A Machine Learning Guide for Average Humans

Posted by alexis-sanders

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Machine learning (ML) has grown consistently in worldwide prevalence. Its implications have stretched from small, seemingly inconsequential victories to groundbreaking discoveries. The SEO community is no exception. An understanding and intuition of machine learning can support our understanding of the challenges and solutions Google’s engineers are facing, while also opening our minds to ML’s broader implications.

The advantages of gaining an general understanding of machine learning include:

  • Gaining empathy for engineers, who are ultimately trying to establish the best results for users
  • Understanding what problems machines are solving for, their current capabilities and scientists’ goals
  • Understanding the competitive ecosystem and how companies are using machine learning to drive results
  • Preparing oneself for for what many industry leaders call a major shift in our society (Andrew Ng refers to AI as a “new electricity”)
  • Understanding basic concepts that often appear within research (it’s helped me with understanding certain concepts that appear within Google Brain’s Research)
  • Growing as an individual and expanding your horizons (you might really enjoy machine learning!)
  • When code works and data is produced, it’s a very fulfilling, empowering feeling (even if it’s a very humble result)

I spent a year taking online courses, reading books, and learning about learning (…as a machine). This post is the fruit borne of that labor — it covers 17 machine learning resources (including online courses, books, guides, conference presentations, etc.) comprising the most affordable and popular machine learning resources on the web (through the lens of a complete beginner). I’ve also added a summary of “If I were to start over again, how I would approach it.”

This article isn’t about credit or degrees. It’s about regular Joes and Joannas with an interest in machine learning, and who want to spend their learning time efficiently. Most of these resources will consume over 50 hours of commitment. Ain’t nobody got time for a painful waste of a work week (especially when this is probably completed during your personal time). The goal here is for you to find the resource that best suits your learning style. I genuinely hope you find this research useful, and I encourage comments on which materials prove most helpful (especially ones not included)! #HumanLearningMachineLearning


Executive summary:

Here’s everything you need to know in a chart:

Machine Learning Resource

Time (hours)

Cost ($)

Year

Credibility

Code

Math

Enjoyability

Jason Maye’s Machine Learning 101 slidedeck: 2 years of headbanging, so you don’t have to

2

$0

’17

Credibility level 3

Code level 1

Math level 1

Enjoyability level 5

{ML} Recipes with Josh Gordon Playlist

2

$0

’16

Credibility level 3

Code level 3

Math level 1

Enjoyability level 4

Machine Learning Crash Course

15

$0

’18

Credibility level 4

Code level 4

Math level 2

Enjoyability level 4

OCDevel Machine Learning Guide Podcast

30

$0

’17-

Credibility level 1

Code level 1

Math level 1

Enjoyability level 5

Kaggle’s Machine Learning Track (part 1)

6

$0

’17

Credibility level 3

Code level 5

Math level 1

Enjoyability level 4

Fast.ai (part 1)

70

$70*

’16

Credibility level 4

Code level 5

Math level 3

Enjoyability level 5

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

20

$25

’17

Credibility level 4

Code level 4

Math level 2

Enjoyability level 3

Udacity’s Intro to Machine Learning (Kate/Sebastian)

60

$0

’15

Credibility level 4

Code level 4

Math level 3

Enjoyability level 3

Andrew Ng’s Coursera Machine Learning

55

$0

’11

Credibility level 5

Code level 2

Math level 4

Enjoyability level 1

iPullRank Machine Learning Guide

3

$0

’17

Credibility level 1

Code level 1

Math level 1

Enjoyability level 3

Review Google PhD

2

$0

’17

Credibility level 5

Code level 4

Math level 2

Enjoyability level 2

Caltech Machine Learning on iTunes

27

$0

’12

Credibility level 5

Code level 2

Math level 5

Enjoyability level 2

Pattern Recognition & Machine Learning by Christopher Bishop

150

$75

’06

Credibility level 5

Code level 2

Math level 5

N/A

Machine Learning: Hands-on for Developers and Technical Professionals

15

$50

’15

Credibility level 2

Code level 3

Math level 2

Enjoyability level 3

Introduction to Machine Learning with Python: A Guide for Data Scientists

15

$25

’16

Credibility level 3

Code level 3

Math level 3

Enjoyability level 2

Udacity’s Machine Learning by Georgia Tech

96

$0

’15

Credibility level 5

Code level 1

Math level 5

Enjoyability level 1

Machine Learning Stanford iTunes by Andrew Ng

25

$0

’08

Credibility level 5

Code level 1

Math level 5

N/A

*Free, but there is the cost of running an AWS EC2 instance (~$70 when I finished, but I did tinker a ton and made a Rick and Morty script generator, which I ran many epochs [rounds] of…)


Here’s my suggested program:

1. Starting out (estimated 60 hours)

Start with shorter content targeting beginners. This will allow you to get the gist of what’s going on with minimal time commitment.

2. Ready to commit (estimated 80 hours)

By this point, learners would understand their interest levels. Continue with content focused on applying relevant knowledge as fast as possible.

3. Broadening your horizons (estimated 115 hours)

If you’ve made it through the last section and are still hungry for more knowledge, move on to broadening your horizons. Read content focused on teaching the breadth of machine learning — building an intuition for what the algorithms are trying to accomplish (whether visual or mathematically).

Your next steps

By this point, you will already have AWS running instances, a mathematical foundation, and an overarching view of machine learning. This is your jumping-off point to determine what you want to do.

You should be able to determine your next step based on your interest, whether it’s entering Kaggle competitions; doing Fast.ai part two; diving deep into the mathematics with Pattern Recognition & Machine Learning by Christopher Bishop; giving Andrew Ng’s newer Deeplearning.ai course on Coursera; learning more about specific tech stacks (TensorFlow, Scikit-Learn, Keras, Pandas, Numpy, etc.); or applying machine learning to your own problems.


Why am I recommending these steps and resources?

I am not qualified to write an article on machine learning. I don’t have a PhD. I took one statistics class in college, which marked the first moment I truly understood “fight or flight” reactions. And to top it off, my coding skills are lackluster (at their best, they’re chunks of reverse-engineered code from Stack Overflow). Despite my many shortcomings, this piece had to be written by someone like me, an average person.

Statistically speaking, most of us are average (ah, the bell curve/Gaussian distribution always catches up to us). Since I’m not tied to any elitist sentiments, I can be real with you. Below contains a high-level summary of my reviews on all of the classes I took, along with a plan for how I would approach learning machine learning if I could start over. Click to expand each course for the full version with notes.


In-depth reviews of machine learning courses:

Starting out

Jason Maye’s Machine Learning 101 slidedeck: 2 years of head-banging, so you don’t have to ↓

{ML} Recipes with Josh Gordon ↓

Need to Know: This mini-series YouTube-hosted playlist covers the very fundamentals of machine learning with opportunities to complete exercises.

Loved:

  • It is genuinely beginner-focused.
    • They make no assumption of any prior knowledge.
    • Gloss over potentially complex topics that may serve as noise.
    • Playlist ~2 hours
  • Very high-quality filming, audio, and presentation, almost to the point where it had its own aesthetic.
  • Covers some examples in scikit-learn and TensorFlow, which felt modern and practical.
  • Josh Gordon was an engaging speaker.

Disliked:

  • I could not get Dockers on Windows (suggested package manager). This wasn’t a huge deal, since I already had my AWS setup by this point; however, a bit of a bummer since it made it impossible to follow certain steps exactly.
    • Issue: Every time I tried to download (over the course of two weeks), the .exe file would recursively start and keep spinning until either my memory ran out, computer crashed, or I shut my computer down. I sent this to Docker’s Twitter account to no avail.

Lecturer:

Josh Gordon:

  • Developer Advocate for at TensorFlow at Google
  • Leads Machine Learning advocacy at Google
  • Member of the Udacity AI & Data Industry Advisory Board
  • Masters in Computer Science from Columbia University

Links:

Tips on Watching:

  • The playlist is short (only ~1.5 hours screen time). However, it can be a bit fast-paced at times (especially if you like mimicking the examples), so set aside 3-4 hours to play around with examples and allow time for installation, pausing, and following along.
  • Take time to explore code labs.

Google’s Machine Learning Crash Course with TensorFlow APIs ↓

Need to Know: A Google researcher-made crash course on machine learning that is interactive and offers its own built-in coding system!

Loved:

  • Different formats of learning: high-quality video (with ability to adjust speed, closed captioning), readings, quizzes (with explanations), visuals (including whiteboarding), interactive components/ playgrounds, code lab exercises (run directly in your browser (no setup required!))
  • Non-intimidating
    • One of my favorite quotes: “You don’t need to understand the math to be able to take a look at the graphical interpretation.”
    • Broken down into digestible sections
    • Introduces key terms

Disliked:

  • N/A

Lecturers:

Multiple Google researchers participated in this course, including:

  • Peter Norvig
    • Director of Research at Google Inc.
    • Previously he directed Google’s core search algorithms group.
    • He is co-author of Artificial Intelligence: A Modern Approach
  • D. Sculley
    • Senior Staff Software Engineer at Google
    • KDD award-winning papers
    • Works on massive-scale ML systems for online advertising
    • Was part of a research ML paper on optimizing chocolate chip cookies
    • According to his personal website, he prefers to go by “D.”
  • Cassandra Xia
  • Maya Gupta
    • Leads Glassbox Machine Learning R&D team at Google
    • Associate Professor of Electrical Engineering at the University of Washington (2003-2012)
    • In 2007, Gupta received the PECASE award from President George Bush for her work in classifying uncertain (e.g. random) signals
    • Gupta also runs Artifact Puzzles, the second-largest US maker of wooden jigsaw puzzles
  • Sally Goldman
    • Research Scientist at Google
    • Co-author of A Practical Guide to Data Structures and Algorithms Using Java
    • Numerous journals, classes taught at Washington University, and contributions to the ML community

Links:

Tips on Doing:

  • Actively work through playground and coding exercises

OCDevel’s Machine Learning Guide Podcast ↓

Need to Know: This podcast focuses on the high-level fundamentals of machine learning, including basic intuition, algorithms, math, languages, and frameworks. It also includes references to learn more on each episode’s topic.

Loved:

  • Great for trips (when traveling a ton, it was an easy listen).
  • The podcast makes machine learning fun with interesting and compelling analogies.
  • Tyler is a big fan of Andrew Ng’s Coursera course and reviews concepts in Coursera course very well, such that both pair together nicely.
  • Covers the canonical resources for learning more on a particular topic.

Disliked:

  • Certain courses were more theory-based; all are interesting, yet impractical.
  • Due to limited funding the project is a bit slow to update and has less than 30 episodes.

Podcaster:

Tyler Renelle:

  • Machine learning engineer focused on time series and reinforcement
  • Background in full-stack JavaScript, 10 years web and mobile
  • Creator of HabitRPG, an app that treats habits as an RPG game

Links:

Tips on Listening:

  • Listen along your journey to help solidify understanding of topics.
  • Skip episodes 1, 3, 16, 21, and 26 (unless their topics interest and inspire you!).

Kaggle Machine Learning Track (Lesson 1) ↓

Need to Know: A simple code lab that covers the very basics of machine learning with scikit-learn and Panda through the application of the examples onto another set of data.

Loved:

  • A more active form of learning.
  • An engaging code lab that encourages participants to apply knowledge.
  • This track offers has a built-in Python notebook on Kaggle with all input files included. This removed any and all setup/installation issues.
    • Side note: It’s a bit different than Jupyter notebook (e.g., have to click into a cell to add another cell).
  • Each lesson is short, which made the entire lesson go by very fast.

Disliked:

  • The writing in the first lesson didn’t initially make it clear that one would need to apply the knowledge in the lesson to their workbook.
    • It wasn’t a big deal, but when I started referencing files in the lesson, I had to dive into the files in my workbook to find they didn’t exist, only to realize that the knowledge was supposed to be applied and not transcribed.

Lecturer:

Dan Becker:

  • Data Scientist at Kaggle
  • Undergrad in Computer Science, PhD in Econometrics
  • Supervised data science consultant for six Fortune 100 companies
  • Contributed to the Keras and Tensorflow libraries
  • Finished 2nd (out of 1353 teams) in $3 million Heritage Health Prize data mining competition
  • Speaks at deep learning workshops at events and conferences

Links:

Tips on Doing:

  • Read the exercises and apply to your dataset as you go.
  • Try lesson 2, which covers more complex/abstract topics (note: this second took a bit longer to work through).


Ready to commit

Fast.ai (part 1 of 2) ↓

Need to Know: Hands-down the most engaging and active form of learning ML. The source I would most recommend for anyone (although the training plan does help to build up to this course). This course is about learning through coding. This is the only course that I started to truly see the practical mechanics start to come together. It involves applying the most practical solutions to the most common problems (while also building an intuition for those solutions).

Loved:

  • Course Philosophy:
    • Active learning approach
      • “Go out into the world and understand underlying mechanics (of machine learning by doing).”
    • Counter-culture to the exclusivity of the machine learning field, focusing on inclusion.
      • “Let’s do shit that matters to people as quickly as possible.”
  • Highly pragmatic approach with tools that are currently being used (Jupyter Notebooks, scikit-learn, Keras, AWS, etc.).
  • Show an end-to-end process that you get to complete and play with in a development environment.
  • Math is involved, but is not prohibitive. Excel files helped to consolidate information/interact with information in a different way, and Jeremy spends a lot of time recapping confusing concepts.
  • Amazing set of learning resources that allow for all different styles of learning, including:
    • Video Lessons
    • Notes
    • Jupyter Notebooks
    • Assignments
    • Highly active forums
    • Resources on Stackoverflow
    • Readings/resources
      • Jeremy often references popular academic texts
    • Jeremy’s TEDx talk in Brussels
  • Jeremy really pushes one to do extra and put in the effort by teaching interesting problems and engaging one in solving them.
  • It’s a huge time commitment; however, it’s worth it.
  • All of the course’s profits are donated.

Disliked:

  • Overview covers their approach to learning (obviously I’m a fan!). If you’re already drinking the Kool-aid, skip past.
  • I struggled through the AWS setup (13-minute video) for about five hours (however, it felt so good when it was up and running!).
  • Because of its practicality and concentration on solutions used today to solve popular problem types (image recognition, text generation, etc.), it lacks breadth of machine learning topics.

Lecturers:

Jeremy Howard:

  • Distinguished Research Scientist at the University of San Francisco
  • Faculty member at Singularity University
  • Young Global Leader with the World Economic Forum
  • Founder of Enlitic (the first company to apply deep learning to medicine)
  • Former President and Chief Scientist of the data science platform Kaggle

Rachel Thomas:

Links:

Tips on Doing:

  • Set expectations with yourself that installation is going to probably take a few hours.
  • Prepare to spend about ~70 hours for this course (it’s worth it).
  • Don’t forget to shut off your AWS instance.
  • Balance out machine learning knowledge with a course with more breadth.
  • Consider giving part two of the Fast.ai program a shot!

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems ↓

Need to Know: This book is an Amazon best seller for a reason. It covers a lot of ground quickly, empowers readers to walk through a machine learning problem by chapter two, and contains practical up-to-date machine learning skills.

Loved:

  • Book contains an amazing introduction to machine learning that briskly provides an overarching quick view of the machine learning ecosystem.
  • Chapter 2 immediately walks the reader through an end-to-end machine learning problem.
  • Immediately afterwards, Aurélien pushes a user to attempt to apply this solution to another problem, which was very empowering.
  • There are review questions at the end of each chapter to ensure on has grasped the content within the chapter and to push the reader to explore more.
  • Once installation was completed, it was easy to follow and all code is available on GitHub.
  • Chapters 11-14 were very tough reading; however, they were a great reference when working through Fast.ai.
  • Contains some powerful analogies.
  • Each chapter’s introductions were very useful and put everything into context. This general-to-specifics learning was very useful.

Disliked:

  • Installation was a common source of issues during the beginning of my journey; the text glided over this. I felt the frustration that most people experience from installation should have been addressed with more resources.

Writer:

Aurélien Géron:

  • Led the YouTube video classification team from 2013 to 2016
  • Currently a machine Learning consultant
  • Founder and CTO of Wifirst and Polyconseil
  • Published technical books (on C++, Wi-Fi, and Internet architectures)

Links:

Tips on Using:

  • Get a friend with Python experience to help with installation.
  • Read the introductions to each chapter thoroughly, read the chapter (pay careful attention to code), review the questions at the end (highlight any in-text answer), make a copy of Aurélien’s GitHub and make sure everything works on your setup, re-type the notebooks, go to Kaggle and try on other datasets.


Broadening your horizons

Udacity: Intro to Machine Learning (Kate/Sebastian) ↓

Need to Know: A course that covers a range of machine learning topics, supports building of intuition via visualization and simple examples, offers coding challenges, and a certificate (upon completion of a final project). The biggest challenge with this course is bridging the gap between the hand-holding lectures and the coding exercises.

Loved:

  • Focus on developing a visual intuition on what each model is trying to accomplish.
  • This visual learning mathematics approach is very useful.
  • Cover a vast variety and breadth of models and machine learning basics.
  • In terms of presenting the concept, there was a lot of hand-holding (which I completely appreciated!).
  • Many people have done this training, so their GitHub accounts can be used as reference for the mini-projects.
  • Katie actively notes documentation and suggests where viewers can learn more/reference material.

Disliked:

  • All of the conceptual hand-holding in the lessons is a stark contrast to the challenges of installation, coding exercises, and mini-projects.
    • This is the first course started and the limited instructions on setting up the environment and many failed attempts caused me to break down crying at least a handful of times.
  • The mini-projects are intimidating.
  • There is extra code added to support the viewers; however, it’s done so with little acknowledgement as to what it’s actually doing. This made learning a bit harder.

Lecturer:

Caitlin (Katie) Malone:

  • Director of Data Science Research and Development at Civis Analytics
  • Stanford PhD in Experimental Particle Physics
  • Intern at Udacity in summer 2014
  • Graduate Researcher at the SLAC National Accelerator Laboratory
  • Podcaster with Ben Jaffe (currently Facebook UI Engineer and a music aficionado) on a machine learning podcast Linear Digressions (100+ episodes)

Sebastian Thrun:

  • CEO of the Kitty Hawk Corporation
  • Chairman and co-founder of Udacity
    • One of my favorite Sebastian quotes: “It occurred to me, I could be at Google and build a self-driving car, or I can teach 10,000 students how to build self-driving cars.”
  • Former Google VP
    • Founded Google X
    • Led development of the robotic vehicle Stanley
  • Professor of Computer Science at Stanford University
  • Formerly a professor at Carnegie Mellon University.

Links:

Tips on Watching:

  • Get a friend to help you set up your environment.
  • Print mini-project instructions to check off each step.

Andrew Ng’s Coursera Machine Learning Course ↓

Need to Know: The Andrew Ng Coursera course is the most referenced online machine learning course. It covers a broad set of fundamental, evergreen topics with a strong focus in building mathematical intuition behind machine learning models. Also, one can submit assignments and earn a grade for free. If you want to earn a certificate, one can subscribe or apply for financial aid.

Loved:

  • This course has a high level of credibility.
  • Introduces all necessary machine learning terminology and jargon.
  • Contains a very classic machine learning education approach with a high level of math focus.
  • Quizzes interspersed in courses and after each lesson support understanding and overall learning.
  • The sessions for the course are flexible, the option to switch into a different section is always available.

Disliked:

  • The mathematic notation was hard to process at times.
  • The content felt a bit dated and non-pragmatic. For example, the main concentration was MATLAB and Octave versus more modern languages and resources.
  • Video quality was less than average and could use a refresh.

Lecturer:

Andrew Ng:

  • Adjunct Professor, Stanford University (focusing on AI, Machine Learning, and Deep Learning)
  • Co-founder of Coursera
  • Former head of Baidu AI Group
  • Founder and previous head of Google Brain (deep learning) project
  • Former Director of the Stanford AI Lab
  • Chairman of the board of Woebot (a machine learning bot that focuses on Cognitive Behavior Therapy)

Links:

Tips on Watching:

  • Be disciplined with setting aside timing (even if it’s only 15 minutes a day) to help power through some of the more boring concepts.
  • Don’t do this course first, because it’s intimidating, requires a large time commitment, and isn’t a very energizing experience.


Additional machine learning opportunities

iPullRank Machine Learning Guide ↓

Need to Know: A machine learning e-book targeted at marketers.

Loved:

  • Targeted at marketers and applied to organic search.
  • Covers a variety of machine learning topics.
  • Some good examples, including real-world blunders.
  • Gives some practical tools for non-data scientists (including: MonkeyLearn and Orange)
    • I found Orange to be a lot of fun. It struggled with larger datasets; however, it has a very visual interface that was more user-friendly and offers potential to show some pretty compelling stories.

C:\Users\asanders\AppData\Local\Microsoft\Windows\INetCache\Content.Word\color=health.png

Disliked:

  • Potential to break up content more with relevant imagery — the content was very dense.

Writers:

iPullRank Team (including Mike King):

  • Mike King has a few slide decks on the basics of machine learnings and AI
  • iPullRank has a few data scientists on staff

Links:

Tips on Reading:

  • Read chapters 1-6 and the rest depending upon personal interest.

Review Google PhD ↓

Need to Know: A two-hour presentation from Google’s 2017 IO conference that walks through getting 99% accuracy on the MNIST dataset (a famous dataset containing a bunch of handwritten numbers, which the machine must learn to identify the numbers).

Loved:

  • This talk struck me as very modern, covering the cutting edge.
  • Found this to be very complementary to Fast.ai, as it covered similar topics (e.g. ReLu, CNNs, RNNs, etc.)
  • Amazing visuals that help to put everything into context.

Disliked:

  • The presentation is only a short conference solution and not a comprehensive view of machine learning.
  • Also, a passive form of learning.

Presenter:

Martin Görner:

  • Developer Relations, Google (since 2011)
  • Started Mobipocket, a startup that later became the software part of the Amazon Kindle and its mobile variants

Links:

Tips on Watching:

  • Google any concepts you’re unfamiliar with.
  • Take your time with this one; 2 hours of screen time doesn’t count all of the Googling and processing time for this one.

Caltech Machine Learning iTunes ↓

Need to Know: If math is your thing, this course does a stellar job of building the mathematic intuition behind many machine learning models. Dr. Abu-Mostafa is a raconteur, includes useful visualizations, relevant real-world examples, and compelling analogies.

Loved:

  • First and foremost, this is a real Caltech course, meaning it’s not a watered-down version and contains fundamental concepts that are vital to understanding the mechanics of machine learning.
  • On iTunes, audio downloads are available, which can be useful for on-the-go learning.
  • Dr. Abu-Mostafa is a skilled speaker, making the 27 hours spent listening much easier!
  • Dr. Abu-Mostafa offers up some strong real-world examples and analogies which makes the content more relatable.
    • As an example, he asks students: “Why do I give you practice exams and not just give you the final exam?” as an illustration of why a testing set is useful. If he were to just give students the final, they would just memorize the answers (i.e., they would overfit to the data) and not genuinely learn the material. The final is a test to show how much students learn.
  • The last 1/2 hour of the class is always a Q&A, where students can ask questions. Their questions were useful to understanding the topic more in-depth.
  • The video and audio quality was strong throughout. There were a few times when I couldn’t understand a question in the Q&A, but overall very strong.
  • This course is designed to build mathematical intuition of what’s going on under the hood of specific machine learning models.
    • Caution: Dr. Abu-Mostafa uses mathematical notation, but it’s different from Andrew Ng’s (e.g., theta = w).
  • The final lecture was the most useful, as it pulled a lot of the conceptual puzzle pieces together. The course on neural networks was a close second!

Disliked:

  • Although it contains mostly evergreen content, being released in 2012, it could use a refresh.
  • Very passive form of learning, as it wasn’t immediately actionable.

Lecturer:

Dr. Yaser S. Abu-Mostafa:

  • Professor of Electrical Engineering and Computer Science at the California Institute of Technology
  • Chairman of Machine Learning Consultants LLC
  • Serves on a number of scientific advisory boards
  • Has served as a technical consultant on machine learning for several companies (including Citibank).
  • Multiple articles in Scientific American

Links:

Tips on Watching:

  • Consider listening to the last lesson first, as it pulls together the course overall conceptually. The map of the course, below, was particularly useful to organizing the information taught in the courses.

Image source: http://work.caltech.edu/slides/slides18.pdf

“Pattern Recognition & Machine Learning” by Christopher Bishop ↓

Need to Know: This is a very popular college-level machine learning textbook. I’ve heard it likened to a bible for machine learning. However, after spending a month trying to tackle the first few chapters, I gave up. It was too much math and pre-requisites to tackle (even with a multitude of Google sessions).

Loved:

  • The text of choice for many major universities, so if you can make it through this text and understand all of the concepts, you’re probably in a very good position.
  • I appreciated the history aside sections, where Bishop talked about influential people and the
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