What is Machine Learning?
This technology falls under the blanket of Artificial Intelligence and serves as a data analysis method used to automate analytical model building.
The idea behind Machine Learning is that systems can learn from data, identify patterns, and make decisions without having to rely (too much) on human interaction. One of the most obvious examples of machine learning is the autonomous vehicle. Automotive manufacturers (such as Tesla) are spending billions of dollars to finally achieve the goal of the self-driving car.
But this isn't an easy challenge. Because we're talking about automobiles, these industries must pass vigorous safety inspections and tests, as well as get beyond current restrictions in state, federal, and global laws. On top of that, they'll need to have the infrastructure in place to make self-driving cars happen.
But before those manufacturers can pull that off, they need to actually achieve the goal of the truly autonomous vehicle. On top of that, those manufacturers must be able to answer the question, "Why do we need autonomous vehicles?" One of the biggest answers the industry has is lowered costs. Autonomous delivery vehicles would drastically cut down the price of shipping products.
To achieve this, machine learning must be applied successfully. Machine learning makes it possible for those manufacturers to achieve higher levels of driver assistance, including how the vehicle perceives the world around it. For a vehicle to become autonomous, it must be able to recognize things like obstacles, traffic patterns, speed limits, routes, and even weather patterns. For that, a vehicle employs numerous sensors around the vehicle, which collect data to be interpreted by the onboard system.
This gets problematic if the machine learning algorithm mistakenly interprets a stop sign for a yield sign, which could lead to catastrophe. This is why machine learning is not only crucial but challenging.
How Does Machine Learning Work?
Machine learning algorithms find patterns in data that generate specific insight for a particular system. Take our autonomous vehicle example from above. The machine learning algorithm must be able to tell the difference between the various signage found on streets. Without the ability to recognize those signs, a car wouldn't know the difference between a stop sign, a speed limit sign, or a sign indicating a turn.
The issue is way more complicated, mainly because machine learning isn't just applied to self-driving cars, but to medical diagnosis, stock trading, energy forecasting, weather predictions, market trends, speech translation, face recognition, sentiment analysis, social media analysis, fraud detection, and product recommendation, among many other things.
Machine learning uses 2 different techniques for the learning process:
- Supervised learning - builds a model based on a known set of data and known responses to the data and trains a model to represent reasonable predictions in response to new data. Examples of supervised learning include classification techniques and regression techniques. You would use this option if you need to train a model to make a prediction.
- Unsupervised learning - finds hidden patterns and structures from datasets without labeled responses. The most popular type of unsupervised learning is clustering. You would use this option if you need to explore data and split it into clusters of information.
Most Popular Programming Languages For Machine Learning
Once you've decided the type of machine learning technique you want to use, the next step is to select the right programming language to build your systems. Keep in mind, though, that not every language is useful for machine learning.
The top 10 most popular programming languages that can be applied to machine learning include:
Conclusion
It's not a matter of if, but when your company will need to add machine learning into your systems. Before you get too far behind this curve, start planning for the eventuality, so you can catch up to the competition. To do that, you're going to need to hire the right people.
What is a neural network?
A neural network is a simplified model of the human brain. This network is a combination of algorithms that aim to identify patterns in data sets through a process that mimics how our brain operates.
What is Naive in the Naive Bayes method?
Instead of the standard Bayes theorem:
P(yi | x1,..., xn) =P(yi)P(x1,..., xn | yi)(P(x1,..., xn)
Naive Bayes uses the naive conditional independence assumption that each xiis independent and simplifies this relationship to:
P(xi | yi, x1, ..., xi-1, xi+1, ...., xn) = P(xi | yi)
What is PCA?
PCA stands for Principal Component Analysis and is used for dimensional reduction.
What is the SVM algorithm?
The Support Vector Machine algorithm is a supervised machine learning model that is capable of performing linear or non-linear classification, regression, and outlier detection.
What is cross-validation?
Cross-validation is a method of splitting data into 3 parts: training, testing, and validation.
What is bias?
Bias informs there's an inconsistency in data that's usually the result of the algorithm being in favor or against a particular view, perspective, or idea. Basically, bias appears because the learning model uses wrong assumptions that are then replicated in how the algorithm itself operates.
What is the difference between classification and regression?
Classification is used to produce discrete results and classify data into specific categories. Regression deals with continuous data.
What are precision and recall?
- Precision answers the question, "Of every item a classifier predicted to be relevant, which ones are actually relevant?"
- Recall answers the question, "Of every item that's relevant, how many were found by the classifier?"
What is collaborative and content-based filtering?
Collaborative filtering is a type of recommendation system that predicts new content by matching an individual's interests. Content-based filtering is focused on the preferences of a user.
We are looking for an expert in machine learning to help extract value from our data. You will be in charge of this process from collecting, cleaning, and preprocessing data to training models and deploying them to production. The ideal candidate will be passionate about artificial intelligence and stay up to date with the latest developments in the field.
Responsibilities
- Understand business objectives and develop models that help to achieve them, along with metrics to track their progress
- Managing all resources such as hardware, data, and personnel to meet deadlines
- Analyze ML algorithms to solve a given problem and rank them by their success probability
- Explore and visualize data and identify differences in data distribution that could affect performance when deploying the model
- Verifying data quality, and/or ensuring it via data cleaning
- Supervising the data acquisition process if more data is needed
- Finding available datasets online that could be used for training
- Defining validation strategies
- Defining the preprocessing or feature engineering to be done on a given dataset
- Defining data augmentation pipelines
- Training models and tuning their hyperparameters
- Analyzing the errors of the model and designing strategies to overcome them
- Deploying models to production
Skills and Qualifications
- Proficiency with a deep learning framework such as TensorFlow
- Proficiency with Python, Java, JavaScript, and the basic libraries used for machine learning
- Expertise in visualizing and manipulating large datasets
- Proficiency with OpenCV
- Familiarity with Linux
- Ability to select hardware to run an ML model with the required latency
- Fundamental understanding of version control systems (such as Git)
- Solid problem-solving skills
- Excellent written and verbal communication
- Good organizational skills
- Ability to work as part of a team
- Attention to detail
- Understanding the nature of asynchronous programming and its quirks and workarounds
- A positive attitude
