This is an amazing collection of tools about Machine learning, so although this post is a little long, I list it in its entirety as a repository, including all the links to the different resources.
The Original can be found at:
on Techgrabyte at https://techgrabyte.com/
Machine Learning
is a beautiful field to work on, it is full of fun and if you are one
who is looking to learn ML, then you are at right place, here today I’ll
show some of the
best machine learning course that will not only save your money but it will also offer you a quality education.
And the fun part is that you don’t even have to leave your room, just
sit down on your chain, pull the desk closer, take a notebook and pen
and finally grab the most import thing, a cup of coffee and go full-on
it.
This all
machine learning courses
are perfectly designed and divided into a curriculum that will take you
from an absolute beginner to an expert in Machine Learning, who knows
how to build real-world projects.
From the listed machine learning course, you will learn the basics
and fundamentals of Machine Learning, how it works internally, how to
train a model and also how to implement the knowledge that you will
gain.
For a general idea, this machine learning courses will teach you about various different concepts like
parameter learning,
logistic regression model,
neural networks,
application of neural networks,
cost function, and backpropagation and much more.
Another thing is that all the machine learning course that we are
going to see here are primarily selected on the quality of content,
language simplicity, and public reviews.
The only prerequisites that are required for this all machine
learning courses are the little understanding and knowledge of
programming languages like R and Python and little basics about
mathematics.
That been said, let us start our journey.
Don’t forget to check the bonus.
1. Programming Language
If you have already master or learning a programming language that
heavily going to contribute in creating Machine Learning based stuff,
then you can directly move to the next point, but if you are new here
then wait I have a surprise for you.
A blank mind is always good as it is free from confusion, if you
haven’t learned any language yet, then at this right moment start with
Python or with
R or with
JavaScript, I’ll suggest you Python.
I mostly suggest beginners go with
Python for Machine Learning, the prime reason behind this is that Python is quick, fast and easy to understand and learn.
Medium has a great article on
Why Python is the most popular language used for Machine Learning, I’m so mad for
Python that I can talk about it all day, but to keep the article short you can head over
this article and can clear doubts and questions, you can also ask them in below comment box.
And if I talk about
R, then
R
is good for ad-hoc analysis and exploring data sets, R has a steep
learning curve, but people without programming experience, find it
overwhelming.
R also don’t have lots of libraries that Python actually offers, also the community of
Python is humongous, so you can easily find someone who can help you in your errors.
Now the surprise that I talk before.
If you are thinking to learn
R or Python, then below are the links from where you can download the most popular and useful books for Python and R for
free.
This book will help you to build the core foundation on which you can learn advanced concepts at a greater speed.
2. Udacity’s Intro to Machine Learning
The clarity in thoughts is must when you are learning something, especially when it is Machine Learning.
Udacity’s
Intro to Machine Learning is the best machine learning course that will
teach and will show you what machine learning actually is.
Another reason that I suggest this machine learning course is that I
many time saw that the students have their some pre-assumptions,
confusion, and doubts on machine learning, Udacity’s this course will
give you a better understanding of ML.
This course is about 10-weeks long and it will teach you the
end-to-end process of investigating data through a machine learning
lens.
It will teach you how to extract and identify useful features that
best represent your data, the most important machine learning
algorithms, and how to evaluate the performance of your machine learning
algorithms.
I also recommend you to take the foundational
Intro to Data Science course
which deals with Data Manipulation, Data Analysis, Data Communication
with Information Visualization, and Data at Scale, this will helps you
understand machine learning concepts more easily.
Intro to Machine Learning will be taught by instructors Sebastian
Thrun and Katie Malone, the instructors expect the beginners to know
basic
statistical concepts and
Python.
3. Stanford Machine Learning Course
This machine learning course is a very special course, as it is taught by
Andrew Y. Ng (my idol), Andrew is a prestige name in the field of machine learning. He is a co-founder of
Coursera,
Baidu’s Chief Scientist and a former head of
Google Brain.
In this machine learning course, you will learn about the most effective machine learning techniques.
You will learn about some of Silicon Valley’s best practices in
innovation as it pertains to ML and AI. This course provides a broad
introduction to machine learning, data mining, and statistical pattern
recognition.
Topics that these machine learning course will cover include:
(i) Supervised learning, parametric and non-parametric algorithms, support vector machines, neural networks.
(ii) Unsupervised learning clustering, dimensionality reduction, recommender systems, deep learning.
(iii) Best practices in machine learning -bias/variance theory, innovation process in machine learning and AI.
This machine learning course also offers numerous case studies and
applications so that you’ll learn how to use ML algorithms to building
smart robots (perception, control), text understanding (web search,
anti-spam), computer vision, medical informatics, audio, database
mining, and other areas.
After doing this course, you will gain skills like Logistic Regression, Artificial Neural Network, and Machine Learning.
4. Google Machine Learning Course
With no doubt,
Google
is the leading company in the field of ML, and Google is offering some
amazing machine learning courses that is absolutely free and that will
teach you many valuable concepts of ML.
This free Machine Learning Course will teach you how to recognize the
relative impact of data quality and size to algorithms, set informed
and realistic expectations for the time to transform the data.
This course will also explain you a typical process for data
collection and transformation within the overall ML workflow, how to
collect raw data and how to construct a data set, and split your dataset
with considerations for imbalanced data and about transform numerical
and categorical data.
This course will have 25 lessons, 40+ exercises, interactive
visualizations of algorithms in action, real-world case studies, and
mind-blowing lectures from Google researchers.
This course will answer all questions like :
1) How does machine learning differ from traditional programming?
2) What is a loss, and how do I measure it?
3) How does gradient descent work?
4) How do I determine whether my model is effective?
5) How do I represent my data so that a program can learn from it?
6) How do I build a deep neural network?
5. Mathematics for Machine Learning Specialization
Mathematics is the foundation of ML, all the algorithms and programs
that you will write, will somehow always be directly or indirectly
related to Mathematics.
It is the foundation of a student learning ML, and this course called
“Mathematics for Machine Learning Specialization” is all designed to
make your foundation solid strong.
It
is one of the most recommend machine learning course by experts. This
course will help in getting speed in the underlying mathematics,
building an intuitive understanding, and relating it to Machine Learning
and Data Science.
This course will teach you about what is linear
algebra and how to it relates to data, what are vectors and matrices and
how to work with them and use them for data fitting.
In the second section called Multivariate Calculus
of this course, you will learn about how to optimize fitting functions
to get good fits to data, what is calculus and how to uses it.
The third section, Dimensionality Reduction with
Principal Component Analysis, will teach you about how to use the
mathematics from the first and second sections, and how to compress
high-dimensional data.
This course is of intermediate difficulty and will require basic
Python and
Numpy, knowledge, which you can quickly learn through above-mentioned books or from youtube.
At the end of this specialization, you will have gained the
prerequisite mathematical knowledge to continue your journey and take
more advanced courses in machine learning.
6. Machine Learning Fundamentals
In this 6th machine learning course, you will learn many
different things and you will also use the knowledge that you gain from
previous courses.
This course will be taught by
Sanjoy Dasgupta, Professor of Computer Science and Engineering, UC San Diego.
In this course, you will learn about the classification,
regression, and conditional probability estimation, generative and
discriminative models, linear models and extensions to nonlinearity
using kernel methods.
You will also learn about ensemble methods, like boosting,
bagging, random forests, representation learning: clustering,
dimensionality reduction, autoencoders, deep nets.
The reason behind the selection of this course is that it also offers real-world case studies of Machine Learning.
By this real-world case studies you will learn how to classify
images, identify salient topics in a corpus of documents, partition
people according to personality profiles, and automatically capture the
semantic structure of words and use it to categorize documents.
This course also offers an additional section called Data Science
MicroMasters program, where you will learn a variety of supervised and
unsupervised learning algorithms and the theory behind those algorithms.
Note that all programming examples and assignments will be in
Python, using Jupyter notebooks.
After successfully learning this course, you will be able to analyze
many different types of data and to build descriptive and predictive
models.
7. Machine Learning: Classification
Machine Learning: Classification is one of the most enjoyable and knowledge riched machine learning course on this list.
It is divided into two sections, the first one is based on analyzing
sentiment, where you will create models that predict a class
(positive/negative sentiment) from input features (text of the reviews,
user profile information).
The second section will have loan default prediction, where you will
tackle financial data, and predict when a loan is likely to be risky or
safe for the bank.
These tasks are examples of classification, one of the most widely
used areas of machine learning, with a broad array of applications,
including ad targeting, spam detection, medical diagnosis, and image
classification.
This machine learning course will teach you how to create classifiers
that provide state-of-the-art performance on a variety of tasks.
You will become familiar with the most successful techniques, which
are most widely used in practice, including logistic regression,
decision trees and boosting.
The major things this Machine Learning will cover be the input and
output of a classification model, tackle both binary and multi-class
classification problems, implement a logistic regression model for
large-scale classification.
It will also teach you how to create a non-linear model using
decision trees, improve the performance of any model using boosting and
how to scale your methods with stochastic gradient ascent.
In addition to this, you will be able to design and implement the
underlying algorithms that can learn these models at scale, using
stochastic gradient ascent.
You will implement these technologies on real-world, large-scale machine learning tasks.
You will also address significant tasks, and real-world applications
of ML, including handling missing data and measuring precision and
recall to evaluate a classifier.
This course is hands-on, action-packed, and full of visualizations
and illustrations of how these techniques will behave on real data.
It also included optional content in every module, covering advanced topics for those who want to go even deeper.
The concepts in this course will be implemented with Python.
8. Machine Learning: Regression
In this machine learning course, you will learn more about
regression in ML, regression is a measure of the relation between the mean value of one variable and corresponding values of other variables.
This course is divided into a number of sections in order to build an easy understanding.
The first section will be of predicting house prices, where you will
create models that predict a continuous value from input features like
square footage, number of bedrooms and bathrooms.
Other applications range from predicting health outcomes in medicine,
stock prices in finance, and power usage in high-performance computing,
to analyzing which regulators are important for gene expression.
In this machine learning course, you will explore regularized linear
regression models for the task of prediction and feature selection.
You will be able to handle very large sets of features and select between models of various complexity.
You will also analyze the impact of aspects of your data such as
outliers on your selected models and predictions. To fit these models,
you will implement optimization algorithms that scale to large datasets.
The major things this Machine Learning will cover will be the input
and output of a regression model, compare and contrast bias and variance
when modeling data, estimate model parameters using optimization
algorithms.
It will also cover tuning parameters with cross-validation, analyze
the performance of the model, describe the notion of sparsity and how
LASSO leads to sparse solutions, build a regression model to predict
prices using a housing dataset.
The concepts in this course will be implemented with Python.
9. Machine Learning for Data Science and Analytics
Though machine learning is sub-branch of AI, I find it mostly close to
Data Science.
When you learn machine learning and play with it you will find that
you are mostly interacting with data and using all its concepts, and at
some point, you will also start feeling this way.
These days machine learning is now mostly using for
searching the web,
placing ads,
credit scoring,
stock trading and for many other applications, by doing this course you will also be able to do such things.
This data science course is an introduction to machine learning and algorithms.
From this machine learning course, you will develop a basic
understanding of the principles of machine learning and derive practical
solutions using predictive analytics.
This course will also help you understand why algorithms play an essential role in Big Data analysis.
In this course, you will learn what machine learning is and how it is related to statistics and data analysis.
At the start of this course, you learn how
machine learning uses computer algorithms to search for patterns in
data, how to use data patterns to make decisions and predictions with
real-world examples from healthcare involving genomics and preterm
birth.
It will also teach you about how to uncover
hidden themes in large collections of documents using topic modeling,
how to prepare data, deal with missing data and create custom data
analysis solutions for different industries, basic and frequently used
algorithmic techniques like sorting, searching, greedy algorithms and
dynamic programming
This course will be taught by Ansaf Salleb-Aouissi, Cliff Stein, David Blei, Itsik Peer, Mihalis Yannakakis and Peter Orbanz.
10. Foundations of Data Science: Prediction and Machine Learning
I know you might be getting sucked or bore with predictions
and regression, but believe me, this machine learning course will
definitely help you to level up your knowledge and skills that you have
achieved through previous courses.
This course called “Foundations of Data Science: Prediction and
Machine Learning” mainly focuses on regression and classification, to
automatically identify patterns in your data and make better
predictions.
One of the principal responsibilities of a data scientist is to make
reliable predictions based on data. When the amount of data available is
enormous, it helps if some of the analysis can be automated.
Machine learning is a way of identifying patterns in data and using
them to automatically make predictions or decisions. In this data
science course, you will learn the basic concepts and elements of
machine learning.
The two main methods of machine learning you will focus on are regression and classification.
Regression is used when you seek to predict a numerical quantity.
Classification is used when you try to predict a category e.g, given
information about a financial transaction, predict whether it is
fraudulent or legitimate.
For regression, you will learn how to measure the correlation between
two variables and compute a best-fit line for making predictions when
the underlying relationship is linear.
The course will also teach you how to quantify the uncertainty in
your prediction using the bootstrap method. These techniques will be
motivated by a wide range of examples.
For classification, you will learn the k-nearest neighbor
classification algorithm, learn how to measure the effectiveness of your
classifier, and apply it to real-world tasks including medical
diagnoses and predicting genres of movies.
The course will highlight the assumptions underlying the techniques
and will provide ways to assess whether those assumptions are good. It
will also point out pitfalls that lead to overly optimistic or
inaccurate predictions.
The major concepts that this course
covers are fundamental concepts of machine learning. linear regression,
correlation, and the phenomenon of regression to the mean,
classification using the k-nearest neighbors’ algorithm, how to compare
and evaluate the accuracy of machine learning models, basic probability
and Bayes’ theorem.
This course will be taught by Ani Adhikari Teaching Professor of Statistics
UC Berkeley, John DeNero Giancarlo Teaching Fellow in the EECS Department
UC Berkeley, David Wagner Professor of Computer Science
UC Berkeley.
11. Dynamic Programming: Applications In Machine Learning and Genomics
If you look at two genes that serve the same purpose in two different
species, how can you rigorously compare these genes in order to see how
they have evolved away from each other? And the answer to this question
will be in this 11th machine learning course.
Our 11th machine learning course will be taught Pavel Pevzner Ronald R. Taylor Professor of the Computer Science t
he University of California, San Diego, Phillip Compeau Assistant Teaching Professor
Carnegie Mellon University.
In this course, you will get introduced to Algorithms and Data
Structures MicroMasters program, where you will see how the dynamic
programming paradigm can be used to solve a variety of different
questions related to pairwise and multiple string comparison.
This course will teach you dynamic programming and how it applies to
basic string comparison algorithms, sequence alignment, including how to
generalize dynamic programming algorithms to handle different cases,
hidden Markov models.
It will also teach you about how to find the most likely sequence of
events given a collection of outcomes and limited information, Machine
learning in the sequence alignment.
In the second part of the course, you will see how a powerful machine
learning approach, using a Hidden Markov Model, can dig deeper and find
relationships between less obviously related sequences, such as areas
of the rapidly mutating HIV genome.
This is one of the amazing course we have up to in this list of best machine learning courses.
12. Introduction to Machine Learning for Coders
Our next course “Introduction to Machine Learning for Coders” will be taught by Jeremy Howard.
This course is little different from other machine learning courses,
as it will not talk about ML basics but it will directly teach you about
most the important machine learning models, including how to create
them yourself from scratch, as well as key skills in data preparation,
model validation, and building data products.
There are around 24 hours of lessons, and you should plan to spend around 8 hours a week for 12 weeks to complete the material.
The course is based on lessons recorded at the University of San Francisco for the
Masters of Science in Data Science program.
Jeremy Howard assumes that you have at least one year of coding
experience, and also remember what you learned in high school math or
are prepared to do some independent study to refresh your knowledge.
Bonus:) Robotics: Vision Intelligence and Machine Learning
And here is the time of bonus,
Robotics:
Vision Intelligence and Machine Learning is a kind of machine learning
course that will help you put your all knowledge in building something
practical.
This course all depends on your personal level of interest but I highly recommend it to you.
From this machine learning course, you will learn how to design robot
vision systems that avoid collisions, safely work with humans and
understand their environment. In this course, you will come across
visual intelligence.
Visual intelligence allows a robot to “sense” and “recognize” the
surrounding environment. It also enables a robot to “learn” from the
memory of past experiences by extracting patterns in visual signals.
In this course, you will understand how Machine Learning extracts
statistically meaningful patterns in data that support classification,
regression and clustering.
The will teach you about the fundamentals of image filtering and
tracking, and how to apply those principles to face detection,
mosaicking, and stabilization.
You will also learn how to use geometric transformations to determine
3D poses from 2D images for augmented reality tasks and visual odometry
for robot localization, how to recognize objects and the basics of
visual learning and neural networks for the purpose of classification.
Then by studying Computer Vision and Machine Learning together, you
will be able to build recognition algorithms that can learn from data
and adapt to new environments.
By the end of this course, you will be able to program vision
capabilities for a robot such as a robot localization as well as to
object recognition using machine learning.
Projects in this course will utilize MATLAB and OpenCV and will
include real examples of video stabilization, recognition of 3D objects,
coding a classifier for objects, building a perceptron, and designing a
convolutional neural network (CNN) using one of the standard CNN
frameworks.
Conclusion :
Machine Learning is rising and if you start learning it today,
believe me after some years you will have a massive advantage over
others. With this knowledge, you can work in companies like
DeepMind or
FaceBook AI Research, you can also start your own start-up that servers AI services or AI-based products.
If you have decided to do these courses mentioned in the list of best
Machine Learning courses, then feel free to share your experience and
difficulties with me by email- techgrabyte@gmail.com.
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