Machine learning is a buzzing topic that is leading the world of IT and STEM rapidly into the mainstream.
A trained machine learning algorithm can detect patterns and anomalies in datasets without being explicitly programmed to do so. By learning to perceive and solve problems on its own, AI enhances and augments human intelligence and creativity in new and unknown ways.
Because of the game-changing impact, this technology will have on every industry, it is a good idea to be informed of what machine learning can do and how it can be deployed.
What Is Machine Learning?
It is a subset of artificial intelligence, the branch of computer science that aims to create software systems that can solve complex problems and perform advanced tasks with superhuman intelligence.
Machine learning is where computers can learn, on their own, to distill patterns from a huge pool of input data. With experience, these computers can do things like weather forecasting, fraud detection, and diagnostics for medical patients.
How Does Machine Learning Work?
In machine learning, the more data the better. A large pool of data to draw from gives computers a higher chance of accurately detecting meaningful patterns to apply to a relevant task.
The three main functions of a machine learning system are:
- Descriptive – cause-and-effect explanations.
- Predictive – forecasts of what will happen in the future.
- Prescriptive – advice on which actions to take or decisions to make.
No matter how natural this sounds, for a computer to do this is no mean feat. Data representations such as images, sensor data, genomic plots, or food recipes must first be prepared to serve as training data.
Programmers then feed this data into the machine learning model of choice, and the computer digests it while training itself to master the task or make accurate predictions.
Programmers can also use libraries or create scripts to alter the hyperparameters. These are the transformation functions the computer model uses internally to make sense of it all.
For example, if a computer is to learn to classify paintings based on style, era, or even creator, it needs an arsenal of operations such as rotating the image, blurring it, and contour tracing.
A job like that, with human programmers labeling the data and specifying the desired result, is called supervised learning. The predictive model learns to map input patterns to correct output results. Regression is used for predicting continuous values, classification for discrete values. Supervised learning is the most common form of machine learning used today.
Some well-known techniques are artificial neural networks (ANN), support vector machines (SVM), and recommender systems. Deep learning (DL) is a form of neural network with many interlinking layers that works much like the human brain and is best for large, complex models.
In contrast, unsupervised learning throws a dollop of data at the computer so it can puzzle for patterns and relationships that are unforeseen and potentially interesting. This is a bit like building with LEGOs without a manual and blindfolded. It takes thousands of iterations before anything useful emerges.
It can also be used to detect anomalies in complex systems which can help improve success rates. Think customer purchasing behavior, industrial equipment sensor monitoring, or crime-related statistics.
In reinforcement learning (RL), the machine is rewarded every time it succeeds in a task. This way, it learns to uncover the embedded rules and act strategically.
Whereas in supervised learning the training data has the answer key included, in RL there is no answer. Instead, the reinforcement agent determines what is best done to achieve the task.
Reinforcement learning is useful for robot skill acquisition such as walking and jumping, autonomous bots to play videogames with, or navigating a city or maze.
Machine learning is predominantly used for:
- Trends and insights: population growth, life expectancy, weather, market data.
- Traffic: optical pattern recognition for self-driving cars, optimizing traffic lights, predicting accidents.
- Healthcare: patient diagnostics for doctor’s review.
- Fraud: Predicting fraudulent credit card transactions.
- E-commerce: Determining viable customer groups.
- Genomics: mutation patterns.
- Pharma: optimizing chemical make-up of drugs.
- Food: predicting the most successful ingredients based on online reviews, sorting fruits and vegetables in factories.
- Communication: automatic copywriting, speech recognition, face recognition, chatbots.
- Stock market: Stock trading bots.
- Social media: Photo tagging and editing.
Where to Start
Creating machines or apps that behave intelligently requires a significant investment. To be at the forefront of software development takes a good deal of activation energy. It requires some math-savviness, but it is possible to learn in a step-by-step fashion for most students of AI.
There is a learning curve to master object-oriented programming languages like Python, C++, Java, R, or SPARK. The key, besides persistence, is to start simple and work with progressively more advanced classes, loops, and functions.
Whether you’re a true beginner or can easily explain how to reverse a string in Python, there are plenty of resources. The most important Python libraries are Pandas, NumPy, TensorFlow, Keras, Scikit-learn, PyTorch, Matplotlib, and NLTK for natural language processing.
And then there is the basic knowledge needed to absorb in the fields of probability, statistics, software architecture, calculus, information theory, and control theory.
To become a fully-fledged data scientist, machine learning engineer, or AI specialist takes years of formal education or workshops. But the benefits of self-directed online learning are manifold:
- Focus on targeted practice makes a project-driven approach with real results.
- Meta-learning: rather like the way a machine learns, you learn to learn better rather than having an institution provide a set path.
- There is no knowledge gap, as you can step in at absolute beginner level.
- You can continuously adapt to the latest trends and needs of the industry for marketable data apps.
- Build a hands-on portfolio for future employers to reference.
There are plenty of entry-level courses that guide you through setting up an ML model, even if that only means swapping input data for your own. And many of these are practically or completely free.
Here are some examples:
- Courses on deep learning.
- FutureLearn course material on machine learning and AI.
- Coursera, one of the most popular (and free) learning platforms.
- Introduction to machine learning on Udacity.
- Udemy’s practically free tutorials on machine learning, deep learning, SciKit Learn, and neural networks with TensorFlow.
- SkillUP’s free basic ML course.
- Geeks for Geeks course in machine learning with Python.
Here is a selection of freely available books to get started:
- Think Stats by Allen B. Downey.
- Deep Learning, an MIT Press publication on deep learning.
- Python Data Science Handbook by Jake VanderPlas.
- Automate the Boring Stuff with Python by Al Sweigart.
- Programming in R for Data Science by Hadley Wickham & Garrett Grolemund.
Meet the Software
The machine learning community is notorious for independently developing custom tools for every occasion. As a result, there are a host of disparate systems for all kinds of functions, from data preparation to programming, analytics, image recognition, infrastructure, data lineage, implementation frameworks, and documentation.
More than that, big names like Amazon, Uber, Google, Facebook, and Apple have all built their ready-to-use platforms for training and testing machine learning models. The goal is to develop marketable apps, services, and new skills for personal digital assistants.
To make quick progress in machine learning, there are numerous downloadable tools:
- Uber’s code-free machine learning toolkit, Ludwig.
- Amazon Web Services (AWS) has a tool named Sagemaker used for building Alexa skills, which can be a high-paid job if successful.
- Google MLKit for mobile Android apps.
- Core ML for Apple iOS.
- Apache software suite includes Hadoop, Mahout, Kafka, Spark, and Druid.
- Ray for advanced cloud development with the Modin and RFlib libraries.
- Snorkel data training framework.
No More Waiting
The reality of a digitally transformed Industry 4.0 with data-driven decision-making is here.
If you have been thinking about upskilling, no matter if it is in robotics, big data, blockchain, the internet of things, mixed reality, digital fabrication, automated injection molding, innovation management, and creativity, or machine learning, now is the best time to make a start.
Make a selection of useful theory and tools, and focus as soon as possible on creating viable projects on your own. But don’t forget to share your work with the community to kickstart your career. Entering forums, coding competitions, and hack-a-thons are fruitful ways to spend your time and develop lasting connections.
First, it is important to understand the complete workflow of Python and build a strong foundation in this. Having a good knowledge of the basic syntax, functions, loops, class, and objects is very necessary to move forward. Unless you know the basic syntax, it’s hard to implement anything. That said, don’t spend too long on this. The goal is to learn the very basics, so you know enough to start working on your own projects in your area(s) of interest.
Get used to with Libraries
There is a vast array of libraries and modules available in Python. Mastering them based on your domain of expertise is necessary. They all have their use cases and functionalities in different domains. For instance, Machine Learning based libraries in Python include Pandas, Numpy, Scikit-learn, Scipy, Matplotlib, etc., Data Analytics based libraries include Bokeh, Matplotlib, PyPlot, etc. Anaconda is an open-source distribution for Python and R for large scale data processing, scientific computing and predictive analytics.
Theory or Mathematics ?
Implementing built-in models with frameworks like Keras, Tensorflow, and Scikit is not unique and special, especially, when we are following an online tutorial and simply changing the data to ours. Coming up and understanding the things behind those architectures is extremely hard. We need a full understanding of probability and statistics, information theory, calculus, etc to just grasp the main ideas.
Start Doing Projects with Further Learning
Start working on the projects along. Define a problem, prepare datasets, evaluate using the algorithms, keep tracks of the result and keep trying to find the optimal result. You will make mistakes, get stuck many times, but gradually you will find ways to come out of your problems. On the journey of finding answers to your queries you will learn new things and here the real learning will start.
Participate in Hackathon, Coding Exams
Keep increasing the difficulty and scope of your projects. If you’re completely comfortable with what you’re building, it means it’s time to try something harder. Participate in coding Competitions, hackathons, and other exams which can enrich your talent and help you to increase your efficiency.
AI components are made up of mathematically proven methods or implementations that are coded to work on data. The use of AI depends upon the type of problems they are designed to work on. At first, you need to go through a scratch and you need to learn the mathematics behind them. Then you can code those algorithms on your own from Scratch and compare the result with the built-in algorithm of scikit-learn.
We hope you like the BEGINNERS GUIDE TO MACHINE LEARNING. So, start practicing and make you career.
Also, checkout: BEGINNERS GUIDE TO SAS PROGRAMMING
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