FREE Machine Learning Crash Course – Learn With Google AI

FREE Machine Learning Crash Course – Learn With Google AI. Google has introduced a new “Learn with Google AI”, course, which will bring machine learning skills and concepts to all users. The course is free and available to all users.  Google says the set of educational resources in this course have been developed by machine learning experts at the company.

Google Machine Learning Course

Google says the idea of this machine learning course is to encourage people to learn about machine learning concepts, develop skills in the area, and apply artificial intelligence to real-world problems.

The new Machine Learning Crash Course will give a quick introduction to practical ML concepts using high-level TensorFlow (TF) APIs. TensorFlow is Google’s open source library for machine learning tools and can be accessed by anyone to build AI, ML frameworks suited to their tasks, problems.

Machine Learning For Artificial Intelligence

Machine Learning allows computers to learn, understand and recognize the data, without being explicitly programmed to do so. Machine Learning is the building block for artificial intelligence and is what powers self-driving cars, image recognition, etc.

You Might Be Interested In – Python Tutorial For Beginners

Users will be able to learn about key ML algorithms and frameworks from Google’s new course It will also include videos from ML experts at Google, interactive visualizations illustrating ML concepts and coding exercises using TensorFlow APIs, says the company.

Learn Machine Learning With Google AI

“We believe it’s important that the development of AI reflects as diverse a range of human perspectives and needs as possible. So, Google AI is making it easier for everyone to learn ML by providing a huge range of free, in-depth educational content.

This is for everyone — from deep ML experts looking for advanced developer tutorials and materials, to curious people who are ready to try to learn what ML is in the first place,” Zuri Kemp, Program Manager for Google’s machine learning education said in a press statement.

You Might Be Interested In – What is Python used for?

How to enroll for Google Machine Learning course? What are the requirements?

Those who are interested can access the Google Machine Learning course at developers.google.com/machine-learning/crash-course/.  While the course is free for all, there are some pre-requisites before taking up an understanding of how to code for machine learning.

Related Article: Advantages of Python

Google’s course page says the user must have a mastery of introductory level of algebra, and this includes variables and coefficients, linear equations, graphs of functions, and histograms. Also, familiarity with advanced math concepts such as logarithms and derivatives will be helpful, though it is not compulsory.

Additionally, a knowledge of programming basics and some experience coding in Python are also required. Google says those enrolling should comfortable reading and writing Python code since the exercises in the course are coded in the same programming language. The course will have lectures from Google’s AI experts, along with exercises and access to some material as well.

The Google course on Machine Learning was originally built for the company’s own employees and so far over 18,000 employees in the company have enrolled in this MLCC course. Google says that seeing the program’s success in-house is what inspired them to make it available to everyone. Source: Indian Express.

Enroll Now In Google Machine Learning Course.!

Python For Machine Learning Tutorial For Beginners

Python For Machine Learning Tutorial For Beginners. Machine learning is the new buzz word all over the world across the industries. Everyone trying to learn machine learning models, classifiers, neural networks and other machine learning technologies. If you are willing to learn machine learning, but you have a  doubt of how do you get started? Here Coding compiler gives answers to your questions. Let’s dive into this article, happy machine learning.

Python For Machine Learning

In this article we will talk about the important features of Python and the reasons it applies to machine learning, introducing some important machine learning packages, and other places where you can get more detailed resources.

Why Python for Machine Learning?

Python is well suited for machine learning. First, it is simple. If you are completely unfamiliar with Python but have some other programming experience (C or other programming languages), getting started is fast.

Second, Python’s community is strong. This makes Python documentation not only tractable but also easy to read. You can also find detailed answers to many questions on StackOverflow.

And again, the by-product of a strong community is the vast library of useful libraries (native to Python and third-party software) that basically solve all your problems (including machine learning).

Related Article: Advantages of Python

Is Python Slow?

Python is slow. It’s not the fastest language to implement, and having so many useful abstractions comes at a price.

But this is a problem that can be solved: Libraries can outsource heavy computations to other more efficient (but harder) languages such as C and C ++. Such as NumPy this numerical computing library is written in C, running fast. In practice, almost all libraries use NumPy to do the heavy lifting. If you see Numpy, you should think of it soon.

So you can make the program run faster with its low-level language to achieve the speed of operation compared. You do not need to worry about the speed of the program.

Machine Learning Tutorial For Beginners

Worth knowing python libraries for machine learning.

  1. Scikit-learn
  2. NLTK
  3. Theano
  4. TensorFlow
  5. Keras
  6. PyTorch

1) Scikit-learn

Have you just started to learn machine learning? If you need a library that covers all the features of feature engineering, model training, and model testing, scikit-learn is your best bet!

This great free software provides all the tools you need for machine learning and data mining. It is the current standard library for machine learning in Python. This library is recommended for use with any sophisticated machine learning algorithm.

This library supports both categorization and regression, implementing all of the classic algorithms (support vector machines, random forests, naive Bayes, etc.). The library design makes migrating algorithms so easy that experimenting with different algorithms is easy. These classic algorithms are highly usable and can be used in a large number of different situations.

But this is not the full functionality of Scikit-learn, it can also be used to do dimensionality reduction, clustering, whatever you can think of. Because it builds on Numpy and Scipy (all numerical calculations are done in C), it runs extremely fast.

These examples can tell you the function of this library, if you want to learn how to use it, you can read the tutorial.

2) NLTK

NLTK is not a machine learning library, but it is a library necessary for natural language processing (NLP). In addition to the features used for word processing, such as clustering, word segmentation, stemming, marking, parsing, etc., it also contains a large number of datasets and other lexical resources that can be used for model training.

3) Theano

Theano is widely used in industry and academia and is the originator of all deep learning architecture. Theano is Python, in conjunction with Numpy. You can use it to build neural networks with multidimensional arrays. Theano handles all the math and you do not need to know the underlying math formula implementation.

Theo already provided support for GPU computing as early as supporting the use of GPU for computing not as popular as it is today. This library is currently very mature and can support many different types of operations. This allows Theano to win when compared to other libraries.

Currently, the biggest problem with Theano is that APIs are not very useful and difficult to use for newbies. However, packages such as  Keras , Blocks, and  Lasagne that already have a solution to this problem can simplify the use of Theano.

TensorFlow Machine Learning

Google Brain Team created TensorFlow for internal use and turned it open in 2015. Designed to replace their existing DistBelief, a closed machine learning framework, it is said that the architecture is too dependent on Google’s overall architecture and not flexible enough to be very inconvenient when sharing code.

So there is TensorFlow. Google learned from previous mistakes. Many consider TensorFlow an improved version of Theano, which provides a more flexible and easy-to-use API. Can be used in scientific research and industry, while supporting the use of a large number of GPU model training. TensorFlow does not support Theano’s much more operations, but its computational visualization is better than Theano’s.

TensorFlow is currently very popular. If you just heard one of the names mentioned in this article today, it is most likely this. Every day, new posts to TensorFlow’s blog posts or academic articles are posted. This popularity provides a large number of users and tutorials, new people are very easy to use.

Keras

Keras is a library that provides higher-level neural network APIs that can be based on Theano or TensorFlow. It has the powerful features of both libraries while greatly simplifying ease of use. It puts the user experience in the forefront, providing simple APIs and useful error messages.

Keras’s design is module-based, which allows you to freely mix different models (neural layers, cost functions, etc.) and the model is very scalable because you only have to simply associate new modules with existing ones It can be up.

If you start with deep learning, take a look at examples  and  documentation  and have a look at what you can do with it. If you want to learn to use it, can from this tutorial begins.

Two similar libraries are Lasagne  and  Blocks , but they only support Theano. If you’ve tried Keras but you do not like it you can try these other libraries, maybe they’re better for you.

PyTorch

There is also a famous deep learning architecture Torch , it is implemented with Lua. Facebook implemented Torch in Python, called PyTorch, and made it open source. With this library you can use the lower level library Torch uses, but you can use Python instead of Lua.

PyTorch is good at troubleshooting, because Theano and TensorFlow use symbolic computation and PyTorch does not. Using symbolic calculations means that an operation (x + y) will not be executed when a single line of code is interpreted, until then it must be compiled (interpreted as CUDA or C).

This makes it hard to troubleshoot problems with Theano and TensorFlow because it’s hard to relate the error to the current code. This has its advantages, but it is not easy to find the wrong one. If you want to start learning PyTorch, official documents for beginners will also contain difficult content.

The first step in machine learning?

Now, You know about so many machine learning packages, which one should I use? How can I compare them? Where do I start?

You can try our Ape Advice ™ platform for beginners and do not bother with the details. If you have absolutely no contact with machine learning, start with scikit-learn. You can see how labeling, training and testing work, and how a model is built.

If you want to try out in-depth learning, starting with Keras, this is the easiest framework to recognize. You can try it first to find the feeling. After you get a bit of experience, you can begin to think about what you need most: speed, different APIs, or whatever, and you’re better off later.

There are currently numerous articles comparing Theano, Torch and TensorFlow. No one can say which is the best. What you have to keep in mind is that all packages support a lot of things and are constantly improving, making it harder and harder to compare them to each other. Six months ago the standard may be outdated, a year ago’s assessment said the framework X does not have the Y function may not be effective.

If you want to know more about the concepts of machine learning, check out this Machine Learning Getting Started Guide.

What is Machine Learning? – A Complete Beginners Guide on ML

What is Machine Learning? – A complete beginner guide on Machine Learning, Machine learning implementation, machine learning algorithms, machine learning programming languages and its insights. Read this article from Coding compiler for a complete understanding of machine learning and its benefits.

What is Machine Learning?

Machine learning is a set of methods by which computers make decisions autonomously. Using certain techniques, computers make decisions by considering or detecting patterns in past records and then predicting future occurrences.

Different types of predictions are possible, such as about weather conditions and house prices. Apart from predictions, machines have learned how to recognize faces in photographs and even filter out email spam. Google, Yahoo, etc, use machine learning to detect spam emails.

Machine Learning Insights

Machine learning is widely implemented across all types of industries. If programming is used to achieve automation, then we can say that machine learning is used to automate the process of automation.

In traditional programming, we use data and programs on computers to produce the output, whereas in machine learning, data and output is run on the computer to produce a program. We can compare machine learning with farming or gardening, where seeds –> algorithms, nutrients –> data, and the gardener and plants –> programs.

What can Machine Learning be used for?

We can say machine learning enables computers to learn to perform tasks even though they have not been explicitly programmed to do so. Machine learning systems crawl through the data to find the patterns and when found, adjust the program’s actions accordingly.

With the help of pattern recognition and computational learning theory, one can study and develop algorithms (which can be built by learning from the sets of available data), on the basis of which the computer takes decisions.

These algorithms are driven by building a model from sample records. These models are used in developing decision trees, through which the system takes all the decisions. Machine learning programs are also structured in such a way that when exposed to new data, they learn and improve over time.

Related Article: Robotic Process Automation

Machine Learning Implementation

Before we understand how machine learning is implemented in real life, let’s look at how machines are taught. The process of teaching machines is divided into three steps.

  1. Data input: Text files, spreadsheets or SQL databases are fed as input to machines. This is called the training data for a machine.
  2. Data abstraction: Data is structured using algorithms to represent it in simpler and more logical formats. Elementary learning is performed in this phase.
  3.  Generalisation: An abstract of the data is used as input to develop the insights. Practical application happens at this stage.

The success of the machine depends on two things:

  • How well the generalization of abstraction data happens.
  • The accuracy of machines when translating their learning into practical use for predicting the future set of actions.
  • In this process, every stage helps to construct a better version of the machine.

What Is Machine Learning Used For?

Now let’s look at how we utilize the machine in real life. Before letting a machine perform any unsupervised task, the five steps listed below need to be followed.

  • Collecting data
  • Preparing the data
  • Training the model
  • Model evaluation
  • Performance improvement

Related Article: Benefits of Robotic Process Automation

Collecting data: Data plays a vital role in the machine learning process. It can be from various sources and formats like Excel, Access, text files, etc. The higher the quality and quantity of the data, the better the machine learns. This is the base for future learning.

Preparing the data: After collecting data, its quality must be checked and unnecessary noise and disturbances that are not of interest should be eliminated from the data. We need to take steps to fix issues such as missing data and the treatment of outliers.

Training the model: The appropriate algorithm is selected in this step and the data is represented in the form of a model. The cleaned data is divided into training data and testing data. The training data is used to develop the data model, while the testing data is used as a reference to ensure that the model has been trained well to produce accurate results.

Model evaluation: In this step, the accuracy and precision of the chosen algorithm is ensured based on the results obtained using the test data. This step is used to evaluate the choice of the algorithm.

Performance improvement: If the results are not satisfactory, then a different model can be chosen to implement the same or more variables are introduced to increase efficiency.

Related Article: Reality of Robotic Process Automation

Machine Learning Algorithms

Machine learning algorithms have been classified into three major categories.

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • K-means clustering algorithm
  • Apriori algorithm
  • Markov decision process

Types of Machine Learning Algorithms

Supervised learning: Supervised learning is the most commonly used. In this type of learning, algorithms produce a function which predicts the future outcome based on the input given (historical data).

The name itself suggests that it generates output in a supervised fashion. So these predictive models are given instructions on what needs to be learnt and how it is to be learnt. Until the model achieves some acceptable level of efficiency or accuracy, it iterates over the training data.

To illustrate this method, we can use the algorithm for sorting apples and mangoes from a basket full of fruits. Here we know how we can identify the fruits based on their colour, shape, size, etc.

Some of the algorithms we can use here are the neural network, nearest neighbour, Naïve Bayes, decision trees and regression.

Related Article: RPA Use Cases

Unsupervised learning: The objective of unsupervised learning algorithms is to represent the hidden structure of the data set in order to learn more about the data. Here, we only have input data with no corresponding output variables.

Unsupervised learning algorithms develop the descriptive models, which approach the problems irrespective of the knowledge of the results. So it is left to the system to find out the pattern in the available inputs, in order to discover and predict the output. From many possible hypotheses, the optimal one is used to find the output.

Sorting apples and mangoes from a basket full of fruits can be done using unsupervised learning too. But this time the machine is not aware about the differentiating features of the fruits such as colour, shape, size, etc. We need to find similar features of the fruits and sort them accordingly.

Some of the algorithms we can use here are the K-means clustering algorithm and hierarchical clustering.

Reinforcement learning: In this learning method, ideas and experiences supplement each other and are also linked with each other. Here, the machine trains itself based on the experiences it has had and applies that knowledge to solving problems.

This saves a lot of time, as very little human interaction is required in this type of learning. It is also called the trial-error or association analysis technique, whereby the machine learns from its past experiences and applies its best knowledge to make decisions.

For example, a doctor with many years of experience links a patient’s symptoms to the illness based on that experience. So whenever a new patient comes, he uses his experience to diagnose the illness of the patient.

Some of the algorithms we can use here are the Apriori algorithm and the Markov decision process.

Machine Learning Applications

Machine learning has ample applications in practically every domain. Some major domains in which it plays a vital role.

Machine Learning In Banking And Financial Services

Banking and financial services: Machine learning plays an important role in identifying customers for credit card offers. It also evaluates the risks involved with those offers. And it can even predict which customers are most likely to be defaulters in repaying loans or credit card bills.

Machine Learning In Healthcare

Healthcare: Machine learning is used to diagnose fatal illnesses from the symptoms of patients, by comparing them with the history of patients with a similar medical history.

Machine Learning In Retail

Retail: Machine learning helps to spot the products that sell. It can differentiate between the fast selling products and the rest. That analysis helps retailers to increase or decrease the stocks of their products. It can also be used to recognize which product combinations can work wonders. Amazon, Flipkart, and Walmart all use machine learning to generate more business.

Machine Learning In Publishing And Social Media

Publishing and social media: Some publishing firms use machine learning to address the queries and retrieve documents for their users based on their requirements and preferences. Machine learning is also used to narrow down the search results and news feeds. Google and Facebook are the best examples of companies that use machine learning. Facebook also uses machine learning to suggest friends.

Machine Learning In Games

Games: Machine learning helps to formulate strategies for a game that requires the internal decision tree style of thinking and effective situational awareness. For example, we can build intelligence bots that learn as they play computer games.

Machine Learning For Face Detection

Face detection/recognition: The most common example of face detection is this feature being widely available in smartphone cameras. Facial recognition has even evolved to the extent that the camera can figure out when to click – for instance, only when there is a smile on the face being photographed. Face recognition is used in Facebook to automatically tag people in photos. It’s machine learning that has taught systems to detect a particular individual from a group photo.

Machine Learning In Genetics

Genetics: Machine learning helps to identify the genes associated with any particular disease.

Machine Learning Tools

There are enough open source tools or frameworks available to implement machine learning on a system. One can choose any, based on personal preferences for a specific language or environment.

  1. Shogun
  2. Weka
  3. Apache Mahout
  4. TensorFlow
  5. CUDA-Convnet
  6. H2O

Shogun Machine Learning Tool

Shogun: Shogun is one of the oldest machine learning libraries available in the market. It provides a wide range of efficient machine learning processes. It supports many languages such as Python, Octave, R, Java/ Scala, Lua, C#, Ruby, etc, and platforms such as Linux/UNIX, MacOS and Windows. It is easy to use, and is quite fast at compilation and execution.

What is Weka Machine Learning Tool?

Weka: Weka is data mining software that has a collection of machine learning algorithms to mine the data. These algorithms can be applied directly to the data or called from the Java code.

Weka is a collection of tools for:

  • Regression
  • Clustering
  • Association rules
  • Data pre-processing
  • Classification
  • Visualisation

Apache Mahout Machine Learning Tool

Apache Mahout: Apache Mahout is a free and open source project. It is used to build an environment to quickly create scalable machine learning algorithms for fields such as collaborative filtering, clustering and classification. It also supports Java libraries and Java collections for various kinds of mathematical operations.

What is TensorFlow Machine Learning Tool?

TensorFlow: TensorFlow performs numerical computations using data flow graphs. It performs optimisations very well. It supports Python or C++, is highly flexible and portable, and also has diverse language options.

CUDA – Convnet Machine Learning Tool

CUDA-Convnet: CUDA-Convnet is a machine learning library widely used for neural network applications. It has been developed in C++ and can even be used by those who prefer Python over C++. The resulting neural nets obtained as output from this library can be saved as Python-pickled objects, and those objects can be accessed from Python.

H2O Machine Learning Tool

H2O: This is an open source machine learning as well as deep learning framework. It is developed using Java, Python and R, and it is used to control training due to its powerful graphics interface. H2O’s algorithms are mainly used for business processes like fraud or trend predictions.

What is the best programming language for machine learning?

The best programming languages for machine learning are:

  • MATLAB
  • R
  • Python
  • Java
  • C++
  • C
  • JavaScript
  • Scala
  • Julia

But for a non-programmer, Weka is highly recommended when working with machine learning algorithms.

Advantages Of Machine learning

The advantages of machine learning are:

  • Machine learning helps the system to decode based on the training data provided in the dynamic or undermined state.
  • It can handle multi-dimensional, multi-variety data, and can extract implicit relationships within large data sets in a dynamic, complex and chaotic environment.
  • It saves a lot of time by tweaking, adding, or dropping different aspects of an algorithm to better structure the data.
  • It also uses continuous quality improvement for any large or complex process.
  • There are multiple iterations that are done to deliver the highest level of accuracy in the final model.
  • Machine learning allows easy application and comfortable adjustment of parameters to improve classification performance.

Challenges Of Machine Learning

The challenges of machine learning are as follows:

A common challenge is the collection of relevant data. Once the data is available, it has to be pre-processed depending on the requirements of the specific algorithm used, which has a serious effect on the final results.

Difficulties Of Machine Learning

Machine learning techniques are such that it is difficult to optimize non-differentiable, discontinuous loss functions. Discontinuous loss functions are important in cases such as sparse representations. Non-differentiable loss functions are approximated by smooth loss functions without much loss in sparsity.

Disadvantages Of Machine Learning

It is not guaranteed that machine learning algorithms will always work in every possible case. It requires some awareness about the problem and also some experience in choosing the right machine learning algorithm.
Collection of such large amounts of data can sometimes be an unmanageable and unwieldy task. Source

Machine Learning Questions

Q) What is Machine Learning Language?

A) Machine Learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning language focuses on the development of computer programs that can access data and use it learn for themselves.

Q) What is machine learning used for?

A) Machine Learning can be used for Collecting data, Preparing the data, Training the model, Model evaluation, and Performance improvement, etc.

Q) What machine learning can do?  (or) What can machine learning be used for?

A) Machine Learning can do a variety of tasks like data modeling, serving content, predicting problems and many other things.

Q) What is machine learning in big data?

A) Machine Learning and Big Data as such have no direct relation. Although one can say that Big Data Techniques can be used in Machine Learning.

While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development.

Q) What is machine learning in artificial intelligence?

A) Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed.

Q) What is Microsoft Azure machine learning?

A) Azure Machine Learning Studio is a GUI-based integrated development environment for constructing and operationalizing Machine Learning workflow on Azure. Azure Machine Learning is built on top of the machine learning capabilities of several Microsoft products and services.