Machine Learning - GeeksforGeeks

02 Jun, 2022
 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.
Machine Learning is an essential skill for any aspiring data analyst and data scientist, and also for those who wish to transform a massive amount of raw data into trends and predictions. Learn this skill today with Machine Learning Foundation – Self Paced Course , designed and curated by industry experts having years of expertise in ML and industry-based projects.
Introduction
Data and it’s Processing
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Dimensionality Reduction
Natural Language Processing
Neural Networks
ML – Deployment
ML – Applications
Miscellaneous
 
Introduction :
Getting Started with Machine LearningAn Introduction to Machine LearningWhat is Machine Learning ?Introduction to Data in Machine LearningDemystifying Machine LearningML – ApplicationsBest Python libraries for Machine LearningArtificial Intelligence | An IntroductionMachine Learning and Artificial IntelligenceDifference between Machine learning and Artificial IntelligenceAgents in Artificial Intelligence10 Basic Machine Learning Interview Questions
Getting Started with Machine Learning
An Introduction to Machine Learning
What is Machine Learning ?
Introduction to Data in Machine Learning
Demystifying Machine Learning
ML – Applications
Best Python libraries for Machine Learning
Artificial Intelligence | An Introduction
Machine Learning and Artificial Intelligence
Difference between Machine learning and Artificial Intelligence
Agents in Artificial Intelligence
10 Basic Machine Learning Interview Questions
Data and It’s Processing:
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 Models
Introduction to Data in Machine Learning
Understanding Data Processing
Python | Create Test DataSets using Sklearn
Python | Generate test datasets for Machine learning
Python | Data Preprocessing in Python
Data Cleaning
Feature Scaling – Part 1
Feature Scaling – Part 2
Python | Label Encoding of datasets
Python | One Hot Encoding of datasets
Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python
Dummy variable trap in Regression Models
Supervised learning :
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 classifier
Getting started with Classification
Basic Concept of Classification
Types of Regression Techniques
Classification vs Regression
ML | Types of Learning – Supervised Learning
Multiclass classification using scikit-learn
Gradient Descent :Gradient Descent algorithm and its variantsStochastic Gradient Descent (SGD)Mini-Batch Gradient Descent with PythonOptimization techniques for Gradient DescentIntroduction to Momentum-based Gradient Optimizer
Gradient Descent algorithm and its variants
Stochastic Gradient Descent (SGD)
Mini-Batch Gradient Descent with Python
Optimization techniques for Gradient Descent
Introduction to Momentum-based Gradient Optimizer
Linear 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 Regression
Introduction to Linear Regression
Gradient Descent in Linear Regression
Mathematical explanation for Linear Regression working
Normal Equation in Linear Regression
Linear Regression (Python Implementation)
Simple Linear-Regression using R
Univariate Linear Regression in Python
Multiple Linear Regression using Python
Multiple Linear Regression using R
Locally weighted Linear Regression
Generalized Linear Models
Python | Linear Regression using sklearn
Linear Regression Using Tensorflow
A Practical approach to Simple Linear Regression using R
Linear Regression using PyTorch
Pyspark | Linear regression using Apache MLlib
ML | Boston Housing Kaggle Challenge with Linear Regression
Python | Implementation of Polynomial Regression
Softmax Regression using TensorFlow
Logistic Regression :Understanding Logistic RegressionWhy Logistic Regression in Classification ?Logistic Regression using PythonCost function in Logistic RegressionLogistic Regression using Tensorflow
Understanding Logistic Regression
Why Logistic Regression in Classification ?
Logistic Regression using Python
Cost function in Logistic Regression
Logistic Regression using Tensorflow
Naive Bayes Classifiers
Support Vector:Support Vector Machines(SVMs) in PythonSVM Hyperparameter Tuning using GridSearchCVSupport Vector Machines(SVMs) in RUsing SVM to perform classification on a non-linear dataset
Support Vector Machines(SVMs) in Python
SVM Hyperparameter Tuning using GridSearchCV
Support Vector Machines(SVMs) in R
Using SVM to perform classification on a non-linear dataset
Decision Tree:Decision TreeDecision Tree Regression using sklearnDecision Tree Introduction with exampleDecision tree implementation using PythonDecision Tree in Software Engineering
Decision Tree
Decision Tree Regression using sklearn
Decision Tree Introduction with example
Decision tree implementation using Python
Decision Tree in Software Engineering
Random Forest:Random Forest Regression in PythonEnsemble ClassifierVoting Classifier using SklearnBagging classifier
Random Forest Regression in Python
Ensemble Classifier
Voting Classifier using Sklearn
Bagging classifier
Unsupervised learning :
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 Model
ML | Types of Learning – Unsupervised Learning
Supervised and Unsupervised learning
Clustering in Machine Learning
Different Types of Clustering Algorithm
K means Clustering – Introduction
Elbow Method for optimal value of k in KMeans
Random Initialization Trap in K-Means
ML | K-means++ Algorithm
Analysis of test data using K-Means Clustering in Python
Mini Batch K-means clustering algorithm
Mean-Shift Clustering
DBSCAN – Density based clustering
Implementing DBSCAN algorithm using Sklearn
Fuzzy Clustering
Spectral Clustering
OPTICS Clustering
OPTICS Clustering Implementing using Sklearn
Hierarchical clustering (Agglomerative and Divisive clustering)
Implementing Agglomerative Clustering using Sklearn
Gaussian Mixture Model
Reinforcement Learning:
Reinforcement learningReinforcement Learning Algorithm : Python Implementation using Q-learningIntroduction to Thompson SamplingGenetic Algorithm for Reinforcement LearningSARSA Reinforcement LearningQ-Learning in Python
Reinforcement learning
Reinforcement Learning Algorithm : Python Implementation using Q-learning
Introduction to Thompson Sampling
Genetic Algorithm for Reinforcement Learning
SARSA Reinforcement Learning
Q-Learning in Python
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 Learning
Introduction to Dimensionality Reduction
Introduction to Kernel PCA
Principal Component Analysis(PCA)
Principal Component Analysis with Python
Low-Rank Approximations
Overview of Linear Discriminant Analysis (LDA)
Mathematical Explanation of Linear Discriminant Analysis (LDA)
Generalized Discriminant Analysis (GDA)
Independent Component Analysis
Feature Mapping
Extra Tree Classifier for Feature Selection
Chi-Square Test for Feature Selection – Mathematical Explanation
ML | T-distributed Stochastic Neighbor Embedding (t-SNE) Algorithm
Python | How and where to apply Feature Scaling?
Parameters for Feature Selection
Underfitting and Overfitting in Machine Learning
Natural Language Processing :
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?
Introduction to Natural Language Processing
Text Preprocessing in Python | Set – 1
Text Preprocessing in Python | Set 2
Removing stop words with NLTK in Python
Tokenize text using NLTK in python
How tokenizing text, sentence, words works
Introduction to Stemming
Stemming words with NLTK
Lemmatization with NLTK
Lemmatization with TextBlob
How to get synonyms/antonyms from NLTK WordNet in Python?
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 Tensorflow
Introduction to Artificial Neutral Networks | Set 1
Introduction to Artificial Neural Network | Set 2
Introduction to ANN (Artificial Neural Networks) | Set 3 (Hybrid Systems)
Introduction to ANN | Set 4 (Network Architectures)
Activation functions
Implementing Artificial Neural Network training process in Python
A single neuron neural network in Python
Convolutional Neural NetworksIntroduction to Convolution Neural NetworkIntroduction to Pooling LayerIntroduction to PaddingTypes of padding in convolution layerApplying Convolutional Neural Network on mnist dataset
Introduction to Convolution Neural Network
Introduction to Pooling Layer
Introduction to Padding
Types of padding in convolution layer
Applying Convolutional Neural Network on mnist dataset
Recurrent 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 Networks
Introduction to Recurrent Neural Network
Recurrent Neural Networks Explanation
seq2seq model
Introduction to Long Short Term Memory
Long Short Term Memory Networks Explanation
Gated Recurrent Unit Networks(GAN)
Text Generation using Gated Recurrent Unit Networks
GANs – Generative Adversarial NetworkIntroduction to Generative Adversarial NetworkGenerative Adversarial Networks (GANs)Use Cases of Generative Adversarial NetworksBuilding a Generative Adversarial Network using KerasModal Collapse in GANs
Introduction to Generative Adversarial Network
Generative Adversarial Networks (GANs)
Use Cases of Generative Adversarial Networks
Building a Generative Adversarial Network using Keras
Modal Collapse in GANs
Introduction to Deep Q-Learning
Implementing Deep Q-Learning using Tensorflow
ML – Deployment :
Deploy 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?https://www.geeksforgeeks.org/deploying-ml-models-as-api-using-fastapi/?ref=rpDeploying Scrapy spider on ScrapingHub
Deploy your Machine Learning web app (Streamlit) on Heroku
Deploy a Machine Learning Model using Streamlit Library
Deploy Machine Learning Model using Flask
Python – Create UIs for prototyping Machine Learning model with Gradio
How to Prepare Data Before Deploying a Machine Learning Model?
https://www.geeksforgeeks.org/deploying-ml-models-as-api-using-fastapi/?ref=rp
Deploying Scrapy spider on ScrapingHub
ML – 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?
Rainfall prediction using Linear regression
Identifying handwritten digits using Logistic Regression in PyTorch
Kaggle Breast Cancer Wisconsin Diagnosis using Logistic Regression
Python | Implementation of Movie Recommender System
Support Vector Machine to recognize facial features in C++
Decision Trees – Fake (Counterfeit) Coin Puzzle (12 Coin Puzzle)
Credit Card Fraud Detection
NLP analysis of Restaurant reviews
Applying Multinomial Naive Bayes to NLP Problems
Image compression using K-means clustering
Deep learning | Image Caption Generation using the Avengers EndGames Characters
How Does Google Use Machine Learning?
How Does NASA Use Machine Learning?
5 Mind-Blowing Ways Facebook Uses Machine Learning
Targeted Advertising using Machine Learning
How Machine Learning Is Used by Famous Companies?
Misc :
Pattern Recognition | IntroductionCalculate Efficiency Of Binary ClassifierLogistic Regression v/s Decision Tree ClassificationR vs Python in DatascienceExplanation of Fundamental Functions involved in A3C algorithmDifferential Privacy and Deep LearningArtificial intelligence vs Machine Learning vs Deep LearningIntroduction to Multi-Task Learning(MTL) for Deep LearningTop 10 Algorithms every Machine Learning Engineer should knowAzure Virtual Machine for Machine Learning30 minutes to machine learningWhat is AutoML in Machine Learning?Confusion Matrix in Machine Learning
Pattern Recognition | Introduction
Calculate Efficiency Of Binary Classifier
Logistic Regression v/s Decision Tree Classification
R vs Python in Datascience
Explanation of Fundamental Functions involved in A3C algorithm
Differential Privacy and Deep Learning
Artificial intelligence vs Machine Learning vs Deep Learning
Introduction to Multi-Task Learning(MTL) for Deep Learning
Top 10 Algorithms every Machine Learning Engineer should know
Azure Virtual Machine for Machine Learning
30 minutes to machine learning
What is AutoML in Machine Learning?
Confusion Matrix in Machine Learning
Machines are learning, so why do you wish to get left behind? Strengthen your ML and AI foundations today and become future ready. This self-paced course will help you learn advanced concepts like- Regression, Classification, Data Dimensionality and much more. Also included- Projects that will help you get hands-on experience. So wait no more, and strengthen your Machine Learning Foundations.
Every organisation now relies on data before making any important decisions regarding their future. So, it is safe to say that Data is really the king now. So why do you want to get left behind? This LIVE course will introduce the learner to advanced concepts like: Linear Regression, Naive Bayes & KNN, Numpy, Pandas, Matlab & much more. You will also get to work on real-life projects through the course. So wait no more, Become a Data Science Expert now.
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