Machine Learning

Machine Learning Courses

Learn how to teach computers to learn. These courses cover the core concepts and tools in machine learning — from training models to evaluating predictions and building intelligent applications.

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A/B Test DesignAI Transparency AwarenessARIMA ModelingActive Learning FundamentalsAdaptive AlgorithmsAdvanced ARIMA TechniquesAdvanced Ensemble IntegrationAdvanced Text CleaningAlgorithm Evaluation and ComparisonAnomaly detection evaluation Applied Calibration WorkflowsArtificial immune systemsAutomated Search with scikit-learnBackward Difference CodingBagging and Random ForestsBayes' TheoremBayesian OptimizationBoosting AlgorithmsCalibration Metrics (ECE, MCE, Brier Score)CatBoost ModelingCategorical Feature HandlingChi-Square AnalysisClassification Loss AnalysisClassification metrics (Accuracy, Precision, Recall, F1, ROC–AUC) Clustering evaluation (Silhouette, Davies–Bouldin, Calinski–Harabasz) Clustering fundamentals and algorithms Coefficient VisualizationCommittee-Based QueryingConvergence TheoryConvex AnalysisCoordinate Reference SystemsCorrelation AnalysisCovariance and eigen decomposition Cross-validation techniquesDBSCAN: noise handling and irregular shapes Data Cleaning Data Leakage PreventionData PreprocessingData StorytellingData Transformation Data Visualization with matplotlib and seabornData normalization and distance metrics Deduplication AlgorithmsDensity-Weighted SamplingDeployment Best PracticesDescriptive StatisticsDimensionality reduction Dimensionality reduction evaluation Drift Detection FundamentalsDynamic Programming MethodsEncoding Leakage PreventionEnsemble Learning FundamentalsEthical AI PrinciplesEvolutionary optimization Experiment Tracking with MLflowExperimental Data PreparationExplainable AI FundamentalsExploratory Data AnalysisFairness in MLFeature Encoding Feature Engineering Feature Engineering for TSFeature ScalingFeature Scaling Feature Selection Feature Selection MethodsForecast Evaluation MetricsForecasting StrategiesFunctions & SetsFuzzy Matching in PythonGaussian Mixture Models: probabilistic clusteringGenetic algorithms Geospatial Data FundamentalsGeospatial VisualizationGradient Boosting for TSGradient DescentGradient Descent Graph Embedding IntuitionGraph Representation in PythonGraph Theory for MLGraphSAGE ConceptsGymnasium BasicsHandling missing and categorical data Helmert CodingHierarchical clustering and dendrograms High-Cardinality Feature EncodingHistogram BinningHybrid Rule-Based SystemsHyperparameter TuningHyperparameter Tuning FundamentalsHypothesis TestingInformation-Theoretic LossesIntegrals Isolation Forest ImplementationIsotonic RegressionK-Means: principles and cluster optimization Knowledge Graph Embedding ModelsKnowledge Graph FundamentalsKolmogorov–Smirnov TestL1, L2, and Elastic Net RegularizationLabel Efficiency TechniquesLearning Curve AnalysisLeave-One-Out EncodingLightGBM ModelingLimits & Derivatives Linear Regression with PythonLinear Transformations Link PredictionLocal Outlier Factor AnalysisLogistic RegressionLoss Function Selection and ComparisonMLOps FundamentalsMachine Learning with scikit-learnManual Search MethodsMathematical Foundations of Loss FunctionsMathematical OptimizationMatrix Decomposition Mean-CenteringMissing Value Imputation Model BlendingModel Deployment with FastAPI and DockerModel Evaluation and DiagnosticsModel Evaluation and GeneralizationModel InterpretabilityModel InterpretationModel Monitoring and CI/CDModel RegularizationModel Training and EvaluationModel-Based Drift DetectionMomentum MethodsMonitoring Model DegradationMonte Carlo TechniquesMulti-Armed Bandit AlgorithmsMultivariate AnalysisNeuroevolutionNode ClassificationNormalization (L1, L2, Max)One-Class SVM for Novelty DetectionOutlier Detection Outlier Detection FundamentalsOverfitting and RegularizationParticle swarm optimizationPattern MiningPipeline Automation with AirflowPipeline BuildingPipeline ConstructionPlatt ScalingPolynomial CodingPopulation Stability IndexPreprocessing PipelinesPrincipal component analysis (PCA) Probabilistic Model CalibrationProbability DistributionsProbability Rules Python Classification ModelsPython ProgrammingRIPPER AlgorithmRandom Walks on GraphsReasoning over Knowledge GraphsRecord Linkage TechniquesRegression Loss AnalysisRegression metrics (MSE, RMSE, MAE, R²) Reinforcement Learning FoundationsReliability DiagramsRisk Minimization TheoryRule PruningRule Quality MetricsRule-Based ModelingRuleFit AlgorithmSampling Strategies in MLSeries Analysis Similarity Scoring for GraphsSpatial Joins and OverlaysSpatial OperationsStandardizationStatistical Anomaly DetectionStatistical Drift MetricsStatistical InterpretationStatistical Measures Stochastic OptimizationSwarm intelligenceTemporal ValidationTemporal-Difference LearningTime Series AnalysisTime Series WindowingTree-Based ForecastingTriple Scoring FunctionsUncertainty-Based QueryingVector and Raster Data HandlingVectors & Matrices Weight-of-Evidence EncodingWhitening and DecorrelationXAI Methods and ConceptsXGBoost Modelingscikit-learn Active Learning Implementationt-test and z-test Application

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Courses

Browse Machine Learning courses and projects

Level

Type of lesson

Technologies

Introduction to Machine Learning with Python

4 hours

32 chapters

Intermediate

Acquired skills: Machine Learning with scikit-learn, Model Training and Evaluation, Hyperparameter Tuning

Linear Regression with Python

2 hours

19 chapters

Intermediate

Acquired skills: Linear Regression with Python, Model Training and Evaluation

Classification with Python

3 hours

24 chapters

Intermediate

Acquired skills: Python Programming, Python Classification Models, Logistic Regression, Data Preprocessing, Model Training and Evaluation, Hyperparameter Tuning

Cluster Analysis with Python

4 hours

34 chapters

Intermediate

Acquired skills: Clustering fundamentals and algorithms , Handling missing and categorical data , Data normalization and distance metrics , K-Means: principles and cluster optimization , Hierarchical clustering and dendrograms , DBSCAN: noise handling and irregular shapes , Gaussian Mixture Models: probabilistic clustering

Introduction to Reinforcement Learning with Python

6 hours

37 chapters

Advanced

Acquired skills: Reinforcement Learning Foundations, Multi-Armed Bandit Algorithms, Dynamic Programming Methods, Monte Carlo Techniques, Temporal-Difference Learning, Gymnasium Basics

Active Learning with Python

1 hour

10 chapters

Intermediate

Acquired skills: Active Learning Fundamentals, Label Efficiency Techniques, Sampling Strategies in ML, Uncertainty-Based Querying, Committee-Based Querying, Density-Weighted Sampling, scikit-learn Active Learning Implementation, Learning Curve Analysis

Advanced Tree-Based Models with Python

1 hour

9 chapters

Intermediate

Acquired skills: CatBoost Modeling, XGBoost Modeling, LightGBM Modeling, Model Regularization, Categorical Feature Handling, Model Interpretation, Model Blending, Deployment Best Practices

Applied Hypothesis Testing & A/B Testing

3 hours

31 chapters

Beginner

Acquired skills: Hypothesis Testing, t-test and z-test Application, Chi-Square Analysis, A/B Test Design, Experimental Data Preparation, Statistical Interpretation

Bio-Inspired Algorithms with Python

1 hour

16 chapters

Beginner

Acquired skills: Evolutionary optimization , Swarm intelligence, Genetic algorithms , Particle swarm optimization, Artificial immune systems, Neuroevolution

Data Cleaning Techniques in Python

2 hours

12 chapters

Intermediate

Acquired skills: Fuzzy Matching in Python, Deduplication Algorithms, Record Linkage Techniques, Advanced Text Cleaning

Data Preprocessing and Feature Engineering with Python

1 hour

12 chapters

Beginner

Acquired skills: Data Cleaning , Missing Value Imputation , Outlier Detection , Feature Encoding , Feature Scaling , Data Transformation , Feature Engineering , Feature Selection , Pipeline Building

Ensemble Learning Techniques with Python

1 hour

14 chapters

Beginner

Acquired skills: Ensemble Learning Fundamentals, Bagging and Random Forests, Boosting Algorithms, Advanced Ensemble Integration

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Machine Learning Mastery

Intermediate

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Real-world projects elevate your portfolio, showcasing practical skills to impress potential employers

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Video-based learning platforms

Codefinity

Video-based learning platforms

Video content

Downloadable materials

Esteemed certificates

Interactive learning environment

Error correction

AI assistant

Access to all content with one subscription

Career opportunitiesLearn about the most popular professions, average salaries, and companies actively seeking specialists in this field.

Data Scientist

Machine Learning Engineer

NLP Engineer

Deep Learning Engineer

Machine Learning Scientist

$149k

$197k

$246k

Min

Average

Max

Annual salary

(Average in the US)

Hiring companies

*Source: Glassdoor

Chosen by students of top schools

Including 30 out of top-30 U.S. colleges

Machine Learning Courses: Key Info and Questions

Introduction to Machine Learning Courses

Machine Learning (ML) is a field of AI that enables machines to learn from data and make predictions without explicit programming. Machine learning courses teach the foundational principles of supervised and unsupervised learning, model training, data processing, and evaluation techniques. From linear regression and classification to more advanced methods like reinforcement learning, these courses guide learners through building models that can recognize patterns in data and improve over time. ML is used in various applications, such as recommendation systems, fraud detection, and autonomous systems.

Benefits of our Machine Learning Courses

Our courses provide practical, hands-on experience with real-world data sets, expert instruction, and a flexible learning environment. This robust approach ensures that students not only learn theoretical concepts but also apply them practically.

Career Opportunities after Completion of Machine Learning Courses

Graduates can pursue various roles such as Data Scientist, Machine Learning Engineer, AI Analyst, or Research Scientist across industries like finance, healthcare, automotive, and technology.

Machine Learning Course Options

We offer a range of courses, from beginner-friendly ones like ML Introduction with scikit-learn to more advanced topics such as Classification with Python, Linear Regression with Python. You can also follow the Supervised Machine Learning track for a structured learning path.

Certificate Information

Upon completing any of our Machine Learning courses, students receive a Certificate of Completion, which is recognized across the industry and can help advance your career.

What is machine learning and why is it important?

Machine Learning is a branch of AI that allows software applications to become more accurate at predicting outcomes without being explicitly programmed. It's crucial for creating adaptive algorithms that can process and learn from data in real-time.

Where is machine learning used in the industry?

Machine Learning is widely used in industries such as finance for algorithmic trading, healthcare for predictive diagnostics, automotive for self-driving cars, and in consumer services for personalized experiences.

What are the career opportunities in machine learning?

Careers in Machine Learning include roles like Machine Learning Engineer, Data Analyst, NLP Scientist, and roles in emerging technologies that require data-driven decision making.

How to Choose the Suitable Machine Learning Course?

Consider your current skill level and your career goals. Beginners should start with "ML Introduction with scikit-learn," while those with some background might prefer more specialized courses like Classification with Python and Linear Regression with Python

What is the Cost of Training for Machine Learning Courses?

The cost of training depends on the type of subscription and its duration. For precise and detailed pricing information, along with any available discounts, please visit our payment page.

Which Machine Learning Course is Best Suited for Beginners?

"ML Introduction with scikit-learn" is ideal for beginners unfamiliar with Machine Learning, providing foundational knowledge necessary to progress in this field.

What are the key skills required to excel in Machine Learning?

Key skills include a strong grasp of statistics, programming (Python is preferred), data intuition, and the ability to apply mathematical models to real-world problems.

How does Machine Learning compare to Artificial Intelligence in terms of applications?

Machine Learning is a subset of AI focused on systems that learn from data, while AI encompasses a broader range of technologies that simulate human intelligence. Machine Learning is more specific to data-driven algorithms.

What impact does Machine Learning have on the healthcare industry?

Machine Learning enhances diagnostic accuracy, optimizes treatment plans, and improves patient outcomes through predictive analytics and disease identification.

What are the 4 types of machine learning?

The four main types are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Is Python enough for machine learning?

Python is sufficient to start in Machine Learning due to its extensive libraries and frameworks, but understanding underlying algorithms and mathematics is crucial for advancing in the field.

Is machine learning still in demand?

Yes, Machine Learning continues to be in high demand as companies across various sectors rely on data-driven decisions for strategic planning and innovation.