The document provides a comprehensive guide for aspiring data scientists and machine learning engineers, outlining key resources, study paths, and career advice. It includes links to online courses, tutorials, and recommended readings in areas like Python programming, data science, statistics, and machine learning. Additionally, it emphasizes the importance of practical experience and ongoing learning in the field.

1. Python Programming, Data Science, Machine Learning and Deep
Learning Resources and Study Path for Aspiring Data Scientist/
Machine Learning Engineers.
The Ultimate guide to AI, Data Science & Machine Learning, Articles, Cheat
sheets and Tutorials ALL in one place
https://www.linkedin.com/pulse/all-cheatsheets-one-place-vipul-patel/
https://www.analyticsvidhya.com/blog/2019/01/learning-path-data-scientist-
machine-learning-2019/
https://www.analyticsvidhya.com/blog/2019/01/comprehensive-learning-path-
deep-learning-2019/
https://www.linkedin.com/pulse/deep-learning-resources-study-path-aspiring-data-
srivatsan-srinivasan/?fbclid=IwAR1JgZhq985KihPgGxG1-
BTlSrtbrEUjlGE5aOihq9HQykN8HEMsYoxGkSE
Udacity Nano Degrees
Data Science Nano Degree
https://mega.nz/#F!qrpxSIRD!PClG5ZMHdd5FroIFTT_Z5Q
https://mega.nz/#F!qrpxSIRD!PClG5ZMHdd5FroIFTT_Z5Q
Deep Learning Nano Degree
https://drive.google.com/drive/u/0/folders/1-
AqdoaLi9MCyUXF_J3cbRTZX48o4_Lbi
https://drive.google.com/drive/u/0/folders/1-
AqdoaLi9MCyUXF_J3cbRTZX48o4_Lbi

2. Data Structures and Algorithms
https://mega.nz/#F!Czh0GYAJ!bJUhVftZ-YTHhceQH3D1-w
https://mega.nz/#F!Czh0GYAJ!bJUhVftZ-YTHhceQH3D1-w
R Stack for Data Science and Machine Learning
Data Science Specialization: John Hopkins University (Highly Recommended)
https://www.coursera.org/specializations/jhu-data-science
DataCamp
https://www.datacamp.com/tracks/data-scientist-with-r
DataQuest
https://www.dataquest.io/directory/
R for Data Science:
The Analytics Edge: MIT (Edx) ( Highly Recommended)
https://www.edx.org/course/the-analytics-edge-2
Python Stack for Data Science and Machine Learning
Python Programming for Data Science
Python 3 Programming Specialization
https://www.coursera.org/specializations/python-3-programming

3. How to Learn Python for Data Science the Right Way
https://www.kdnuggets.com/2019/06/python-data-science-right-way.html
Learning Data Science with Python
IBM Data Science Professional Certificate (Beginner Level, Highly
Recommended for All)
https://www.coursera.org/professional-certificates/ibm-data-science
Applied Data Science with Python Specialization ( Intermediate)
https://www.coursera.org/specializations/data-science-python
Mathematics for Data Science & Machine Learning
Mathematics for Machine Learning Specialization (Coursera)
https://www.coursera.org/specializations/mathematics-machine-learning
Essential Math for Machine Learning: The Python Edition (EDX)
https://www.edx.org/course/essential-math-for-machine-learning-python-edition
The Mathematics that you require when dividing into AI
https://www.linkedin.com/pulse/want-get-ai-you-need-learn-lot-math-heres-how-
ken-urquhart-phd/
Statistics for Data Science (Python and R Stack)
Statistics with Python Specialization
https://www.coursera.org/specializations/statistics-with-python

4. Statistics with R Specialization
https://www.coursera.org/specializations/statistics
Statistical techniques that Aspiring Data Scientists need to master
https://medium.com/cracking-the-data-science-interview/the-10-statistical-
techniques-data-scientists-need-to-master-1ef6dbd531f7
 Check out Statistics, Descriptive and Inferential Statistics Free Courses on
Udacity
Advanced Machine Learning Resources
Advanced Machine Learning Specialization (National Research Univeristy,
Higher School of Economics, Russia)
https://www.coursera.org/specializations/aml
Deep Learning Resources
https://www.deeplearning.ai/
https://www.fast.ai/
Reinforcement Learning
https://www.coursera.org/specializations/reinforcement-learning
Data Visualization with Tableau
https://www.coursera.org/specializations/data-visualization

5. Machine Learning CS5350/6350
http://www.cs.utah.edu/~piyush/teaching/cs5350.html
MLMasteryBooks by Jason BrownLee
https://github.com/rajatmodi62/MLMasteryBooks
Youtube Channels
Stanford University: Computer Vision and Natural Language Processing
Sentdex: Python Programming from Basics to Advanced topics
Krish Naik: Leading Data Scientist, Mentor and your one stop guy on anything
pertaining data science and Machine Learning
Edureka and Data School: Highly recommended to all
Thomas Simonini: Reinforcement Learning
Storytelling and influencing: Communicate with Impact
https://www.coursera.org/learn/communicate-with-impact
State of the Art Machine Learning Papers
https://paperswithcode.com/sota
https://paperswithcode.com/sota
Books, Journals and Other Resources
I would highly recommend going through the following amazing books

6.  Master Machine Learning Algorithms by Jason Brownlee.
 Hands-On Machine Learning with Scikit Learn and Tensorflow: Concepts,
Tools and Techniques to Build Intelligent Systems by O’ Reilly.
 The Deep Learning book by Ian Goodfellow and Yoshua Bengio
 Deep Learning with Python by Jason Brownlee
Top 10 Data Science Leaders / Influencers You Should Follow
https://www.kdnuggets.com/2019/07/top-10-data-science-leaders.html
https://www.linkedin.com/in/vipulppatel/
Career Paths to follow in Machine Learning
· Research and Development - learn the math’s first. In research the job is to
come up with new, or substantially improve existing algorithms. Can’t do that
without math.
Industry, applied - learn to program and handle data first because 90% of applied
machine learning is data wrangling and programming. You should not stop there
though. Data wrangling and machine learning without a good grasp on statistics is
not a good combination.
 The foremost step to begin with ML is to get comfortable with a
programming language, most preferably Python. Python has an easy
learning curve and a host of useful libraries well-suited for different ML
tasks.
 After you’ve become well-versed with programming, break the ice with
the basic ML concepts including supervised learning, unsupervised
learning, reinforcement learning and then move on to more complex ones
like Deep Learning.
 Brush up on your Mathematics and Statistical skills. Revisit Linear
Algebra, Multivariable Calculus, along with the fundamental Statistical

7. concepts like probability distributions, statistical significance, hypothesis
testing, variance and standard deviation, Bayesian theory etc.
 Once you feel like you’ve become comfortable with all the things
mentioned above, try your hand at practical applications of ML.
 Experiment with ML algorithms and build simple applications/projects.
 Participate in Kaggle/ Zindi and other online competitions. These
competitions help you assess your strengths and weaknesses compared to
your rivals and also are a great opportunity for landing jobs - top recruiters
regularly monitor online competitions in the field.
Golden Advice
You must always harbor the will to up skill - read Data Science articles, subscribe
to newsletters, attend conferences/seminars, etc.
Newsletters to subscribe to: Towards Data Science, Analytics Vidhya, Data
Science Central, Reddit. They'll keep you updated with latest improvements and
algorithms taking place currently, and in the field of machine learning, the person
most updated with these news is the winner. So keep reading, and keep on learning
and enhancing your skills.