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Algorithm Alchemy: Unlocking the Secrets of Machine Learning · LearnSpace
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Algorithm Alchemy: Unlocking the Secrets of Machine Learning

Курс от Packt
Средний≈ 5.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will unlock the secrets of machine learning algorithms, learning how to implement them in Python to tackle real-world data problems. You'll explore both supervised and unsupervised learning algorithms, gaining practical experience with techniques such as linear regression, decision trees, and deep learning models. With each lesson, you will build your skill set to apply machine learning methods effectively, from basic models to advanced techniques. The journey begins with foundational concepts of machine learning and progresses through practical implementation of various algorithms. You will cover supervised methods like linear regression, KNN, and support vector machines, as well as unsupervised learning techniques such as K-Means clustering, PCA, and autoencoders. Each section provides hands-on coding experiences, giving you the confidence to apply these methods in real-world scenarios. The course also delves into advanced topics such as deep reinforcement learning, convolutional and recurrent neural networks, and transformer models. These cutting-edge techniques will help you build powerful predictive models, perform anomaly detection, and solve complex tasks across various domains. This course is ideal for individuals looking to deepen their knowledge of machine learning algorithms and how to implement them using Python. It is suitable for aspiring data scientists, machine learning engineers, and anyone with a strong interest in machine learning and AI. Prior programming knowledge in Python is recommended, and a basic understanding of statistics will help in grasping the concepts more effectively. The course is intermediate in difficulty, designed to challenge learners who are ready to dive deeper into the world of machine learning.

Навыки, которые вы освоите

Recurrent Neural Networks (RNNs)Machine Learning MethodsScientific VisualizationClassification AlgorithmsAutoencoders

Программа курса

4 модулей · 35 учебных материалов

01Introduction to Machine Learning Algorithms and Implementation in Python1 материалов

Introduction to Machine Learning Algorithms and Implementation in Python

Introduction to Machine Learning Algorithms and Implementation in PythonВидео
02Supervised Learning Algorithms12 материалов

Supervised Learning Algorithms

Linear Regression Implementation in PythonВидеоRidge and Lasso Regression Implementation in PythonВидео

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Packt - Course Instructors

Преподаватель курса

Algorithm Alchemy: Unlocking the Secrets of Machine Learning
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 5.7 ч

4 модулей

Язык: Английский

Субтитры: Казахский

Часть программы вашего университета
Polynomial Regression Implementation in PythonВидео
Logistic Regression Implementation in PythonВидео
K-Nearest Neighbors (KNN) Implementation in PythonВидео
Support Vector Machines (SVM) Implementation in PythonВидео
Decision Trees Implementation in PythonВидео
Random Forests Implementation in PythonВидео
Gradient Boosting Implementation in PythonВидео
Naive Bayes Implementation in PythonВидео
Predicting with Linear RegressionDIALOGUE
Supervised Learning Algorithms - AssessmentЗадание
03Unsupervised Learning Algorithms9 материалов

Unsupervised Learning Algorithms

K-Means Clustering Implementation in PythonВидеоHierarchical Clustering Implementation in PythonВидеоDBSCAN (Density-Based Spatial Clustering of Applications with Noise)ВидеоGaussian Mixture Models (GMM) Implementation in PythonВидеоPrincipal Component Analysis (PCA) Implementation in PythonВидеоt-Distributed Stochastic Neighbor Embedding (t-SNE) Implementation in PythonВидеоAutoencoders Implementation in PythonВидеоApplying K-Means ClusteringDIALOGUEUnsupervised Learning Algorithms - AssessmentЗадание
04Other Specialized Categories13 материалов

Other Specialized Categories

Self-Training Implementation in PythonВидеоQ-Learning Implementation in PythonВидеоDeep Q-Networks (DQN) Implementation in PythonВидеоPolicy Gradient Methods Implementation in PythonВидеоOne-Class SVM Implementation in PythonВидеоIsolation Forest Implementation in PythonВидеоConvolutional Neural Networks (CNNs) Implementation in PythonВидеоRecurrent Neural Networks (RNNs) Implementation in PythonВидеоLong Short-Term Memory (LSTM) Implementation in PythonВидеоTransformers Implementation in PythonВидеоOther Specialized Categories - AssessmentЗаданиеFull Course Practice AssessmentЗаданиеFull Course Assessment Задание