Machine Learning tutorial covers basic and advanced concepts, specially designed to cater to both students and experienced working professionals.
This machine learning tutorial helps you gain a solid introduction to the fundamentals of machine learning and explore a wide range of techniques, including supervised, unsupervised, and reinforcement learning.
Machine learning (ML) is a subdomain of artificial intelligence (AI) that focuses on developing systems that learn—or improve performance—based on the data they ingest. Artificial intelligence is a broad word that refers to systems or machines that resemble human intelligence. Machine learning and AI are frequently discussed together, and the terms are occasionally used interchangeably, although they do not signify the same thing. A crucial distinction is that, while all machine learning is AI, not all AI is machine learning.
What is Machine Learning?Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.
Table of Content
What is Machine Learning?Features of Machine LearningGetting StartedData and It’s ProcessingSupervised learning Unsupervised learning Reinforcement LearningDimensionality Reduction Natural Language Processing Neural Networks ML – Deployment ML – Applications Features of Machine LearningMachine learning is a data-driven technology. A large amount of data is generated by organizations daily, enabling them to identify notable relationships and make better decisions.Machines can learn from past data and automatically improve their performance.Given a dataset, ML can detect various patterns in the data.For large organizations, branding is crucial, and targeting a relatable customer base becomes easier.It is similar to data mining, as both deal with substantial amounts of data.Introduction :We will start by exploring the foundational concepts of machine learning, including its history, key terminology, and the types of problems it can solve.
Getting Started with Machine LearningAn Introduction to Machine LearningIntroduction to Data in Machine LearningDemystifying Machine LearningML – ApplicationsBest Python libraries for Machine LearningMachine Learning and Artificial IntelligenceDifference between Machine learning and Artificial IntelligenceData and It’s Processing:Data is the foundation of machine learning. The quality and quantity of data you have directly impact the performance of your machine learning models. In this section, we will explore various aspects of data and its processing, which are crucial for building robust ML systems.
Introduction to Data in Machine LearningUnderstanding Data ProcessingPython | Create Test DataSets using SklearnPython | Generate test datasets for Machine learningPython | Data Preprocessing in PythonData CleaningFeature Scaling – Part 1Feature Scaling – Part 2Python | Label Encoding of datasetsPython | One Hot Encoding of datasetsHandling Imbalanced Data with SMOTE and Near Miss Algorithm in PythonDummy variable trap in Regression ModelsSupervised learning :Supervised learning is a fundamental approach in machine learning where models are trained on labeled datasets. This technique is used to predict outcomes based on input features, making it invaluable for various applications, from spam detection to medical diagnosis. In this section, we will cover key concepts and methodologies related to supervised learning, including classification and regression techniques.
Getting started with ClassificationBasic Concept of ClassificationTypes of Regression TechniquesClassification vs RegressionML | Types of Learning – Supervised LearningMulticlass classification using scikit-learnGradient Descent :Gradient Descent algorithm and its variantsStochastic Gradient Descent (SGD)Mini-Batch Gradient Descent with PythonOptimization techniques for Gradient DescentIntroduction to Momentum-based Gradient OptimizerLinear Regression :Introduction to Linear RegressionGradient Descent in Linear RegressionMathematical explanation for Linear Regression workingNormal Equation in Linear RegressionLinear Regression (Python Implementation)Simple Linear-Regression using RUnivariate Linear Regression in PythonMultiple Linear Regression using PythonMultiple Linear Regression using RLocally weighted Linear RegressionGeneralized Linear ModelsPython | Linear Regression using sklearnLinear Regression Using TensorflowA Practical approach to Simple Linear Regression using RLinear Regression using PyTorchPyspark | Linear regression using Apache MLlibML | Boston Housing Kaggle Challenge with Linear RegressionPython | Implementation of Polynomial RegressionSoftmax Regression using TensorFlowLogistic Regression :Understanding Logistic RegressionWhy Logistic Regression in Classification ?Logistic Regression using PythonCost function in Logistic RegressionLogistic Regression using TensorflowNaive Bayes ClassifiersSupport Vector:Support Vector Machines(SVMs) in PythonSVM Hyperparameter Tuning using GridSearchCVSupport Vector Machines(SVMs) in RUsing SVM to perform classification on a non-linear datasetDecision Tree:Decision TreeDecision Tree Regression using sklearnDecision Tree Introduction with exampleDecision tree implementation using PythonDecision Tree in Software EngineeringRandom Forest:Random Forest Regression in PythonEnsemble ClassifierVoting Classifier using SklearnBagging classifierUnsupervised learning :Unsupervised learning is a key area of machine learning that focuses on discovering hidden patterns and structures in data without labeled outputs. Unlike supervised learning, where models learn from labeled examples, unsupervised learning relies on the inherent structure of the input data. This section will delve into various techniques and applications of unsupervised learning, primarily focusing on clustering methods.
ML | Types of Learning – Unsupervised LearningSupervised and Unsupervised learningClustering in Machine LearningDifferent Types of Clustering AlgorithmK means Clustering – IntroductionElbow Method for optimal value of k in KMeansRandom Initialization Trap in K-MeansML | K-means++ AlgorithmAnalysis of test data using K-Means Clustering in PythonMini Batch K-means clustering algorithmMean-Shift ClusteringDBSCAN – Density based clusteringImplementing DBSCAN algorithm using SklearnFuzzy ClusteringSpectral ClusteringOPTICS ClusteringOPTICS Clustering Implementing using SklearnHierarchical clustering (Agglomerative and Divisive clustering)Implementing Agglomerative Clustering using SklearnGaussian Mixture ModelReinforcement Learning:Reinforcement Learning (RL) is a dynamic area of machine learning focused on how agents ought to take actions in an environment to maximize cumulative reward. Unlike supervised learning, where the model learns from a fixed dataset, RL involves learning through trial and error, making it particularly suited for complex decision-making problems. This section will explore the foundational concepts, algorithms, and applications of reinforcement learning.
Reinforcement learningReinforcement Learning Algorithm : Python Implementation using Q-learningIntroduction to Thompson SamplingGenetic Algorithm for Reinforcement LearningSARSA Reinforcement LearningQ-Learning in PythonDimensionality Reduction :Dimensionality Reduction is a crucial technique in machine learning and data analysis that focuses on reducing the number of features or dimensions in a dataset while preserving essential information. As datasets grow in complexity, high dimensionality can lead to issues such as overfitting, increased computation time, and difficulties in visualization. This section will explore various methods and applications of dimensionality reduction.
Introduction to Dimensionality ReductionIntroduction to Kernel PCAPrincipal Component Analysis(PCA)Principal Component Analysis with PythonLow-Rank ApproximationsOverview of Linear Discriminant Analysis (LDA)Mathematical Explanation of Linear Discriminant Analysis (LDA)Generalized Discriminant Analysis (GDA)Independent Component AnalysisFeature MappingExtra Tree Classifier for Feature SelectionChi-Square Test for Feature Selection – Mathematical ExplanationML | T-distributed Stochastic Neighbor Embedding (t-SNE) AlgorithmPython | How and where to apply Feature Scaling?Parameters for Feature SelectionUnderfitting and Overfitting in Machine LearningNatural Language Processing :Natural Language Processing (NLP) is a vital subfield of artificial intelligence and machine learning that focuses on the interaction between computers and human language. It enables machines to understand, interpret, and generate human language in a way that is both meaningful and useful. This section will explore the fundamental concepts, techniques, and applications of NLP.
Introduction to Natural Language ProcessingText Preprocessing in Python | Set – 1Text Preprocessing in Python | Set 2Removing stop words with NLTK in PythonTokenize text using NLTK in pythonHow tokenizing text, sentence, words worksIntroduction to StemmingStemming words with NLTKLemmatization with NLTKLemmatization with TextBlobHow to get synonyms/antonyms from NLTK WordNet in Python?Neural Networks :Neural networks are a fundamental component of deep learning and a powerful tool for solving complex problems in machine learning. Inspired by the human brain, neural networks consist of interconnected layers of nodes (neurons) that work together to process data, learn patterns, and make predictions. This section will cover the essential concepts, architectures, and applications of neural networks.
Introduction to Artificial Neutral Networks | Set 1Introduction to Artificial Neural Network | Set 2Introduction to ANN (Artificial Neural Networks) | Set 3 (Hybrid Systems)Introduction to ANN | Set 4 (Network Architectures)Activation functionsImplementing Artificial Neural Network training process in PythonA single neuron neural network in PythonConvolutional Neural NetworksIntroduction to Convolution Neural NetworkIntroduction to Pooling LayerIntroduction to PaddingTypes of padding in convolution layerApplying Convolutional Neural Network on mnist datasetRecurrent Neural NetworksIntroduction to Recurrent Neural NetworkRecurrent Neural Networks Explanationseq2seq modelIntroduction to Long Short Term MemoryLong Short Term Memory Networks ExplanationGated Recurrent Unit Networks(GAN)Text Generation using Gated Recurrent Unit NetworksGANs – Generative Adversarial NetworkIntroduction to Generative Adversarial NetworkGenerative Adversarial Networks (GANs)Use Cases of Generative Adversarial NetworksBuilding a Generative Adversarial Network using KerasModal Collapse in GANsIntroduction to Deep Q-LearningImplementing Deep Q-Learning using TensorflowML – Deployment :Machine learning deployementDeploy your Machine Learning web app (Streamlit) on HerokuDeploy a Machine Learning Model using Streamlit LibraryDeploy Machine Learning Model using FlaskPython – Create UIs for prototyping Machine Learning model with GradioHow to Prepare Data Before Deploying a Machine Learning Model?Deploying ML Models as API using FastAPIDeploying Scrapy spider on ScrapingHubML – Applications :Rainfall prediction using Linear regressionIdentifying handwritten digits using Logistic Regression in PyTorchKaggle Breast Cancer Wisconsin Diagnosis using Logistic RegressionPython | Implementation of Movie Recommender SystemSupport Vector Machine to recognize facial features in C++Decision Trees – Fake (Counterfeit) Coin Puzzle (12 Coin Puzzle)Credit Card Fraud DetectionNLP analysis of Restaurant reviewsApplying Multinomial Naive Bayes to NLP ProblemsImage compression using K-means clusteringDeep learning | Image Caption Generation using the Avengers EndGames CharactersHow Does Google Use Machine Learning?How Does NASA Use Machine Learning?5 Mind-Blowing Ways Facebook Uses Machine LearningTargeted Advertising using Machine LearningHow Machine Learning Is Used by Famous Companies?FAQs on Machine Learning TutorialQ.1 What is Machine learning and how is it different from Deep learning ?Answer:
Machine learning develop programs that can access data and learn from it. Deep learning is the sub domain of the machine learning. Deep learning supports automatic extraction of features from the raw data.
Q.2. What are the different type of machine learning algorithms ?Answer:
Supervised algorithms: These are the algorithms which learn from the labelled data, e.g. images labelled with dog face or not. Algorithm depends on supervised or labelled data. e.g. regression, object detection, segmentation.Non-Supervised algorithms: These are the algorithms which learn from the non labelled data, e.g. bunch of images given to make a similar set of images. e.g. clustering, dimensionality reduction etc.Semi-Supervised algorithms: Algorithms that uses both supervised or non-supervised data. Majority portion of data use for these algorithms are not supervised data. e.g. anamoly detection.Q.3. Why we use machine learning ?Answer:
Machine learning is used to make decisions based on data. By modelling the algorithms on the bases of historical data, Algorithms find the patterns and relationships that are difficult for humans to detect. These patterns are now further use for the future references to predict solution of unseen problems.
Q.4. What is the difference between Artificial Intelligence and Machine learning ?Answer:
ARTIFICIAL INTELLIGENCEMACHINE LEARNINGDevelop an intelligent system that perform variety of complex jobs.Construct machines that can only accomplish the jobs for which they have trained.It works as a program that does smart work.The tasks systems machine takes data and learns from data.AI has broad variety of applications.ML allows systems to learn new things from data.AI leads wisdom.ML leads to knowledge.K
kartik
Improve Next ArticlePython for Machine Learning