TOP 10 MACHINE LEARNING ALGORITHMS FOR BEGINNERS

 It’s never too late to start off with machine learning basics

There’s no denying that the area of machine learning or artificial intelligence has grown in prominence in recent years. Machine learning is very effective for making predictions or calculating suggestions based on vast quantities of data, which is the trendiest topic in the tech sector right now. In this article, we will discuss the top 10 ML algorithms for newbies.

What are machine learning algorithms?

Any other algorithm in computer programming can be connected to a machine learning method. An ML algorithm is a data-driven process for developing a production-ready ML model. If you consider ML as a train that will get you to your destination, then ML algorithms are the engines that will get you there. The sort of ML algorithm that works best is determined by the business challenge at hand, the dataset’s structure, and the available resources.

Types of Machine Learning Algorithms

• Supervised ML Algorithms
• Unsupervised ML Algorithms
• Reinforcement ML Algorithms

Best ML Algorithms

1. Decision Tree

The decision tree is a decision-making aid that employs a tree-like graph or model of options, as well as their potential consequences, such as chance-event results, resource costs, and execution.

2. Principal Component Analysis

When data contains several dimensions, dimension reduction methods are among the most essential algorithms in ML.

3. Deep Learning Algorithms.

Deep learning algorithms are based on the neurological system of a person and are usually built on neural networks that have a lot of computing power. To execute certain tasks, all of these algorithms employ various forms of neural networks.

4. Naive Bayes Classifier

There are two sorts of probability in the model:

• Probability of each class
• Conditional Probability

5. Ordinary Least Square Regression

The least-square is the technique for doing linear regression in statistics. The traditional least-squares approach is to draw a clear line between an independent variable and a dependent variable, then compute the vertical distance between the spot and the line for each data set, and add them up.

 

6. Linear Regression

Linear regression describes the effect on the dependent variable when the independent variable is changed; as a result, the independent variable is referred to as the explained variable, and the dependent variable is referred to as the factor of interest.

7. Logistic Regression

A useful statistical method for modeling a binomial output including one or more explanatory factors is logistic regression. It calculates the relationship between the categorical dependent variable and one or even more independent variables by using a logistic function to measure probabilities.

8. Support Vector Machines

In SVM, a hyperplane is used to correctly divide the data points throughout the input variable space by their corresponding class, which is either 0 or 1.

9. Clustering Algorithms

Clustering is a data analysis method of finding meaningful data patterns, such as groupings of consumers based on their behavior or geography, because it is an unsupervised learning issue.

10. Gradient Boosting & AdaBoost

When dealing with large amounts of data, boosting algorithms are employed to make very accurate predictions. It is an ensemble learning method that mixes the different susceptible and mediocre predictors to produce strong predictors or estimators by combining the predictive power of varied base estimators in order to improve resilience.

Conclusion

Machine learning algorithms assist automate manual processes to make our lives easier, from simple day-to-day operations to making systems smarter. Machine learning’s importance has increased even more, which is why eager data scientists and engineers are eager to acquire new approaches to improve their abilities.

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TOP 5 PYTHON IDES AND CODE EDITORS IN 2021

 Python is mostly used for server-side web development, software development, arithmetic, scripting, and artificial intelligence (AI). IDEs stands for Integrated Development Environments. It is a software package that includes tools for developing and testing software. The IDE aids in the automation of a developer’s activity by minimizing manual efforts and bringing all of the tools together in a single framework. A code editor is essentially a text editor in which a programmer may create code for the development of any software. The developer can also save tiny text files for the code in the code editor. Code editors, in comparison to IDEs, are quick to use and have a small size.

 

PyCharm:

Jet Brains produced PyCharm, which is one of the most frequently used Python IDEs. It is one of the top Python IDEs. PyCharm is all a Python developer needs to get things done. PyCharm allows developers to build clean, maintainable code. It aids productivity and provides smart support to developers. It includes a smart code editor, smart code navigation, and quick and safe refactoring.

 

Spyder:

Spyder is another well-known IDE brand. It’s an excellent Python compiler. It is well-known for python programming. It was created primarily for scientists and engineers to provide Python a robust scientific environment. It has advanced editing, debugging, and data exploring capabilities. It has a nice plugin system and API and is extremely expandable. A developer can use Spyder as an extension because it utilizes PYQT. It’s a capable IDE. It is one of the top Python IDEs.

 

Pydev:

It is also one of the top Python IDEs. PyDev is a third-party Eclipse plugin. It’s a Python programming environment in the form of an IDE. It has a linear size. It mainly focuses on the refactoring of python code, debugging in the graphical style, analysis of code, etc. Python interpreter with a lot of power.

 

Idle:

Idle is a well-known Python-based integrated development environment that has been integrated with the default language. It is one of the top Python IDEs. Idle is a very basic and simple IDE that is mostly used by beginning-level developers that wish to experiment with Python. It’s also cross-platform, which helps new developers a lot, but it’s also known as a disposable IDE since after learning the basics, a developer goes on to a more advanced IDE.

 

Wing:

The wing is another popular and capable IDE on the market today, with several useful features for python development. It has a powerful debugger and a smart editor that make interactive Python development quick, accurate, and enjoyable. Wing also offers a 30-day trial edition for developers to get a feel for the software’s capabilities.

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TOP 10 DATA SCIENCE JOBS TO APPLY IN SEPTEMBER 2021

 

Looking for a job in the field of data science? Check out these new openings

Presently, the data science course is one of the top courses that assist you to land trending job areas globally. If you are pursuing a data science course or you are already a data scientist then, without a doubt, it is the best profession to pursue your career in the present developing world.

Each organization has its necessities with regards to data science; nonetheless, various jobs are directly or indirectly, related to data science, these jobs are data scientists, data engineers, data architects, machine learning engineers, big data engineers, and artificial intelligence experts.

Here are the top 10 data science jobs to apply for in September 2021:

 1. Senior Manager – Data Science at Bain & Company

Location: Bangalore

2. Senior Manager – Data Science at The Smart Cube

Location: Noida/Gurgaon

3. Data Scientist – Banking/Insurance at Aureus Analytics

Location: Mumbai

4. Data Scientist – Product Owner at Dell

Location: Bangalore

5. Data Scientist – Advanced Machine Learning at Thoucentric

Location: Bangalore

6. Data Scientist – Advanced Analytics at Eclerx

Location: Pune

7. Data Scientist at Analytos

Location: Kolkata

8. Data Scientist at Jumio Corporation

Location: Jaipur, Rajasthan

9. Data Scientist – C3 Developer at Shell

Location: Chennai/Bangalore

10. Data Scientist at IBM

Location: Bangalore

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TOP 10 FREQUENTLY ASKED MACHINE LEARNING INTERVIEW QUESTIONS

 This article will take you through the top 10 frequently asked machine learning interview questions.

Companies are using new-age technologies like artificial intelligence (AI) and machine learning (ML) to make information and services more accessible to users. These technologies are being increasingly adopted in industries such as banking, finance, retail, manufacturing, healthcare, and more. Some of the in-demand organizational jobs that are adopting AI include data scientists, artificial intelligence engineers, Machine learning Engineers, and data analysts. If you want to apply for jobs like these, you should be aware of the sorts of machine learning interview questions that recruiters and hiring managers could ask. This article guides you through some of the most common machine learning interview questions and answers you’ll come across on your journey to landing your ideal job.

1. 

Explain what artificial intelligence (AI), machine learning (ML), and deep learning are and what they mean.

2 . 

What are the different types of machine learning?

3.

Make a distinction between data mining and machine learning.

4. 

What is the difference between deep learning and machine learning?

5. 

What is overfitting in machine learning? Why does it occur and how can you stay away from this?

6. 

In machine learning, what is a hypothesis?

7.

In machine learning, what is Bayes’ theorem?

8. 

What is cross-validation in machine learning?

9.

What is entropy in machine learning?

10. 

What is the epoch in machine learning?

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TOP 10 AFFORDABLE COUNTRIES TO STUDY DATA SCIENCE

 

You can’t miss this list of countries if you are data science passionate and want to enhance your skills.

Data science is the trendiest and the most in-demand course to follow up in 2021. With millions of qubits of data produced every day, it is really important to cater to the number of data scientists and analysts to analyze, interpret, organize and distribute data. But more often than not in countries like the USA and UK, pursuing data science becomes really difficult to afford. So here we at Analytics Insight churned up the whole world’s universities and colleges and countries to bring to you the top 10 affordable countries to study data science in the world.

1. India

(The average cost of the course would be around 20,000 – 5,00,000 INR)

2. Norway

(Study Costs in Norway

Tuition Fees: FREE

Living Expenses: NOK 116,369 (appx. INR 9 – 10 Lacs) per year.)

3. Germany

(Study Costs in Germany

Tuition Fees: EUR 125 – EUR 1,500 per semester | INR 20,000 – 2 Lacs per year

Living Expenses: EUR 650 – 800 per month | appx. INR 6.5 – 8 Lacs per year.)

4. Switzerland

(Tuition Fees: CHF 500 – 2,000 per semester | INR 1.2 Lacs – 4 Lacs per year

Living Expenses: CHF 1,400 – 1,800 per month | INR 10 – 14 Lacs per year.)

4. Denmark

(Tuition Fees: DKK 45,000 – DKK 120,000 per year | INR 4.5 Lacs to 12 Lacs per year

Living Expenses: DKK 6,000 – DKK 10,000 per month | INR 7.5 Lacs – INR 11 Lacs per year.)

5. Belgium

(Tuition Fees: EUR 900 – EUR 4,000 per year | INR 70,000 – INR 3.5 Lacs per year

Living Expenses: EUR 700 – EUR 1,200 per month | INR 7 – 11 Lacs per year.)

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TOP 10 BUSINESS INTELLIGENCE AND ANALYTICS PROVIDERS IN INDIA

 

Today, all organization’s decisions and processes are driven by digitization and data analysis. Collecting data from digital systems and analyzing it is the key to making sophisticated and intelligent decisions for business organizations. With digital transformation, businesses rely on big data and data science to enhance efficiency and this has increased the demand for business intelligence and business analytics solutions thus encouraging the need for business intelligence and business analytics companies to emerge.

Indian business intelligence companies provide business reporting solutions that are all-inclusive, reasonably priced, and simple to understand, implement, organize and maintain. Most BI products offered by these firms are next-generation reporting solutions constructed from modern technology and processes. Here is the list of the top 10 business intelligence and analytics companies in India.

1. Teradata Corporation ( Located in around 40 nations )

2. MAIA Intelligence

3. Binary Semantics Ltd

4. IDC India Ltd.

5.TechAxes

6. Fractal

7. Manthan

8. Latent View (Based in New Jersey, Latent View has its Indian operations in Mumbai and Chennai.)

9. Tiger Analytics

10. Absolut Data

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Big Data Analytics Tutorial

 The volume of data that one has to deal with has exploded to unimaginable levels in the past decade, and at the same time, the price of data storage has systematically reduced. Private companies and research institutions capture terabytes of data about their users’ interactions, business, social media, and also sensors from devices such as mobile phones and automobiles. The challenge of this era is to make sense of this sea of data. This is where big data analytics comes into the picture.

Big Data Analytics largely involves collecting data from different sources, munge it in a way that it becomes available to be consumed by analysts, and finally deliver data products useful to the organization business.

The process of converting large amounts of unstructured raw data, retrieved from different sources to a data product useful for organizations forms the core of Big Data Analytics.

In this tutorial, we will discuss the most fundamental concepts and methods of Big Data Analytics.

Topics include :

• Big Data Analytics Tutorial
• Big Data Analytics - Home
• Big Data Analytics - Overview
• Big Data Analytics - Data Life Cycle
• Big Data Analytics - Methodology
• Core Deliverables
• Key Stakeholders
• Big Data Analytics - Data Analyst
• Big Data Analytics - Data Scientist

• Big Data Analytics Project
• Data Analytics - Problem Definition
• Big Data Analytics - Data Collection
• Big Data Analytics - Cleansing data
• Big Data Analytics - Summarizing
• Big Data Analytics - Data Exploration
• Data Visualization

• Big Data Analytics Methods
• Big Data Analytics - Introduction to R
• Data Analytics - Introduction to SQL
• Big Data Analytics - Charts & Graphs
• Big Data Analytics - Data Tools
• Data Analytics - Statistical Methods

• Advanced Methods
• Machine Learning for Data Analysis
• Naive Bayes Classifier
• K-Means Clustering
• Association Rules
• Big Data Analytics - Decision Trees
• Logistic Regression
• Big Data Analytics - Time Series
• Big Data Analytics - Text Analytics
• Big Data Analytics - Online Learning

• Big Data Analytics Useful Resources
• Big Data Analytics - Quick Guide
• Big Data Analytics - Resources
• Big Data Analytics - Discussion

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