Best Machine Learning Courses 2019

Machine learning is the process of making the machine learns by itself by pre-processing it using some technologies that have their base in mathematical concepts like Statistics. Once the machine is programmed to identify and learn by itself, one can process the humongous amount of data at rapid rates.

List of the Best Machine Learning Courses Available Online

The world is growing at a hyper-fast rate. It is, therefore, necessary for one to learn the emerging technologies that one can always stay ahead of their times. It is mandated for the professionals in the Information Technology industries to keep updating themselves with some of the best technologies to look out for more prospective jobs with a larger learning curve and more pay.

But for a professional who works for about 9-10 hours per day, reading about new technologies using books might seem very tough. Thus, these professionals can make use of the online learning platforms to learn courses that are the need of the hour. Machine learning is one such technology which is paying very high, and this list provides five courses handpicked as they are of great content.

Data Science, Deep Learning, & Machine Learning with Python
Python for Data Science and Machine Learning Bootcamp
Machine Learning Specialization
Data Science and Machine Learning Bootcamp with R
Machine Learning A-Z: Hands-On Python & R In Data Science

Thus, one can choose any one of the five courses mentioned above to master the concepts of machine learning using different technology. One might feel very tough to choose anyone at all five courses provide unique content and great learning experience.

Except for the course titled, “Machine Learning Specialization,” everything else is provided by Udemy whereas Coursera provides this course.

A layman explanation of the terms used in the course titles suggested

Data Science in simpler terms means the use of highly sophisticated technology to derive useful insights out of the data that has been fed into. There are several tools used in the field of Data Science. The tools are programmed using high-level languages like Python. The Python has great features that are fit for complex technologies like this. Deep Learning is a subset of Machine Learning, and Machine Learning is again a subset of Artificial Intelligence. R is a language that is the best for statistical analysis of data and to produce the best visualization.

Course 1: Machine Learning A-Z: Hands-On Python & R In Data Science

As the course name suggests, every Concept of Machine Learning is covered in this course. This course is a total value for money. This statement is proved by having a look at the salient features of the course.

Salient Features of the course:

The entire course duration is for 40.5 hours and is divided into 281 lectures
An umpteen number of 24 articles can be downloaded.

What are the topics covered in this course?

Why one should learn Machine Learning and why Machine Learning is stated to be the future?
How to install Anaconda and Python in MAC, windows, and Linux
The Pre-processing of Data and getting the datasets
The procedure to import libraries, import datasets, missing data and categorical data is explained.

Only after learning this course, one can get to know how many regression models are available in Machine Learning.

There are various Regression models, and every regression model is taught to solve using both Python and R.

Linear Regression
Multiple Linear Regression
The Intuition based Multiple Linear Regression
Polynomial Regression
Support Vector Regression
Decision Tree Regression

One can also get to learn of what are p-value in Multiple Linear Regression and the R Regression Template.

The concept of K Nearest neighbors and Support Vector Machines is learned using R and Python. These are some of the niche technologies which are covered as a part of this course?

Naïve Byes
Decision Tree Classification
Model Selection like K-Fold Cross Validation and Grid Search
Linear Discriminant Analysis
Thompson Sampling
Natural language processing
Principal Component Analysis

Course 2: Python for Data Science and Machine Learning Bootcamp

This course is the must prefer course by the ones who are already in the Data analytics field. This course gives more focus on the tools using Python for Data Science and Machine Learning whereas all the other courses focus more on the theoretical concepts that lie behind the Machine Learning Concepts. The range of concepts that are covered by this course would give an overall idea of the course, and the statement will be justified.

The range of topics covered under this course

One can learn the following by taking up this course
How is Python used for Machine Learning?
How are Pandas used for Data Analytics?
How is Numpy used in Numerical Data Analytics?
How Is Spark used for Big Data Analytics?
How are Scikit-Learn used for Python plotting?
How Plotly is used for the creation of designs that are very interactive with the user.
How is Seaborn used to plotting data driven by statistical analysis?
A basic Python Crash Course
About Jupter
About Numpy of how indexing and operations are carried out.
About Pandas of how DataFrames, Groupby, Missing data is found and how the data can be inputted and outputted.
About Seaborn of how one can plot distributions, categorical differences, matrices, and Regression
The Seaborn is also used for styling and coloring.
One can also learn about how one can plot Geographies. This is highly useful in the Remote Sensing data analytics and other data centers.

Python for Data Science and Machine Learning Bootcamp is a great course if one wishes to enjoy an overall learning experience. This course has the following features.

21.5 hours of excellent video lectures
Several complementary reading materials and other articles

A course that can be accessed anywhere using a good, supportive electronic device.

Course 3: Data Science, Deep Learning, & Machine Learning with Python

The course is of 12 hours’ time duration divided into 90 lectures. One can download three articles after purchasing the course for a nominal price. 90 minutes of the 12 hours are dedicated to mentoring the course takers on the basics of Python, how to get started with Python, downloading the necessary libraries for Python and Pandas. About 100 minutes are allotted for the learning of the statistical models; few practice sessions are dedicated for Python. Before one venture into the learning of the techniques used in Machine Learning, one must learn the predictive models like Linear Regression, Multivariate Regression, and Polynomial Regression.

Core concepts covered in this course

These are the core concepts covered in this course. The core concepts are tested by using some of the day-to-day practical examples and test cases.

Supervised Learning
Unsupervised Learning
Prevention of Overfitting in Polynomic Regressions
Concepts of Bayesian theory
Clustering methods like K-means
Decision Trees (This concept is used to make the candidate work on test cases like how many people might get hired in this company and such).
Ensemble learning
Support Vector Machines
Recommender Systems
Data mining concepts like K-Nearest Neighbors (This concept is used to provide the ratings for movies based on the polls conducted for the users).
Data Cleaning
Spark
Resilient Distributed Datasets
Deep Learning
Artificial Neural Networks
Tensorflow in Deep Learning
Recurrent Neural Networks
Convolutional Neural Networks
Keras

Course 4: Data Science and Machine Learning Bootcamp with R

This course is for those who have already mastered Machine Learning and Data Science with Python and wish to expand their knowledge horizons with R. Those who are in the Business Intelligence department or Banking industry, one would prefer learning more about Data Science and Machine Learning using R as there is a lot of presentations and graphs needs to be displayed to the customers. All of the Machine learning concepts are learned to handle using R. The term Bootcamp is used here to indicate the fact that one can learn all the major concepts within a short duration of time.

Salient features of this course:

17.5 hours of lecture videos
3 Free Supplementary materials and eight articles
Lifetime Access and Certificate of Completion will be provided if one completes listening all the video lectures, assignments and projects.

What is covered in this course?

One can learn how to write basic programs using R. A brief overview is given about this language.
The role of R in Data Science and how data is being effectively manipulated by using R.
The procedure to visualize data using R.
How R is used in Data Analytics
How R can be used for Machine Learning Algorithms
How R is used for scraping data from the web and handling all sorts of files

There are also benefits like, if one takes up this course, one can avail offer on the other courses too.
Some lectures are dedicated to how one can install this particular R software in MAC OS, Windows OS, and Linux OS. These help all the users as one might use a different Operating system.

What is the best feature of this course?

This course can be taken by those who don’t even know the basics of R. There are so many video lectures that teach the candidate about the basic concepts in R. First, the machine learning concept is explained followed by the usage of R for the concept.

Course 5: Machine Learning Specialization

This is a set of 4 courses provided by the University of Washington. This course is taught by some of the elite professors of that particular university. Each course is divided into some weeks. The first course covers the basics of Machine Learning, the foundational concepts; the second course covers the Regression concepts of Machine Learning, the third course covers the concepts of Classification in Machine Learning.

The Machine Learning course is rightly named specialization as it is no way lesser than that of a degree that provides specialization in a particular domain. Coursera provides this course. One will be provided with the course completion certificate only if one completes the project that has been assigned to the student.

It is not easy to fit in all the topics that are covered under this course as there are too many. This is again a reason for those who want to pursue a career in Machine Learning to take up this course and ace interviews.

Data Science, Data Analytics, and the prospects these courses offer to students

Data Science encompasses highly technical concepts. Data Analytics does not require one to possess as many concepts as Data Science. Data Science would require more deep knowledge of statistics, Bayesian theory, Machine Learning concepts whereas Data Analytics would require few technology skills like SQL, knowledge of Database, good decision-making skills.

The courses that are mentioned below would cater the needs of those who are already Data analysts and wish to get elevated to the post of Data Scientists. These courses are beneficial to the college students who are appearing for placements. The certificates carry a lot of value that a candidate can crack the rounds of hefty package companies as well as have it as an added advantage to their certificate file before appearing for an interview.

In all of the courses, one could come across that R and Python are used to solve Data Science problems and Data Analytics problems as well. But, one always doubts of do both mean the same and how are R and Python used to solve these problems. The procedure that R and Python use is explained much clearly in this article.

Thus, after having a read of this article, one can decide on which course he/she has to choose to meet his/her needs. The courses suggested here are from the websites that provide best quality content at the best possible prices that everyone can afford it and benefit out of it. The common aspect of the courses mentioned is the onetime payment. One need not pay again and again to have access to the videos. When one pays for once, he/she can access as many times wanted to learn on the go.

If you’re looking for the best Machine Learning courses, here is a right place to get started. All top-rated courses recommended here have been proven to be the best choices by most students in the world.