Learn Machine Learning Online With Coursera

Online Machine Learning courses from Coursera

Looking to fast-tack your career as A Machine Learning Scientist? One of these online Machine Learning certifications will help you realize your goals and better your life

While Machine Learning and Artificial Intelligence have been around since the mid-twentieth century, they have only gained popularity in recent times. Why is this so and why is Machine Learning gaining this much traction, and what is it in the first place? Most important, how and where can you learn Machine Learning online? These are the questions we are going to answer in this post. Read on!

What is Machine Learning?

Machine Learning is a branch of computer science that gives the computer the ability to learn and perform tasks without explicit programming. The main focus of Machine Learning is to provide algorithms that can be trained to execute tasks.

Machine Learning is closely related to mathematical optimization as well as computational statistics. It contains multiple topics such as Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, and Reinforcement Learning. All these have their own unique use cases and algorithms.

Why become an expert in Machine Learning?

Currently valued at $3.5 billion, the Machine Learning industry is expected to grow to $59.8 billion by 2025. This is certainly a huge growth potential. According to Forbes, Machine Learning patents grew at 34% Compound Annual Growth Rate (CAGR) in the periods between 2013 and 2017. The International Data Corporation (IDC) projected that spending in Machine Learning and Artificial Intelligence could grow from $12 billion to $57.6 billion between 2017 and 2021.

There is no doubt that the demand for knowledgeable Machine Leaning experts is at an all-time high. And thanks to this demand, there has been a better time to become an expert in Machine Learning than now.

Career paths in Machine Learning

As already mentioned, Machine Learning is becoming increasingly popular. Consequently, there are many career paths are opening up in the field of Machine Learning that are well-paying such as NLP scientist, Data Scientist, and Machine Learning Engineer among others.

  1. Machine Learning Engineer

A Machine Learning Engineer is a professional who uses programming languages like Java, Python, and Scala to run various experiments with various ML libraries. Some of the key skills required for this career include Probability, Programming, Data Modeling, Statistics, Machine Learning Algorithms, and System Design among others.

Most people ask the difference between a Machine Learning Engineer and a Data Scientist.

Well, a Data Scientists analyzes data with the goal of producing actionable insights. These are then used by business leaders to make business. Machine Learning Engineer, on the other hand, analyzes data to create multiple machine learning algorithms that autonomously run with minimal human intervention. In other words, a Data Scientist creates output for humans while Machine Learning Engineers create output for the machines.

  1. Data Scientist

A Data Scientist utilizes advanced analytics technologies like Machine Learning and Predictive Modeling to gather, analyze and interpret large amounts of data for purposes of creating actionable insights. These are then used by business executives for decision making. Thus, Machine Learning is an indispensable skill for a Data Scientist alongside other skills like knowledge of statistical research techniques and data mining. Additionally, knowledge of big data tools and platforms like Pig, Hadoop, and Spark as well as programming languages like Python, SQL, Perl, Scala, are needed for Data Science.

  1. Natural Language Processing Scientist

Natural Language Processing, abbreviated as NLP, empowers machines to understand human language. This machines that machines can be made to talk like you, in your language! An NLP Scientist basically creates a machine that can learn speech patterns and translate spoken words into other languages. Thus, an NLP Scientist should have fluency in spelling, syntax, and grammar of at least one language in addition to machine learning so they can train the machine on similar skills.

  1. Business Intelligence Developer

A Business Intelligence Developer utilizes Machine Learning and Data Analytics to gather, analyze and interpret large amounts of data with the goal of producing actionable insights that can be used for business decision making purposes. To be efficient in this, a Business Intelligence Developer should be knowledgeable in both multidimensional and relational databases as well as programming languages like Python, SQL, Scala, and Perl among others. They should also be knowledgeable in various business analytic services like Power BI.

  1. Human-Centered Machine Learning Designer

Human-Centered Machine Learning has to do with Machine Learning algorithms that are focused on humans. A good example of this is Netflix that provides viewers with movie suggestions based on their preferences and viewing history.

A Human-Centered Machine Learning Designer builds various that are capable of performing Human-Centered Machine Learning based on pattern recognition and information processing. This allows the machine to “learn” human preferences without analyzing complex programs that accounts for user scenarios.

Why you should sign up for an online Machine Learning course

  1. It is career focused

An online machine learning course is an excellent option when you are hoping to achieve specific career goals in a short time frame. These courses are focused and tend to come with projects that can teach you how apply the skills learned in real-life. Projects like building a phishing website detector or a predictive model for housing prices using LR algorithms or KNN algorithms, or building an MNIST Classifier provides learners with curated content that you need to upskill in these specific areas. University education, on the other hand, tends to be much longer with undefined ROI since it is not always geared towards any specific goals.

  1. It is time saving

An online machine learning certification program generally takes between 1-6 months to complete depending on your learning pace and goals. A University degree, on the other hand, can take up to four years to complete. It is questionable whether you need to spend so much time pursuing a career in machine learning as a good certificate course is certainly all you need to succeed.

  1. It is cost effective

Online Machine Learning courses do not cost as much as university degrees for a number of reasons:

  1. The programs take lesser periods to complete, meaning that operational costs are lower too
  2. These courses are exclusively offered online, thus no operational overhead costs. Learners too do not have to worry about campus visits, which can add up to the overall learning cost.

4. It is flexible

One major advantage of studying Machine Learning online is that you can sign up and access your learning platform online from any location. Whether you are at home, work, or on a vacation, online courses afford you the flexibility to learn from any location as long as you have internet access.

  1. Learning is highly engaging and customized

Major online learning platforms rely on learner feedback to determine which tutors are work keeping and which ones are not. They rely on this important feedback when crafting future online courses. You cannot find this level of personalized attention from the traditional university.  Additionally, online education can help learners harness their strengths while minimizing their weaknesses by providing project-focused learning, complete with guided assistance with the help of teaching assistants and course tutors.

The Top 7 Online Machine Learning Courses from Coursera

Finding the best Machine Learning course online can be a daunting task if you do not know where to begin your search. These highly rated courses from Coursera (read review here) are facilitated by reputable learning institutions and instructors.

1. Machine Learning by Stanford University

Offered by Stanford University, this online Machine Learning course introduces learners to a broad overview of machine learning, statistical pattern recognition, and data mining. The topics covered in this program include supervised learning, unsupervised learning, as well as best practices in Machine Learning. Learners will acquire skills in logical regression, algorithms, and artificial neural networks.

The program’s curriculum comes with case studies as well as practical applications allowing learners to get a good grasp on the application of ML algorithms. This program is perfect for beginners looking to test the waters to explore whether they might be interested in pursuing ML much deeper. This program requires 55 hours to complete.

2. Launching into Machine Learning by Google Cloud

This online Machine Learning program is offered by Google Cloud. It introduces learners to the history of machine learning and explains why neural networks are critical for solving modern data science challenges. Learners will understand why ML is gaining popularity, how to apply it to solve common problems, and how to create scalable and repeatable test data sets.

This program is designed for learners with intermediate grasp of machine learning and requires about 7 hours to complete.

3. Machine Learning Foundations: A Case Study by The University of Washington

This online Machine Learning program is offered by The University of Washington and focuses on delivering hands-on learning experience through a series of practical projects and case-studies. At the end of the program, you should be able to understand how to use apply Machine Learning to predict real estate prices based on the property’s features, search for images, and recommended products among other features.

During the program, you will learn how to identify different ways of applying machine learning, describe the differences between classification, regression, and clustering among others. This program requires 24 hours to complete.

4. Introduction to Machine Learning by Duke University

Offered by Duke University, this online Machine Learning course provides learners with a fundamental understanding of machine learning and machine learning models. You will learn how to apply these models in different scenarios to solve problems.

The program comes with practice exercises necessary for acquiring hands-on experience. These exercises instruct learners how to use TensorFlow as well as other popular open source libraries in the machine learning work. The course is designed for learners with intermediate skill level and requires 14 hours to complete.

5. Machine Learning Specialization by The University of Washington

This course is offered by The Washington University and it is designed for learners who are interested in taking a more intensive path in Machine Learning. The program comes in a series of four courses that include Regression, Classification, Clustering and Retrieval, and A Case Study. Each of these four courses comes with a hands-on project that is designed to help learners master Machine Learning. Upon completing this program, you will be issued with a machine learning certificate that you can show to potential employers as proof of your knowledge. The entire program takes 8 months to complete for learners who are ready to dedicate 6 hours per week to the coursework.

6. Deep Learning by deeplearning.ai

Designed by Andrew Ng, the co-founder of Coursera, the goal of this online machine learning program is to bring Artificial Intelligence to as general audience as possible. This course is ideal for learners seeking to break into Artificial Intelligence.

The program comes with five courses that will help you master the fundamentals of Deep Learning, learn how to build neural networks, and master how to lead successful machine learning projects. You will learn about RNNs, Convolutional networks, LSTM, Dropout, Adam, Xavier/He initialization, BatchNorm, and more. You will work on case studies from autonomous driving, healthcare, music generation, sign language reading, and natural language processing. Besides mastering the theory, you will also learn how to apply it in real life scenarios. You will practice all these ideas in TensorFlow and Python, which you will be introduced to.

7. IBM Data Science

Offered by IBM, the goal of this online course is to popularize data science while granting learners an entry into the world of Machine Learning. This program contains 9 courses designed to provide learners with the latest job-ready skills and techniques covering a huge range of data science topics that include Python, open source tools and libraries, methodologies, SQL, databases, data analysis, data visualization, and machine learning.

You will use real data science tools and real-world data sets for hands-on practice in the IBM cloud.

To wrap it up

Machine Learning and Artificial Intelligence industry is expected to explode over the next couple of years and taking one of the online Machine Learning courses discussed above will put you in an excellent position to land your dream job in this exciting industry. Choosing an online course offered by a reputable institution and facilitated by a knowledgeable instructor is one of the most important steps you can take to show potential employers that you received your training and certification from the best brains in the industry. Happy learning!

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