Machine Learning Tutorial

What is Machine Learning? Machine Learning Life Cycle Python Anaconda setup Difference between ML/ AI/ Deep Learning Understanding different types of Machine Learning Data Pre-processing Supervised Machine Learning

ML Regression Algorithm

Linear Regression

ML Classification Algorithm

Introduction to ML Classification Algorithm Logistic Regression Support Vector Machine Decision Tree Naïve Bayes Random Forest

ML Clustering Algorithm

Introduction to ML Clustering Algorithm K-means Clustering Hierarchical Clustering

ML Association Rule learning Algorithm

Introduction to association Rule Learning Algorithm

Miscellaneous

Top 5 programming languages and their libraries for Machine Learning Basics Vectors in Linear Algebra in ML Decision Tree Algorithm in Machine Learning Bias and Variances in Machine Learning Machine Learning Projects for the Final Year Students Top Machine Learning Jobs Machine Learning Engineer Salary in Different Organisation Best Python Libraries for Machine Learning Regularization in Machine Learning Some Innovative Project Ideas in Machine Learning What is Cross Compiler Decoding in Communication Process IPv4 vs IPv6 Supernetting in Network Layer TCP Ports TCP vs UDP TCP Working of ARP Hands-on Machine Learning with Scikit-Learn, TensorFlow, and Keras Kaggle Machine Learning Project Machine Learning Gesture Recognition Machine Learning IDE Pattern Recognition and Machine Learning a MATLAB Companion Chi-Square Test in Machine Learning Heart Disease Prediction Using Machine Learning Machine Learning and Neural Networks Machine Learning for Audio Classification Standardization in Machine Learning Student Performance Prediction Using Machine Learning Data Visualization in Machine Learning How to avoid over fitting In Machine Learning Machine Learning in Education Machine Learning in Robotics Network intrusion Detection System using Machine Learning

Is Data Science and Machine Learning Same?

Although the terms "machine learning" and "data science" are sometimes used synonymously, they have distinct meanings. Despite the fact that both professions entail dealing with data, their objectives and methods differ.

The broad discipline of data science involves employing a variety of methods to draw conclusions and information from data. Data scientists analyse and interpret data using statistical techniques, data visualisation, and machine learning algorithms. Their objective is to interpret data and employ it to guide decision-making.

The use of algorithms and statistical models, on the other hand, allows machines to learn from information and arrive at predictions or judgements without being pattern recognition. Machine learning is a subset of data science. Machine learning algorithms are used for a variety of tasks, such as fraud detection, picture identification, and natural language processing. They can be supervised, uncontrolled, or supervised learning.

In other words, machine learning is the process of creating algorithms that are able to learn from information to arrive at predictions or judgements, whereas data science is the application of diverse techniques to analyse and create sense of data.

Despite their differences, data science & machine learning are frequently combined. Data scientists utilise statistical techniques to assess the accuracy of their predictions after using algorithms that use machine learning to make accurate predictions or classify data. On the other hand, data scientists and machine learning engineers collaborate to develop and implement machine learning models.

In conclusion, while machine learning and data science are related topics, they differ from one another. Machine learning is a branch of data science that focuses on creating algorithms that are able to learn from information to arrive at predictions or judgements.

Data science is a vast field that involves analysing and interpreting data. Businesses and organisations who wish to make information decisions and obtain insights from both of these domains should be aware of their importance.

Although there are some approaches and objectives that both data science and machine learning share, there are also numerous significant differences between the two professions.

The following are some key distinctions between machine learning and data science:

Data science's main goal is to draw conclusions and knowledge using data, but machine learning's main goal is to create algorithms that are able to learn from information to arrive at judgements or predictions.

Focus: Compared to machine learning, data science encompasses a wider range of tasks, such as data gathering, data preparation and cleaning, exploratory data analysis, data techniques, and data visualisation. On the opposing hand, machine learning is largely concerned with creating and improving prediction models.

Techniques: To glean insights from data, data scientists employ a variety of techniques, such as statistical analysis, data gathering, and machine learning. Yet, machine learning is a branch of data science which specialises in employing algorithms to derive knowledge from data.

Data requirements: Machine learning needs structured data to train and improve predictive models, however data science may deal both with structured and unstructured data.

Applications: Market research, consumer segmentation, fraud prevention, and forecasting are just a few of the many uses for data science. On the other hand, machine learning is mainly utilised for prediction & classification tasks, including image recognition, processing of natural languages, or recommendation systems.

In conclusion, data science & machine learning are linked but separate sciences with various goals, methods, and applications. Deep learning is a specialised area that focuses on creating algorithms capable of learning from data, whereas data science covers a wider range of tasks.

Projects of data science:

In the quickly developing subject of data science, numerous methodologies are used to draw conclusions and information from data. Simple data analysis activities to sophisticated algorithms for machine learning that may draw conclusions or make predictions based on vast amounts of data can all be included in data science initiatives. We will talk about some typical initiatives in data science and its applications in this article.

Analysing Exploratory Data (EDA)

Data analysis and visualisation are steps in the process of understanding the properties and relationships of data, or EDA. EDA can assist in locating trends, anomalies, and correlations in the data that may be utilized to guide additional study. EDA is frequently used in combination with other approaches, such as statistical modelling or machine learning, and is a crucial first step in so many data science initiatives.

Statistical Modelling

Building predictive methods that can forecast future results using past information is known as predictive modelling. Several applications, including fraud detection, sales plan, or risk management, can benefit from the usage of predictive models. Decision trees, logistic regression, and linear regression are frequently used methods in predictive modelling.

Automatic Language Recognition (NLP)

The goal of NLP research is to make it possible for computers to comprehend and analyse human language. Many applications, including sentiment analysis, chatbots, even machine translation, make use of NLP principles. Text classification, recognition of named entities, and topic modelling are a few popular NLP techniques.

Processing of images

Analysing and modifying digital photographs to obtain information or improve their quality is known as image processing. Many different applications, including object detection, diagnostic imaging, and face recognition, can make use of image processing algorithms. Neural networks with convolution (CNNs), edge detection, and picture segmentation are a few typical image processing methods.

Systems of Recommendations

Algorithms used in recommendation systems can make suggestions to users for products or services based on personal preferences or past behaviour. Social media, e-commerce, and content - based recommendation platforms all frequently use recommendation algorithms.

Recommender system, content-based filtering, and hidden markov model are frequently used methods in recommendation systems.

In conclusion, data science experiments may range from simple machine learning techniques to in-depth exploratory data analysis.

Numerous applications, including recommendation systems, natural language processing, image analysis, and predictive modelling, can benefit from the use of data science approaches. The need for data scientists with experience in these fields is only expanding as data's significance in today's world grows.