What Is Machine Learning| Google FREE Machine Learning Crash Course

Wants to learn Machine Learning but doesn’t have any knowledge regarding ML and don’t know which resources are best?

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Do you get automatic recommendations of what you have searched on google? Or maybe you get amazing deals on what you have searched on Flipkart/Amazon? and most of you use social media handles so where you have seen that they are giving you some suggestions that people you may know on Facebook, Instagram, etc. That’s all because of Machine Learning.

Google is the perfect example for machine learning as GOOGLE records the number of searches you have made and then suggests you similar searches when you google something in the future. Similarly, AMAZON recommends your products based on your previous searches and so does NETFLIX, based on the TV show or Movies that you watched, you get similar types of suggestions. That is all because of Machine Learning.

Nowadays machine learning is used in every technology we can say that we all are covered with so many technologies. So let’s try to know more about ML and also see how google developers ease to learn ML absolutely FREE. In this blog we will understand every concept of ML in a detailed way, what are the types of ML, which language is best, and how google developers are providing the free courses.

Let’s get started

Here are a couple of related posts you may find helpful, too:

  1. 7 Proven Ways To Learn Programming Quickly And Efficiently
  2. Is learning coding worth it In 2021? 5 ultimate motives to start coding.
  3. 11 Exclusive Online Jobs For Students To Earn Up To $100

What Is Machine Learning

Machine Learning
Machine Learning

Machine Learning is a part of Artificial Intelligence (AI) and computer science which focuses on the usability of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

As the name suggests, is all about M. L. automatically without being explicitly programmed or learning without any direct human intervention. This machine learning process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data we have and what kind of task we are trying to automate.

Basically, there are so many misconceptions regarding AI, ML and deep learning. People thought they were all are same and that is totally right all these are resembling with each other. Let’s understand through an image.

So now it’s totally cleared that ML is a superset of AI and deep learning is a superset of ML where the artificial neural network, the recurrent neural network approaches in similarity.

Let’s see some amazing results of how ML is healing giant companies and it will also encourage you to learn more about it.

  • Netflix saved $1 billion in 2017 because of ML algorithm. It suggests personalized shows TV shows and movies to subscribers.
  • Amazon automates picking and packing items in a ware house logistics settings. Also, Amazon’s average ‘click to ship’ time is less by 225% than earlier.
  • After using ML Algorithm, google has increased its translation speed from 55% to 85%.

Types Of ML

Basically, there are 3 types of ML:

  1. Supervised Machine Learning: Supervised ML works with labeled data. Basically in this algorithm it learns from a training dataset and makes predictions that are compared with the actual output values. If the predictions are not correct, then the algorithm is modified until it is fully satisfied. This learning process continues until the algorithm achieves the required level of performance. Then it can provide the desired output values for any new inputs.
  2. Unsupervised Machine Learning: Unsupervised ML works with unlabaled data. This means that human work is not required to make the dataset machine-readable. In supervised learning the labled data allows to find the exact relation between two data points however in unsupervised learning it doesn’t have labels to work off of, resulting in the creation of hidden structures. That’s why in this algorithm it finds the underlying structure in order to learn more about the data itself.
  3. Reinforcement Machine Learning: In Reinforcement Machine Learning Algorithms learn optimal actions through trial and error. This means that the algorithm decides the next action by learning behaviors that are based on its current state and that will maximize the reward in the future.

Which Programming Languages Is Best For ML

Mainly there are 2 main programming languages i.e. Python and R language which is widely used in ML. Although R language is also good Python is a more popular language that has amazing support because of its variety of libraries and tools. There are two popular machine learning libraries i.e. Scikit and TensorFlow which is a very popular and powerful library. So let’s get a short intro of both the language.

Python

Python logo

Python is a general-purpose, high-level, interpreted language created by Guido van Rossum in 1991. It’s a very simple language to discover and is often promoted as an ideal programming language for beginners. Python is one of the most popular programming languages and it’s used in many domains, Data Science, Machine Learning, Web Development, Game Development, Medicine and Pharmacology, etc. 

To know more about the Python language here are a couple of related posts you may find helpful.

  1. The Best Python Udemy Course That Skyrockets Your Beginners Journey
  2. 6 Best Exciting Python Books For Beginners in 2021.

R

R language is an open-source statistical programming language that is basically designed for statistical computing, data analysis, graphical representation of data, and many more things. It is one of the most popular languages that statisticians use to retrieve, clean, analyzing, visualizing, and represent data.

To know more about the R language here are a couple of related posts you may find helpful.

  1. Associating with the R language through features, platforms.

Resources To Learn Machine Learning

There are numerous resources to learn machine learning I will tell you 4 amazing resources which is totally enough to learn many concepts easily. So let’s start with the first one.

Machine Learning Crash Course

Machine Learning Crash Course

Google developers are providing you with a range of free learning content produced to help you to acknowledge machine learning properly. Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises. So try to learn and apply fundamental machine learning concepts to best practices with Google experts.

Let’s see what are the things they are providing

  • 25 lessons
  • 15 hours of video lecture
  • Lectures from Google researchers
  • 30+ exercises
  • Real-world case studies
  • Interactive visualizations of algorithms in action

Hands-On Machine Learning with Scikit–Learn and TensorFlow

Hands–On Machine Learning with Scikit–Learn and TensorFlow

Nowadays deep learning has boosted the entire field of machine learning. Even the programmers who don’t have any knowledge of this technology can easily use and implement programs.

This book helps you by providing concrete examples, minimum theory, and two production-ready Python frameworks Scikit-learn and TensorFlow. It helps you to understand the concepts and tools for building intelligent systems. It begins with simple linear regression and progressing to deep neural networks. One of the most important facts about the book is at the end of the chapters they have provided exercises to help you apply what you’ve learned.

So first of all what you have to do is just explore the machine learning landscape, particularly neural nets Use scikit-learn to track. As an example machine-learning project end-to-end. Secondly, examine several training models, including support vector machines, decision trees, random forests, and ensemble methods. Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details

This book really helps to cover every concept, especially for beginners. There are numerous platforms to learn ML you can watch any Youtube videos or maybe any course from Udemy but my main motive is to tell you that Google developers are also providing the same course for FREE. Isn’t great to learn from highly skilled developers. Along with this book also nourishes you.

Buy Now.

Furthermore, there is one more website i.e. madewithml.com that also benefits you.

Final Thoughts

So that’s it for the blog I hope you understand the basic concepts of Machine Learning very well. The main motive fo this blog is to provide information regarding Machine Learning Crash Course that is absolutely FREE. So its very good for the beginners who want to start learning ML.

FAQ

1. What is machine learning with example?

Ans: Google is the perfect example for machine learning as GOOGLE records the number of searches you have made and then suggests you similar searches when you google something in the future. Similarly, AMAZON recommends your products based on your previous searches and so does NETFLIX, based on the TV show or Movies that you watched, you get similar types of suggestions. That is all because of Machine Learning.

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