Since its inception in 1999, Google has been known as the pioneer of search engines and learning. One of its numerous learning programs covers one of the most demanding sub-fields of Information Technology (IT) called “Machine Learning” or ML which allows computers to learn without explicit programming. Technically, Machine Learning has been around for a while but now it has undergone numerous iterations and innovations, now new techniques and technologies have emerged, making it essential to take courses to stay up to date with the latest developments. Google courses on Machine Learning have been a great attraction for ML enthusiasts around the world. Cybertech has compiled more than 5 Google courses that are pretty effective and popular. Get, set, and read:
Course on Introduction to Machine Learning (ML)
For beginners, this Machine Learning (ML) course will meet your needs. What’s more, it’ll only take a very short duration of time to get to grips with the basics of ML, and then you can move on to more advanced courses. This course introduces Machine Learning (ML) concepts. The course duration is pretty short, but it encompasses all the basic. One can easily understand the fundamentals of Machine Learning (ML) including the basics of Machine Learning (ML) and data science, the way ML models work, supervised and unsupervised learning, ML tools and applications, classification, regression, evaluating machine learning models, best practices in ML, its various types, the concepts associated with it as well as traditional problem-solving approaches. These topics will give you everything you need to develop a robust ML skillset.
Read More: Top 10 Trending Technologies Highest Paying IT Jobs In India Should Know About
Crash Course on Machine Learning (ML)
In this fast-paced, practical introduction to Machine Learning (ML), one can get to master some of the most integral features of Machine Learning (ML). Get to learn the best practices from Google experts on key Machine Learning (ML) concepts. One will be able to understand the difference between Machine Learning (ML) and traditional programming, the fundamentals of loss, and how to work out the gradient descent to create an effective model. Best of all, under this course, you will get hands-on on how to represent your data as well as on how to develop a deep neural network for the same. All this will happen with a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.
Course on Machine Learning (ML) for Data Science
It is well known that Machine Learning (ML) is used in Data Sciences which is one of the crucial prerequisites for a data scientist. This course by Google will help you in knowing the fundamentals or basics of ML and the different learning algorithms before you do any data science work. Here, you will get to know how to perform cross-validation to avoid overtraining, the most popular machine-learning algorithms, how to build a recommendation system, and what is regularization and data analysis, and how to train data to obtain valuable insights. This is one of the most lucrative courses extended by Google.
Google’s Course on Data Prep and Feature Engineering
It is with the help of a Google course on data prep and feature engineering that one can identify as well as predict patterns in the data. To get those predictions right, we must construct the data set and transform the data correctly. This course covers these two key steps and helps you become an enlightened feature engineer. In this course, one can recognize the relative impact of data quality and the size of the algorithms generated. Apart from that, you get to formulate informed and realistic expectations for the time to transform the data. One learns how to collect raw data and construct a data set. You get a typical process for data collection which after the much-sought explanation can transform the overall ML workflow. One can also sample and split the data set with considerations for imbalance or improper data. Best of all, this course will teach you how to transform categorical and numerical data.
Hands-on Machine Learning (ML) Course for Analytics
Dedicated to data science and analytics, this course covers anything and everything including computer science and statistics professors. It’ll help you get to grips with the fundamentals of ML and its respective algorithms, including linear regression and supervised and unsupervised learning, among others. One will get to learn how to search for patterns in data and use them to make decisions and predictions about real-world issues, uncover hidden themes in extensive collections of documents, handle missing data, create custom data analysis solutions suitable for different businesses, and make data predictions.
Read More: Why Python Is the Future of web application development
Machine Learning (ML) with Python
Python is the most popular programming language used in Machine Learning (ML). That’s why you should consider learning how to apply it in ML projects, and this Machine Learning (ML) in Python course can help you with that. The course’s main goal is to show you how to use Python, one of the most popular and approachable programming languages, in ML. Get a hands-on introduction to the Python libraries suitable for ML, master the linear, non-linear, and model evaluation regression methods, K-Nearest neighbor, decision trees, logistic regression, support vector machines, model evaluation, the K-Means, hierarchical, and density-based clustering. The Content-based recommender systems and collaborative filtering. Once finished, you’ll understand the difference between the two main types of Machine Learning (ML) methods, the various ML algorithms, and how statistical modeling relates to ML (as well as how to compare them). Moreover, you’ll know how to transform theoretical knowledge into practical skills.
Google’s course on Machine Learning (ML) and Artificial Intelligence
We know some of you are familiar with the basics, so let’s move on to a more advanced Machine Learning and Artificial Intelligence course. Run by Google, one can choose between two paths: The first is for data scientists or Machine Learning (ML) engineers. Consisting of eight broad categories, it lets you get a sneak peek into big data & Machine Learning (ML) fundamentals, and perform foundational data, ML, and AI tasks in Google Cloud. Get advanced Machine Learning with TensorFlow on the Google Cloud Platform. Master MLOps fundamentals as well as ML Pipelines on Google Cloud. Explore Machine Learning (ML) models with Explainable AI and learn to build and deploy Machine Learning (ML) solutions on Vertex AI.
The other path is entirely dedicated to the Contact Center Engineers, offering a shorter AI course. This path has just three elements. One gets to master the customer experiences with Contact Center AI, automate interactions with Contact Center AI, and learn to create Conversational AI Agents with Dialogflow CX.
Post-completion, one will get to implement the latest Machine Learning and AI solutions by exploring training on TensorFlow, Natural Language API, Cloud Vision, and more. These skills are a must-have if you plan to work with Google Cloud.
Course on introduction to TensorFlow for Machine Learning (ML) as well as Deep Learning
TensorFlow is an open-source framework that gives you many opportunities to create advanced machine-learning models. This course is a great starting point if you want to use it to build and apply scalable models to real-world problems. Under this course, one will get to learn the best practices for using TensorFlow, how to build a primary neural network, how to train a NN for a computer vision application, and how to use convolutions to enhance your neural network.
This free TensorFlow course is best for those who have experience in Python and high school-level math. However, prior ML or DL knowledge is not required. Get a hands-on
Course on the classification of Machine Learning (ML)
Classification is the art of making some e-mails land in the inbox and the rest in spam. Hence, it is earmarked by categorical division. It is governed by certain algorithms that power plenty of other applications, and during this free ML course, you’ll learn about most of them using real-world case studies. The course runs through numerous topics, including a laconic introduction to classification, linear classifiers, logistic regression (inc. overfitting & regularization), decision trees, handling missing data, boosting, precision-recalling as well as scaling to huge datasets. This course is lengthy and might take a decent amount of time. However, it is one of the most lucrative and demanding courses extended by Google. So, opt for it only when you don’t have too much on your plate.
Course on Fundamentals of Reinforcement Learning
This course focuses on the subfield of ML called “reinforcement learning”. This technology appears in many real-life applications, including autonomous cars, healthcare, gaming, and marketing. You will get to lay your hands on the essential exploration techniques and the exploration/exploitation tradeoff. Get, set, and comprehend tools for making optimal decisions for lucrative reinforcement learning. One can also get to implement dynamic programming under this very course. All those who are well-versed in probability and prediction, linear algebra and calculus fundamentals, and Python 3.0 can elevate their skills with the help of this course on ML. Even those who know how to implement algorithms from pseudocode can lay their hands on this course extended by Google on Machine Learning (ML).
So, these were some of the most lucrative Google courses on Machine Learning.
