Can I Take Andrew Ng Into To Machine Learning In Python?

Pinnacle 7 Machine Learning Courses - 2022 Guide & Reviews

Learn Machine Learning this twelvemonth from these top courses. Curriculum and learning guide included.

With strong roots in statistics, Auto Learning is becoming one of the most heady and fast-paced computer science fields. There's an endless supply of industries and applications that car learning can brand more efficient and intelligent.

Chatbots, spam filtering, ad serving, search engines, and fraud detection are amongst just a few examples of how machine learning models underpin everyday life. Machine learning lets the states find patterns and create mathematical models for things that would sometimes be impossible for humans to do.

Unlike data scientific discipline courses, which contain topics like exploratory data analysis, statistics, communication, and visualization techniques, machine learning courses focus on educational activity just the machine learning algorithms, how they piece of work mathematically, and how to utilize them in a programming language.

Now, it's fourth dimension to go started. Here'south a TL;DR of the top five motorcar learning courses this year.

Best 7 Machine Learning Courses in 2022:

Machine Learning — Coursera
Deep Learning Specialization — Coursera
Car Learning Crash Course — Google AI
Machine Learning with Python — Coursera
Advanced Machine Learning Specialization — Coursera*
Machine Learning — EdX
Introduction to Machine Learning for Coders — Fast.ai

What makes an excellent auto learning course?

Later on several years of post-obit the e-learning mural and enrolling in countless machine learning courses from diverse platforms, like Coursera, Edx, Udemy, Udacity, and DataCamp, I've nerveless the best available motorcar learning courses.

Criteria

Each course in the list is subject to the following criteria.
The course should:

Strictly focus on car learning.
Utilize free, open up-source programming languages, such as Python or R.
Use gratis, open-source libraries for those languages. Some instructors and providers use commercial packages, so these courses are removed from consideration.
Contain programming assignments for practice and hands-on experience
Explicate how the algorithms work mathematically
Be self-paced, on-need, or bachelor every month or so
Have engaging instructors and interesting lectures
Have above-average ratings and reviews from various aggregators and forums

With that, the overall pool of courses gets culled down quickly, simply the goal is to aid y'all make up one's mind on a course worth your time and energy.

To immerse yourself and learn ML every bit fast and comprehensively equally possible, I believe you should as well seek out various books in addition to your online learning. Below are two books that significantly impacted my learning experience and remained at arm's length.

Two Excellent Book Companions

In addition to taking any of the video courses below, if you're relatively new to machine learning, you lot should consider reading the following books:

Introduction to Statistical Learning, which is also available for gratis online.

This book has detailed, straightforward explanations and examples to boost your overall mathematical intuition for many fundamental machine learning techniques. This volume is more on the theory side of things, only it does contain many exercises and examples using the R programming language.

Hands-On Motorcar Learning with Scikit-Learn and TensorFlow

A good complement to the previous book since this text focuses more on applying machine learning using Python. Together with any of the courses below, this book will reinforce your programming skills and immediately prove yous how to apply machine learning to projects.

Now, permit's get to the form descriptions and reviews.

This is the grade for which all other motorcar learning courses are judged. This beginner's course is taught and created past Andrew Ng, a Stanford professor, co-founder of Google Brain, co-founder of Coursera, and the VP that grew Baidu'southward AI team to thousands of scientists.

The form uses the open up-source programming linguistic communication Octave instead of Python or R for the assignments. This might be a deal-breaker for some, just Octave is a uncomplicated mode to acquire the fundamentals of ML if you're a consummate beginner.

Overall, the grade material is extremely well-rounded and intuitively articulated past Ng. The math required to understand each algorithm is completely explained, with some calculus explanations and a refresher for Linear Algebra. The class is fairly self-contained, merely some knowledge of Linear Algebra beforehand would help.

Provider: Andrew Ng, Stanford
Cost: Free to audit, $79 for Certificate

Class structure:

Linear Regression with One Variable
Linear Algebra Review
Linear Regression with Multiple Variables
Octave/Matlab Tutorial
Logistic Regression
Regularization
Neural Networks: Representation
Neural Networks: Learning
Advice for Applying Motorcar Learning
Machine Learning Arrangement Design
Support Vector Machines
Dimensionality Reduction
Anomaly Detection
Recommender Systems
Large Scale Machine Learning
Application Instance: Photo OCR

All of this is covered over xi weeks. If you can commit to completing the whole course, y'all'll have a expert base knowledge of machine learning in almost iv months.

After that, you tin can comfortably move on to a more advanced or specialized topic, like Deep Learning, ML Engineering, or anything else that piques your involvement.

This is undoubtedly the best course to start with a newcomer.

Also taught by Andrew Ng, this specialization is a more than avant-garde class serial for anyone interested in learning virtually neural networks and Deep Learning, and how they solve many bug.

The assignments and lectures in each course utilize the Python programming language and use the TensorFlow library for neural networks. This is naturally an excellent follow-up to Ng's Motorcar Learning course since you'll receive a like lecture style merely now will exist exposed to using Python for automobile learning.

Provider: Andrew Ng, deeplearning.ai
Price: Free to inspect, $49/month for Certificate

Courses:

Neural Networks and Deep Learning
- Introduction to Deep Learning
- Neural Network Basics
- Shallow Neural Networks
- Deep Neural Networks

Improving Neural Networks: Hyperparameter Tuning, Regularization, and Optimization
Structuring Machine Learning Projects
Convolutional Neural Networks
Sequence Models

To empathise the algorithms presented in this course, you should already be familiar with Linear Algebra and machine learning in general. If you need some suggestions for picking upwardly the math required, see the Learning Guide towards the stop of this article.

This grade comes from Google AI Education, a completely free platform that'due south a mix of manufactures, videos, and interactive content.

The Machine Learning Crash Class covers the topics needed to solve ML problems as soon as possible. Like the previous course, Python is the programming language of pick, and TensorFlow is introduced. Each main section of the curriculum contains an interactive Jupyter notebook hosted on Google Colab.

Video lectures and articles are succinct and straightforward, so you'll be able to quickly motility through the course at your own pace.

Provider: Google AI

Cost: Free

Curriculum (simplified)

Linear and Logistic Regression
Classification
Training and loss
Reducing Loss - slope descent, learning rates
TensorFlow
Overfitting
Preparation sets, splitting, and validation
Feature Engineering and cleaning data
Feature Crosses
Regularization - L1 and L2, Lambda
Model performance metrics
Neural Networks - single and multi-class
Embeddings
ML Engineering science

This is the best pick in this list if you have tinkered with ML but are looking to cover all your bases. The grade discusses many nuances of machine learning that may otherwise accept hundreds of hours to acquire serendipitously.

There doesn't seem to exist a document on completion at the time of writing, then if that's something yous're looking for, this course may not exist the best fit.

Another beginner course, but this 1 focuses solely on the about primal motorcar learning algorithms. The teacher, slide animations, and explanation of the algorithms combine very nicely to give you an intuitive feel for the basics.

This course uses Python and is somewhat lighter on the mathematics behind the algorithms. With each module, you lot'll get a chance to spool upwards an interactive Jupyter notebook in your browser to work through the new concepts you just learned. Each notebook reinforces your knowledge and gives yous concrete instructions for using an algorithm on real data.

Provider: IBM, Cognitive Course
Price: Free to audit, $39/calendar month for Certificate

Course structure:

Intro to Car Learning
Regression
Nomenclature
Clustering
Recommender Systems
Final Project

1 of the best things nigh this course is the practical advice given for each algorithm. When introduced to a new algorithm, the instructor provides you lot with how information technology works, its pros and cons, and what sort of situations yous should employ information technology in. These points are oft left out of other courses and this information is important for new learners to sympathise the broader context.

#v Advanced Auto Learning Specialization — Coursera

Russian-Ukraine War

Due to the Russian invasion of Ukraine, Coursera is no longer offering this class until further notice.

This is another avant-garde series of courses that casts a very wide internet. If y'all are interested in covering as many automobile learning techniques every bit possible, this Specialization is the cardinal to a balanced and extensive online curriculum.

The didactics in this course is fantastic: extremely well-presented and concise. Due to its advanced nature, you will demand more math than any other courses listed and then far. If you have already taken a beginner course and brushed up on linear algebra and calculus, this is a skillful choice to fill out the rest of your machine learning expertise.

Much of what's covered in this Specialization is pivotal to many machine learning projects.

Provider: National Research University College Schoolhouse of Economics
Cost: Costless to audit, $49/month for Document

Courses:

Introduction to Deep Learning
- Intro to Optimization
- Intro to Neural Networks
- Deep Learning for Images
- Unsupervised Representation Learning
- Deep Learning for Sequences
- Concluding Project

How to Win Information Science Competitions: Acquire from Pinnacle Kagglers
Bayesian Methods for Machine Learning
Practical Reinforcement Learning
Deep Learning in Calculator Vision
Tongue Processing
Addressing the Big Hadron Collider Challenges by Car Learning

It takes about eight-ten months to consummate this series of courses, so if you lot first today, in a niggling under a yr, you'll have learned a massive corporeality of machine learning and be able to starting time tackling more than cutting-edge applications.

Throughout the months, you will likewise be creating several real projects that consequence in a computer learning how to read, see, and play. These projects will be great candidates for your portfolio and will consequence in your GitHub looking very active to whatsoever interested employers.

This is an advanced course with the highest math prerequisite out of whatever other course on this list. Yous'll need a very firm grasp of Linear Algebra, Calculus, Probability, and programming. The form has interesting programming assignments in either Python or Octave, but the class doesn't teach either language.

One of the biggest differences with this grade is the coverage of the probabilistic arroyo to machine learning. If you've been interested in reading a textbook, similar Motorcar Learning: A Probabilistic Perspective — which is one of the most recommended data science books in Principal'southward programs — then this course would exist a fantastic complement.

Provider: Columbia
Price: Free to audit, $300 for Certificate

Course structure:

Maximum Likelihood Estimation, Linear Regression, Least Squares
Ridge Regression, Bias-Variance, Bayes Rule, Maximum a Posteriori Inference
Nearest Neighbor Classification, Bayes Classifiers, Linear Classifiers, Perceptron
Logistic Regression, Laplace Approximation, Kernel Methods, Gaussian Processes
Maximum Margin, Back up Vector Machines (SVM), Trees, Random Forests, Boosting
Clustering, K-Means, EM Algorithm, Missing Information
Mixtures of Gaussians, Matrix Factorization
Not-Negative Matrix Factorization, Latent Factor Models, PCA and Variations
Markov Models, Hidden Markov Models
Continuous Country-space Models, Association Analysis
Model Selection, Adjacent Steps

Many of the topics listed are covered in other courses aimed at beginners, merely the math isn't watered downwards here. If y'all've already learned these techniques, are interested in going deeper into the mathematics behind ML, and want to piece of work on programming assignments that derive some of the algorithms, and then give this class a shot.

Fast.ai produced this excellent, gratuitous machine learning grade for those that already have roughly a year of Python programming feel.

Information technology'due south phenomenal how much fourth dimension and endeavour the founders of Fast.ai take put into this course — and other courses on their site. The content is based on the University of San Diego's Data Science plan, and so you'll discover that the lectures are done in a classroom with students, similar to the MIT OpenCourseware style.

The course has many videos, some homework assignments, extensive notes, and a discussion board. Unfortunately, you won't find graded assignments and quizzes or certification upon completion, so Coursera/Edx would be a better route for yous if you'd rather have those features.

Much of the class content is practical, and then you'll learn how to not simply how to use the ML models but also launch them on cloud providers, like AWS.

Provider: Fast.ai

Cost: Costless

Grade Structure:

Introduction to Random Forests
Random Forest Deep Dive
Performance, Validation, and Model Interpretation
Feature Importance. Tree Interpreter
Extrapolation and RF from Scratch
Information Products and Live Coding
RF From Scratch and Slope Descent
Slope Descent and Logistic Regression
Regularization, Learning Rates, and NLP
More NLP and Columnar Data
Embeddings
Complete Rossmann. Ethical Issues

This course is excellent if you're a programmer who wants to learn and apply ML techniques, simply I find at that place is 1 drawback: they teach machine learning through the use of their open-source library (called fastai), which is a layer over other machine learning libraries, like PyTorch.

If you lot just care about using ML for your project and don't care about learning something like PyTorch, and so the fastai library offers convenient abstractions.

Learning Guide

Now that you've seen the course recommendations, here'due south a quick guide for your learning machine learning journey. Showtime, we'll impact on the prerequisites for near machine learning courses.

Course Prerequisites

More advanced courses will require the following knowledge earlier starting:

Linear Algebra
Probability
Calculus
Programming

These are the general components of being able to sympathise how machine learning works under the hood. Many beginner courses unremarkably ask for at least some programming and familiarity with linear algebra nuts, such equally vectors, matrices, and their annotation.

The outset course in this list, Car Learning by Andrew Ng, contains refreshers on most of the math y'all'll need, only it might exist challenging to learn machine learning and Linear Algebra if you haven't taken Linear Algebra before at the same fourth dimension.

If you need to brush up on the math required, check out:

Matrix Algebra for Engineers from Coursera to cover Linear Algebra
Fat Chance: Probability from the Ground Up from EdX to comprehend Probability
Single Variable Calculus from MIT OpenCourseWare to cover intro Calculus.
Programming for Everybody course on Coursera to learn Python programming

I'd recommend learning Python since the majority of good ML courses utilize Python. If y'all take Andrew Ng's Machine Learning course, which uses Octave, you should learn Python either during the course or after since y'all'll need it eventually. Additionally, some other splendid Python resource is dataquest.io, which has many free Python lessons in their interactive browser environs.

Afterward learning the prerequisite essentials, yous can first to actually understand how the algorithms piece of work.

Key Algorithms

There's a base set of algorithms in automobile learning that everyone should be familiar with and accept experience using. These are:

Linear Regression
Logistic Regression
one thousand-Means Clustering
k-Nearest Neighbors
Back up Vector Machines (SVM)
Decision Copse
Random Forests
Naive Bayes

These are the essentials, just at that place are many, many more. The courses listed above contain essentially all of these with some variation. Understanding how these techniques piece of work and when to use them volition exist critical when taking on new projects.

After the basics, some more advanced techniques to acquire would be:

Ensembles
Boosting
Dimensionality Reduction
Reinforcement Learning
Neural Networks and Deep Learning

This is just a outset, but these algorithms are what y'all see in some of the most interesting machine learning solutions, and they're applied additions to your toolbox.

And simply like the basic techniques, with each new tool, you learn you should go far a habit to apply it to a project immediately to solidify your understanding and have something to go back to when in need of a refresher.

Tackle a Project

Learning auto learning online is challenging and extremely rewarding. It's of import to think that just watching videos and taking quizzes doesn't hateful you're really learning the material. You'll acquire fifty-fifty more if you lot have a side projection you're working on that uses different data and has other objectives than the course itself.

As soon equally you lot first learning the nuts, you should look for interesting information that you can use while experimenting with your new skills. The courses in a higher place volition give you some intuition on when to apply certain algorithms, and so it'southward a good practise to apply them in a project of your own immediately.

Through trial and error, exploration, and feedback, you'll discover how to experiment with different techniques, how to measure results, and how to classify or make predictions. For some inspiration on what kind of ML project to take on, see this list of examples.

Tackling projects gives yous a meliorate high-level understanding of the machine learning landscape. As you get into more advanced concepts, like Deep Learning, in that location's nearly an unlimited number of techniques and methods to sympathize.

Read New Inquiry

Machine learning is a apace developing field where new techniques and applications come out daily. Once you're past the fundamentals, you lot should exist equipped to work through some research papers on a topic that piques your involvement.

In that location are several websites to go notified about new papers matching your criteria. Google Scholar is always a good place to start. Enter keywords like "machine learning" and "Twitter", or whatsoever else yous're interested in, and hit the trivial "Create Alarm" link on the left to get emails.

Make it a weekly habit to read those alerts, browse through papers to see if their worth reading, and and so commit to understanding what's going on. If it has to exercise with a project yous're working on, see if y'all can apply the techniques to your own problem.

Wrapping Up

Car learning is incredibly enjoyable and exciting to larn and experiment with, and I promise you found a grade in a higher place that fits your own journey into this heady field.

Auto learning makes up one component of Information Science. If you're also interested in learning about statistics, visualization, information analysis, and more be sure to check out the top data science courses, which is a guide that follows a similar format to this one.

Lastly, if you have whatever questions or suggestions, feel complimentary to exit them in the comments below.

Thanks for reading, and take fun learning!