This ultimate course lies at the intersection of research, Machine Learning, and Data Science. The first in class preaching by Jukka-Pekka "JP" Onnela and the latest python libraries such as Numpy, Pandas, SicPY, etc. make it the god sent bridge between intermediate and advanced levels in python.
About the course:
Self-paced, 12 weeks with 15+ hours
Instructor: Jukka-Pekka "JP" Onnela(Associate Professor of Biostatistics at Harvard University) learn more here
Homework Exercises(major): 10 problems worth 55%
Comprehension Check(minor): numerous questions worth 30%
Final Project: worth 15% (only for verified learners).
Passing score: 70%
Verified certificate: $170 or INR 12570
Syllabus
Incorporates a dense and rigorous curriculum [detailed syllabus here on edX]
Week 1: Basics of Python 3 (1 homework)
Objects and methods
sequence and manipulating objects
Week 2: Python Libraries and Concepts Used in Research(1 homework)
scope rules and classes
Numpy, Matplotlib, pyplot, Randomness, and time
Week 3: Case Studies Part 1 (3 homework)
DNA Translation
Language Processing
Introduction to Classification
Week 4: Case Studies Part 2 (3 homework)
Classifying Whiskies
Bird Migration
Social Network Analysis
Week 5: Statistical Learning (2 homework)
Linear Regression
Logistic Regression
Random Forest
Timeline: 2 weeks for every week listed here plus 1-2 for the final project.
WHO CAN TAKE THIS COURSE?
Beginners should look away as it requires some complicated libraries like SciPY and Pandas which are challenging for a newbie with less programming experience.
Intermediate
This course is best for people with some prior knowledge of logic building and other python libraries. It fulfills the gap between introductory and advanced-level courses. If you have completed some introductory python courses, you can try out this course.
EXERCISES
Homework exercises
These problems will be tough! The versions of the problem explained by the professor are a bit simple than the homework exercises. This requires the learner to focus on the concept to come up with the solution.
Comprehension Checks
These are simple to the point MCQ or fill-in-the-blank questions based on the lecture you watched before. They offer a chance to increase your grade by 30%.
Project
I would suggest you avoid the project because it can make any programmer cry and get your passing grade by the above two ways. PS: I had only scored 20% on the project.
Questions and problems
This course unravels some of the most intriguing concepts in machine learning like classification and regression. Therefore, it is expected from the case studies and problems to be complex.
Each exercise will give you a hard time, from the caesars cipher to plotting and predicting bird migration patterns, but the knowledge is well worth the effort.
I will be releasing some of the most challenging problems from the course on my blog. You can find them here
My take
I being on the beginner level had a hard time solving the problems and the final project. These are some of the most intimidating questions. If you are excited to solve them, you can complete the course!
Histogram of the central limit theorem by the sum of rolling 10 dies 1 million times.
DNA translation from DNA to Protein
Classification theory plots
Caesar's cipher encoding and decoding
Hamlet processing to find number of words using pandas
Classifying wines and whiskies based on the flavor of manufacturers with plots
Tracking and predicting bird migration patterns using cartopy
Social network analysis using neural network graphs
logistic and linear regression to predict the revenue and success of a movie.
Deep thinking is required to solve these analytical questions. Do not worry if your score is falling behind while completing this course, use the comprehension check exercises to boost it. Even I had barely passed this course at 71%.
Rating- 4.5/5
ups:
High-quality questions and lectures
amazing FAQ support
one of the few courses in this class
Downs:
The homework questions are complex compared to the lectures.
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