К содержимому
learnspaceYOUR NEXT CHAPTER
ПРОСТРАНСТВО ОБУЧЕНИЯ
ГлавнаяКаталог курсовМоё обучениеCoursera

Знания без границ

Учитесь у лучших университетов и компаний мира.

Открыть Coursera
Интеграция
Пространство университета
Моё пространствоСтраница курса
↵
ЯЛичный кабинетСтудент
© 2026 LearnSpaceКаждый день — возможность узнать больше.Помощь
Python Machine Learning By Example · LearnSpace
Назад в каталог
courseraАнализ данных

Python Machine Learning By Example

Курс от Packt
Средний≈ 32.4 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Machine learning is one of the most sought-after skills in today’s data-driven world, and this course provides the perfect balance between theory and application. You’ll explore how Python can be leveraged to build, evaluate, and deploy machine learning models effectively across various domains. Through this course, you’ll gain hands-on experience with practical tools and techniques to improve your ability to design, train, and optimize predictive models. You’ll learn how to apply advanced methods in areas such as deep learning, computer vision, and natural language processing to achieve measurable, real-world outcomes. What sets this course apart is its focus on bridging theoretical foundations with practical, implementation-based exercises. You’ll work on real-world case studies using TensorFlow and PyTorch, ensuring that the skills you acquire are immediately applicable in professional settings. This course is ideal for data scientists, ML engineers, and Python developers aiming to strengthen their expertise in applied machine learning. A working knowledge of Python and basic data analysis concepts will help you get the most out of this course.

Навыки, которые вы освоите

Reinforcement LearningModel EvaluationPyTorch (Machine Learning Library)Model TrainingPython ProgrammingLarge Language ModelingApplied Machine LearningModel OptimizationConvolutional Neural NetworksData PreprocessingMachine LearningTensorflowNatural Language ProcessingMachine Learning AlgorithmsArtificial Intelligence and Machine Learning (AI/ML)Transfer LearningImage AnalysisComputer VisionDeep LearningMachine Learning Methods

Программа курса

15 модулей · 140 учебных материалов

01Getting Started with Machine Learning and Python15 материалов

Lesson 1

Course OverviewВидеоGetting Started with Machine Learning and Python - Overview VideoВидеоIntroductionЧтениеMachine Learning ApplicationsЧтение

Учитесь у экспертов

Packt - Course Instructors

Преподаватель курса

Python Machine Learning By Example
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 32.4 ч

15 модулей

Язык: Английский

Часть программы вашего университета
A Brief History of the Development of Machine Learning AlgorithmsЧтение
OverfittingЧтение
The Bias-Variance Trade-OffЧтение
Avoiding Overfitting with Cross-ValidationЧтение
Avoiding Overfitting with RegularizationЧтение
Data Preprocessing and Feature EngineeringЧтение
One-hot EncodingЧтение
Combining ModelsЧтение
Setting Up Python and EnvironmentsЧтение
TensorFlowЧтение
Introduction to Machine Learning FundamentalsЗадание
02Building a Movie Recommendation Engine with Naïve Bayes9 материалов

Lesson 1

Building a Movie Recommendation Engine with Naïve Bayes - Overview VideoВидеоIntroductionЧтениеExploring Naïve BayesЧтениеThe Mechanics of Naïve BayesЧтениеImplementing Naïve Bayes from ScratchЧтениеBuilding a Movie Recommender with Naïve BayesЧтениеTraining a Naïve Bayes ModelЧтениеTuning Models with Cross-ValidationЧтениеMovie Recommendation System FundamentalsЗадание
03Predicting Online Ad Click-Through with Tree-Based Algorithms7 материалов

Lesson 1

Predicting Online Ad Click-Through with Tree-Based Algorithms - Overview VideoВидеоIntroductionЧтениеGini ImpurityЧтениеImplementing a Decision Tree from ScratchЧтениеImplementing a Decision Tree with Scikit-learnЧтениеEnsembling Decision Trees Random ForestsЧтениеTree-Based Algorithms in Ad Click PredictionЗадание
04Predicting Online Ad Click-Through with Logistic Regression10 материалов

Lesson 1

Predicting Online Ad Click-Through with Logistic Regression - Overview VideoВидеоIntroductionЧтениеJumping from the Logistic Function to Logistic RegressionЧтениеTraining a Logistic Regression Model Using Gradient DescentЧтениеPredicting Ad Click-Through with Logistic Regression Using Gradient DescentЧтениеTraining a Logistic Regression Model with RegularizationЧтениеTraining on Large Datasets with Online LearningЧтениеHandling Multiclass ClassificationЧтениеImplementing Logistic Regression Using TensorFlowЧтениеLogistic Regression and Feature Engineering FundamentalsЗадание
05Predicting Stock Prices with Regression Algorithms9 материалов

Lesson 1

Predicting Stock Prices with Regression Algorithms - Overview VideoВидеоIntroductionЧтениеGetting Started with Feature EngineeringЧтениеAcquiring Data and Generating FeaturesЧтениеHow Does Linear Regression Work?ЧтениеImplementing Linear Regression with Scikit-learnЧтениеImplementing Decision Tree RegressionЧтениеImplementing a Regression ForestЧтениеRegression Techniques in Financial ForecastingЗадание
06Predicting Stock Prices with Artificial Neural Networks8 материалов

Lesson 1

Predicting Stock Prices with Artificial Neural Networks - Overview VideoВидеоIntroductionЧтениеBackpropagationЧтениеImplementing Neural Networks from ScratchЧтениеImplementing Neural Networks with PyTorchЧтениеEarly StoppingЧтениеFine-tuning the Neural NetworkЧтениеNeural Networks in Financial ForecastingЗадание
07Mining the 20 Newsgroups Dataset with Text Analysis Techniques12 материалов

Lesson 1

Mining the 20 Newsgroups Dataset with Text Analysis Techniques - Overview VideoВидеоIntroductionЧтениеNLP ApplicationsЧтениеCorporaЧтениеNERЧтениеGetting the Newsgroups DataЧтениеExploring the Newsgroups DataЧтениеCounting the Occurrence of Each Word TokenЧтениеReducing Inflectional and Derivational Forms of WordsЧтениеt-SNE for Dimensionality ReductionЧтениеBuilding Embedding Models Using Shallow Neural NetworksЧтениеExploring Text Analysis with the 20 Newsgroups DatasetЗадание
08Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling9 материалов

Lesson 1

Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling - Overview VideoВидеоIntroductionЧтениеGetting Started with K-Means ClusteringЧтениеImplementing k-Means with scikit-learnЧтениеClustering Newsgroups Data Using K-MeansЧтениеDescribing the Clusters Using GPTЧтениеDiscovering Underlying Topics in NewsgroupsЧтениеTopic Modeling Using LDAЧтениеExploring Text Data Analysis TechniquesЗадание
09Recognizing Faces with Support Vector Machine7 материалов

Lesson 1

Recognizing Faces with Support Vector Machine - Overview VideoВидеоIntroductionЧтениеHandling OutliersЧтениеMulticlass Cases in Scikit-learnЧтениеChoosing Between Linear and RBF KernelsЧтениеBuilding an SVM-Based Image ClassifierЧтениеExploring SVM Techniques and ApplicationsЗадание
10Machine Learning Best Practices10 материалов

Lesson 1

Machine Learning Best Practices - Overview VideoВидеоIntroductionЧтениеBest Practice 4 Dealing with Missing DataЧтениеBest practice 5 – Storing large-scale dataЧтениеBest Practice 10 Deciding Whether to Rescale FeaturesЧтениеTF and TF-IDFЧтениеBest practices in the model training, evaluation, and selection stageЧтениеBest Practice Reducing OverfittingЧтениеSaving and Restoring Models Using PickleЧтениеMachine Learning Data Preparation EssentialsЗадание
11Categorizing Images of Clothing with Convolutional Neural Networks7 материалов

Lesson 1

Categorizing Images of Clothing with Convolutional Neural Networks - Overview VideoВидеоIntroductionЧтениеThe Pooling LayerЧтениеClassifying Clothing Images with CNNsЧтениеFitting the CNN ModelЧтениеRotation for Data AugmentationЧтениеExploring Convolutional Neural Networks for Clothing Image ClassificationЗадание
12Making Predictions with Sequences Using Recurrent Neural Networks9 материалов

Lesson 1

Making Predictions with Sequences Using Recurrent Neural Networks - Overview VideoВидеоIntroductionЧтениеOne-to-many RNNsЧтениеAnalyzing and Preprocessing the DataЧтениеBuilding a Simple LSTM NetworkЧтениеRevisiting Stock Price Forecasting with LSTMЧтениеWriting Your Own War and Peace with RNNsЧтениеBuilding and Training an RNN Text GeneratorЧтениеExploring Sequence Modeling with RNNsЗадание
13Advancing Language Understanding and Generation with the Transformer Models9 материалов

Lesson 1

Advancing Language Understanding and Generation with the Transformer Models - Overview VideoВидеоIntroductionЧтениеAttention Score Calculation and Embedding Vector GenerationЧтениеMulti-head AttentionЧтениеPositional EncodingЧтениеFine-tuning a Pre-trained BERT Model for Sentiment AnalysisЧтениеUsing the Trainer API to Train Transformer ModelsЧтениеWriting Your Own Version of War and Peace with GPTЧтениеExploring Transformer Architecture and ApplicationsЗадание
14Building an Image Search Engine Using CLIP a Multimodal Approach9 материалов

Lesson 1

Building an Image Search Engine Using CLIP a Multimodal Approach - Overview VideoВидеоIntroductionЧтениеZero-shot Image ClassificationЧтениеGetting Started with the DatasetЧтениеVision EncoderЧтениеCLIP ModelЧтениеObtaining Embeddings for Images and Text to Identify MatchesЧтениеZero-shot ClassificationЧтениеMultimodal Models in Image SearchЗадание
15Making Decisions in Complex Environments with Reinforcement Learning10 материалов

Lesson 1

Making Decisions in Complex Environments with Reinforcement Learning - Overview VideoВидеоIntroductionЧтениеCumulative RewardsЧтениеSimulating the FrozenLake EnvironmentЧтениеSolving FrozenLake with the Value Iteration AlgorithmЧтениеSolving FrozenLake with the Policy Iteration AlgorithmЧтениеSimulating the Blackjack EnvironmentЧтениеPerforming On-Policy Monte Carlo ControlЧтениеIntroducing the Q-Learning AlgorithmЧтениеReinforcement Learning FundamentalsЗадание