## Machine Learning Models Explained

### K Nearest Neighbors (KNN)

This model can be used for either classification or regression. The name “K Nearest Neighbors” is not intended to be confusing. The model first plots out all of the data. The “K” part of the title refers to the number of closest neighboring data points that the model looks at to determine what the prediction value should be. You, as the future data scientist, get to choose K and you can play around with the values to see which one gives the best predictions.

All of the data points that are in the K=__ circle get a “vote” on what the target variable value should be for this new data point. Whichever value receives the most votes is the value that KNN predicts for the new data point. In above, our example, the nearest neighbors are class 1, while 1 of the neighbors is class 2. Thus, the model would predict class 1 for this data point. If the model is trying to predict a numerical value instead of a category, then all of the “votes” are numerical values that are averaged to get a prediction.

### Support Vector Machines (SVMs)

Support Vector Machines work by establishing a boundary between data points, where the majority of one class falls on one side of the boundary (a.k.a. line in the 2D case) and the majority of the other class falls on the other side.

The way it works is the machine seeks to find the boundary with the largest margin. The margin is defined as the distance between the nearest point of each class and the boundary. New data points are then plotted and put into a class depending on which side of the boundary they fall on.

## Unsupervised Machine Learning Models

Now we are venturing into unsupervised learning (a.k.a. the deep end, pun intended). As a reminder, this means that our data set is not labeled, so we do not know the outcomes of our observations.

### K Means Clustering

When you use K means clustering, you have to start by assuming there are K clusters in your dataset. Since you do not know how many groups there really are in your data, you have to try out different K values and use visualizations and metrics to see which value of K makes sense. K means works best with clusters that are circular and of similar size.

The K Means algorithm first chooses the best K data points to form the center of each of the K clusters. Then, it repeats the following two steps for every point:

1. Assign a data point to the nearest cluster center
2. Create a new center by taking the mean of all of the data points that are now in this cluster

### DBSCAN Clustering

The DBSCAN clustering model differs from K means in that it does not require you to input a value for K, and it also can find clusters of any shape. Instead of specifying the number of clusters, you input the minimum number of data points you want in a cluster and the radius around a data point to search for a cluster. DBSCAN will find the clusters for you! Then you can change the values used to make the model until you get clusters that make sense for your dataset.

Additionally, the DBSCAN model classifies “noise” points for you (i.e. points that are far away from all other observations). This model works better than K means when data points are very close together.

### Neural Networks

Neural networks are the coolest and most mysterious models. They are called neural networks because they are modeled after how the neurons in our brains work. These models work to find patterns in the dataset; sometimes they find patterns that humans might never recognize.

Neural networks work well with complex data like images and audio. They are behind lots of software functionality that we see all the time these days, from facial recognition (stop being creepy, Facebook) to text classification. Neural networks can be used with data that is labeled (i.e. supervised learning applications) or data that is unlabeled (unsupervised learning) as well.

### Conclusion

Hopefully, this article has not only increased your understanding of these models but also made you realize how cool and useful they are. When we let the computer do the work/learning, we get to sit back and see what patterns it finds.

### KnowledgeHut

Author

KnowledgeHut is an outcome-focused global ed-tech company. We help organizations and professionals unlock excellence through skills development. We offer training solutions under the people and process, data science, full-stack development, cybersecurity, future technologies and digital transformation verticals.
Website : https://www.knowledgehut.com

## Trending Specialization Courses in Data Science

Data scientists, today are earning more than the average IT employees. A study estimates a need for 190,000 data scientists in the US alone by 2021. In India, this number is estimated to grow eightfold, reaching $16 billion by 2025 in the Big Data analytics sector. With such a growing demand for data scientists, the industry is developing a niche market of specialists within its fields. Companies of all sizes, right from large corporations to start-ups are realizing the potential of data science and increasingly hiring data scientists. This means that most data scientists are coupled with a team, which is staffed with individuals with similar skills. While you cannot remain a domain expert in everything related to data, one can be the best at the specific skill or specialization that they were hired for. Not only this specialization within data science will also entail you with more skills in paper and practice, compared to other prospects during your next interview. Trending Specialization Courses in Data ScienceOne of the biggest myths about data science is that one needs a degree or Ph.D. in Data Science to get a good job. This is not always necessary. In reality employers value job experience more than education. Even if one is from a non-technical background, they can pursue a career in data science with basic knowledge about its tools such as SAS/R, Python coding, SQL database, Hadoop, and a passion towards data. Let’s explore some of the trending specializations that companies are currently looking out for while hiring data scientists: Data Science with RA powerful language commonly used for data analysis and statistical computing; R is one of the best picks for beginners as it does not require any prior coding experience. It consists of packages like SparkR, ggplot2, dplyr, tidyr, readr, etc., which have made data manipulation, visualization, and computation faster. Additionally, it also has provisions to implement machine learning algorithms. Data Science with Python Python, originally a general-purpose language, is open-source code and a common language for data science. This language has a dedicated library for data analysis and predictive modelling, making it a highly demanded data science tool. On a personal level, learning data science with python can also help you produce web-based analytics products. Big Data analytics Big data is the most trending of the listed specializations and requires a certain level of experience. It examines large amounts of data and extracts hidden patterns, correlations, and several other insights. Companies world-over are using it to get instant inputs and business results. According to IDC, Big Data and Business Analytics Solutions will reach a whopping$189.1 billion this year. Additionally, big data is a huge umbrella term that uses several types of technologies to get the most value out of the data collected. Some of them include machine learning, natural language processing, predictive analysis, text mining, SAS®, Hadoop, and many more.  Other specializationsSome knowledge of other fields is also required for data scientists to showcase their expertise in the field. Being in the know-how of tools and technologies related to machine learning, artificial intelligence, the Internet of Things (IoT), blockchain and several other unexplored fields is vital for data enthusiasts to emerge as leaders in their niche fields.  Building a career in Data ScienceWhether you are a data aspirant from a non-technical background, a fresher, or an experienced data scientist – staying industry-relevant is important to get ahead. The industry is growing at a massive rate and is expected to have 2.7 million open job roles by the end of 2020. Industry experts point out that one of the biggest causes for tech companies to lay off employees is not automation, but the growing gap between evolving technologies and the lack of niche manpower to work on it. To meet these high standards keeping up with your data game is crucial.
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## 10 Mandatory Skills to Become a Data Scientist

The data science industry is growing at an alarming pace, generating a revenue of $3.03 billion in India alone. Even a 10% increase in data accessibility is said to result in over$65 million additional net income for the typical Fortune 1000 companies worldwide. The data scientist has been ranked the best job in the US for the 4th year in a row, with an average salary of \$108,000; and the demand for more data scientists only seems to be growing. Who is a Data scientist?A data scientist is precisely someone who collects all the massive data that is available online, organizes the unstructured formats into bite-sized readable content, and analyses this to extract vital information about customer trends, thinking patterns, and behavior. This information is then used to create business goals or agendas that are aligned to the end-user/customer’s needs.  This outlines that a data scientist is someone with sound technical knowledge, interpersonal skills, strong business acumen, and most importantly, a passionate data enthusiast. Listed below are some mandatory skills that an aspiring data scientist must develop. 10 Mandatory Skills to Become a Data Scientist Technical Skills  1. Programming, Packages, and Software Since the first task of data scientists is to gather all the information or raw data and transform this into actionable insights, they need to have advanced knowledge in coding and statistical data processing. Some of the common programming languages used by data scientists are Python, R, SQL, NoSQL, Java, Scala, Hadoop, and many more.  2. Machine Learning and Deep LearningMachine Learning and Deep Learning are subsets of Artificial Intelligence (AI). Data science largely overlaps the growing field of AI, as data scientists use their potentials to clean, prepare, and extract data to run several AI applications. While machine learning enables supervised, unsupervised, and reinforced learning, deep learning helps in making datasets study and learn from existing information. A good example is the facial recognition feature in photos, doodling games like quick draw, and more. 3. Big Data Data Scientists are the best bridge between the vast pool of big data and emerging businesses. Big data analytics uses Hadoop or Spark to gather, distribute, and process various datasets. This is an important business trend that companies are using to predict customer tendencies and create a competitive edge.  4. NLP, Cloud Computing and othersNatural Language Processing (NLP), a branch of AI that uses the language used by human beings, processes it, and learns to respond accordingly. Several apps and voice-assisted devices like Alexa and Siri are already using this remarkable feature. As data scientists use large amounts of data stored on clouds, familiarity with cloud computing software like AWS, Azure, and Google cloud will be beneficial. Learning frameworks like DevOps can help data scientists streamline their work, along with several other such upcoming technologies. 5. Database management and visualizationWhile all the above skills deal with gathering and reading data, database management is related to data manipulation. In database management, the data clusters are edited, indexed, and manipulated to yield desirable outcomes or information. The next step to this transformed raw data is to present it in a visually comprehensible manner, which is nothing but data visualization. It includes graphical representation and other elements to make the data easily understandable even by a layman.  Non-technical Skills 6. Communication skills As explained above, once the raw data is processed, it needs to be presented understandably. This does not limit the job to just visually coherent information but also the ability to communicate the insights of these visual representations. The data scientist should be excellent at communicating the results to the marketing team, sales team, business leaders, and other stakeholders. 7. Team player This is related to the previous point. Along with effective communication skills, data scientists need to be good team players, accommodating feedback, and other inputs from business teams. They should also be able to efficiently communicate their requirements to the data engineers, data analysts, and other members of the team. Coordination with their team members can yield faster results and optimal outputs. 8. Business acumenSince the job of the data scientist ultimately boils down to improving/growing the business, they need to be able to think from a business perspective while outlining their data structures. They should have in-depth knowledge of the industry of their business, the existing business problems of their company, and forecasting potential business problems and their solutions. 9. Critical thinking Apart from finding insights, data scientists need to align these results with the business. They need to be able to frame appropriate questions and steps/solutions to solve business problems. This objective ability to analyse data and addressing the problem from multiple angles is crucial in a data scientist. 10. Intellectual curiosityAccording to Harvard Business Review, data scientists spend 80% of their time discovering and preparing data. For this, they must always be a step ahead and catch up with the latest trends. Constant upskilling and a curiosity to learn new ways to solve existing problems quicker can get data scientists a long way in their careers. Taking data-driven decisionsData science is indisputably one of the leading industries today. Whether you are from a technical field or a non-technical background, there are several ways to build up the skill to become a data scientist. From online courses to boot camps, one should always be a step ahead in this competitive field to build up their data work portfolios. Additionally, reading up on the latest technologies and regular experimentation with new trends is the way forward for aspirants.
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