Listemall is back with the newest review for another of Jose Portilla’s popular Udemy courses, Python for Data Science and Machine Learning Bootcamp. The course aims to cover Python programming, how to create amazing data visualizations, and how to use Machine Learning with Python.
Our score:
4.5
| PROS | CONS |
Python Crash course at the beginning The course is detail-oriented Take note easier with Jupyter Notebook |
Focuses on basic concepts Repetitive later on |
Overview
I came into this summer with an insane longing to learn Data Science and Machine Learning. The reason is simple.
Because I just found out earlier that:
- Data Scientist has been ranked the number one job on Glassdoor.
- The average salary of a data scientist is over $120.000 in the United States alone, according to Indeed.
- Data Science allows us to solve some of the world’s most unusual problems, like traffic and earthquake predictions.
As Python is the core of Data Science and Machine Learning, I decided to start with this language first. However, the path is not at all easy.
Finding a quality online Python course is like finding a needle in a haystack. There are thousands of materials and tutorials available. And, the number increases on a daily basis.
I chose to stick to a safe option: pick a course that everyone is loving. The first one I tried is Complete Python Bootcamp: Go from zero to hero in Python 3 by Jose Portilla. I loved it and felt that Jose Portilla has a lot more to offer. So, I started looking through his other Udemy courses.
Check out my full review for Complete Python Bootcamp: Go from zero to hero in Python 3 to learn about all the cool features, the downsides, and my insider’s note on the course.
After some quick search, I landed upon Python for Data Science and Machine Learning Bootcamp. The Bootcamp seems like a good next step.
I’ve been eating up about a section a day for the past few weeks. I’ll probably it in a couple of days. While the experience is still fresh in my mind, I thought I’d note down a few things I like and dislike from the course.
So, here it is: my complete review for Python for Data Science and Machine Learning Bootcamp by Jose Portilla.
Meet the Instructor
Before digging into the details of the review, let’s talk a bit about the instructor of Python for Data Science and Machine Learning Bootcamp. The class is lead by Jose Portilla, whose courses mostly focus on Python, Deep Learning, Data Science, and Machine Learning.
Jose Portilla
Jose Portilla has quite a background in Data Science. After receiving a BS and MS in Mechanical Engineering from Santa Clara University, he went on to become a professional instructor and trainer for Data Science and programming.
His work history got me totally hooked. Some of the companies he’s worked with include General Electric, Cigna, The New York Times, and Credit Suisse. Those are the organizations people dream to work for.
His skillset and experience in data-analyzing are applied directly to his teaching, which makes Jose is the second most popular Udemy instructors.
What I’ve Gained from the Bootcamp
Target Audience
Before we go any further, let’s see if you’re the right audience for the course.
One thing I want to make clear right from the start is that: Machine Learning and Data Science are advanced topics. It requires a whole lot of effort, patience, and, of course, good learning resources to understand these fields.
The most important thing is that you definitely cannot jump from Novice to Expert. It’s a process that takes time. Lots of it.
For this course, you’re going to need some programming experience. It’s preferable if this experience is in Python. Your knowledge of the Python language doesn’t have to be super wide as the course does start out with a Python Crash Course. But, it sure helps out a ton.
Course Content
Python for Data Science and Machine Learning Bootcamp is one of the most immersive courses I’ve ever taken. With 22.5 hours of video lectures, Jose Portilla guides learners through a Python crash course, an overview of data analysis libraries, an overview of data visualization libraries, and machine learning algorithms.
Let’s take a closer look at the actual content of this Udemy Bootcamp.
1 Python Crash Course
This section concentrates on the Python programming language. It takes you through basic Python concepts like data types, conditional operators, statements, loops, and lambdas. Thanks to the mini Python course, you can review necessary Python knowledge for the rest of this Python for Data Science and Machine Learning Bootcamp.
After the crash course, the Bootcamp is split up into two mains parts:
- Python for Data Science
- Python for Machine Learning.
2 Data Analysis using Python
The course will touch on two data analysis libraries in Python: NumPy – a Python library adding support for large, multi-dimensional arrays and matrices, and Pandas – a Python library for data manipulation and analysis.
3 Python for Data Visualization
Data visualization helps with communicating information to users. Some of the visualization libraries covered in this course include Matplotlib, Seaborn, Pandas Built-in Data Visualization, Plotly
4 Machine Learning
The second part of the Bootcamp focuses on how to use Python in Machine Learning. The machine learning algorithms taught in this course include:
- Linear Regression. It’s used to measure values based on continuous variables.
- Logistic Regression. Unlike Linear Regression, Logistic Regression is used to calculate values based on a given set of independent variables.
- K Nearest Neighbour. This is a simple algorithm to store and classify cases.
- Natural Language Processing. This algorithm uses computational techniques to analyze languages and speech.
- Neural Nets and Deep Learning. The neural network is a computer system that is built based on the human brain and nervous system. Deep learning refers to the techniques of learning in the neural network.
- Support Vector Machines. This is a machine learning algorithm for both classification and regression challenges.
The Bootcamp also includes other algorithms, like K-Means Clustering, big data and Spark with Python, principal component analysis, and recommender systems.
You will also learn about the Scikit-Learn library, which is a Python library with the implementation of quite a few machine learning algorithms.
What Makes the Course Great
1. The Python Crash Course
As stated above, Python for Data Science and Machine Learning Bootcamp provides a Crash Course section in Python. This section goes through basic concepts in the Python programming language.
I simply loved this.
Even though I had completed the Complete Python Bootcamp: Go from zero to hero in Python 3, it’s still great to have my knowledge revived. Thanks to the crash course, I felt comfortable tackling the rest of the materials.
As things can move a bit slow sometimes, I suggest kicking up to 1.25x speed. This way, you can still absorb the knowledge without getting bored.
2. Detail-Oriented
Another thing I like about this course is that it doesn’t ignore difficult concepts but even dives deep into them. Jose Portilla takes his time to explain and go into details on those important concepts. If a concept is too difficult, Jose would split it into smaller, more digestible sections. This helps me understand the topic more fully.
The structure of the course is quite easy to follow. Jose moves from theory to hands-on practice, then finally to review and corrections.
This course has a custom exercise for almost every section. Further solutions for the exercises will be offered either by Jose or in the FAQs. For different machine learning algorithms, a real-world data set is provided to students with questions requiring them to use the concepts learned to solve them. Students will also be able to get more data sets to sharpen their skills via resources like Kaggle.
I’ve also totally enjoyed the extra reading material, Introduction to Statistical Learning by Gareth James. This has been used as a companion book to expand the knowledge in algorithms.
3. Jupyter Notebook
One of the problems I run into while doing an online course is taking notes. Sometimes, I could just watch and rewatch the videos, then completely forgot to note down important things. I would remember the key points for a couple of days. But, a month into the course, I would forget them and have to go over the whole set of videos to look up the concepts.
I can safely tell you, that’s a huge waste of time.
This course provides notes both on screen and before, or after, videos to explain difficult concepts. Though I can’t tell this is as helpful as writing down your own notes, for snobs like me, we cannot wish for more.
As Python for Data Science and Machine Learning Bootcamp follows a hands-on approach, there are a lot of notes written down throughout the course.
To make things easier, Jose Portilla decided to use Jupyter Notebooks to share all the codes. Once registered, you will have access to a “Resources Folder” which contains well-arranged Jupyter Notebooks for each section. These notebooks help learners follow the lectures easily and also be able to do more practice later.
Jupyter NoteBooks are used to share the code and provide a playground for students to write and execute code. Be sure to get familiar with the platform beforehand.
What’s Not So Good
Although the course is good and all, I still have a few criticisms with it. It may be just me but that’s how I feel.
As I had discussed above, data science and machine learning are complex topics. It’s advisable to enter the course when you already know something about Python and programming. This is also stated in the course description.
Yet, Jose Portilla chose to spend quite a large amount of time explaining the basics.
This makes the Bootcamp so much longer than is required. I would pretty much prefer if Jose puts those fundamental concepts together as an optional section like the aforementioned Python Crash Course.
I also think of the course to be a bit repetitive.
Despite spending half the course on machine learning algorithms, the content doesn’t go very deep into them.
I found that the application of different algorithms with minimal theory got repetitive after the third (or fourth) Machine Learning section.
I’m not saying that repetition isn’t good. In fact, it is. However, I don’t need to learn scikit-learn calls for a hundred times. Instead, I would like them to be replaced with more fundamental Machine Learning theory and concepts.
Should I Join the Bootcamp?
Though not a hundred percent of what I was looking for, Python for Data Science and Machine Learning Bootcamp is still a well-designed course with a lot of support from the instructor.
However, do keep in mind that this course can be a bit basic and repetitive. If you need a more theory-intensive course, Machine Learning by Andrew Ng on Coursera is a good alternative. The best option is to pair the two courses to give you a strong foundation in Machine Learning.
Practice makes perfect.
Simply going through this course won’t make you a kick-ass data scientist or machine learning engineer. You will have to put in the hard work to get to the top.
Hope that you’ve found my review about the course Python for Data Science and Machine Learning Bootcamp on Udemy helpful.