (One Review) - **Pacing Issues:** The delivery speed of the trainer is reportedly slow, and while increasing playback speed can help, it may not always make the voice sound pleasant. (Two Reviews) - **Lack of Depth:** Some learners expected a more extensive course that delved deeper into each library rather than just an overview. (Multiple Reviews) - **Inconsistent Content Delivery:** The instructor jumps around topics without consistency, which can be confusing for those looking for a systematic learning approach. (One Review) - **Presentation Techniques:** Drawing words on the screen and speaking speed issues could be improved to enhance the learning experience. (One Review) - **Content Familiarity:** For some experienced learners, the content may not provide new insights as it was already familiar to them. (One Review) - **Expectation Misalignment:** A few reviews indicate that the course description did not align with what was delivered, leading to disappointment. (One Review) - **Pricing and Value:** Some learners felt that the course was too basic for the price or expected more content for the cost. (One Review) ### General Feedback: The course generally receives positive feedback for its overview of Python's machine learning libraries, with a focus on providing a gentle introduction to beginners. The instructor's knowledge and the course's content are highly valued by many learners. However, concerns regarding audio quality, spelling errors, pacing, depth of content, and presentation techniques have been consistently noted. Despite these issues, the course is deemed useful and entertaining, with a few learners suggesting that it could be offered for free due to its value. **Conclusion:** "A Gentle Introduction to Python's Machine Learning Libraries" is a well-regarded course that provides a solid foundation in machine learning libraries for beginners or those needing a refresher. While there are some notable issues with the presentation and delivery, the educational content remains strong, making it a worthwhile option for those looking to start their journey into Python's data science ecosystem.