Tack för att du skickade din fråga! En av våra teammedlemmar kontaktar dig snart.
Tack för att du skickade din bokning! En av våra teammedlemmar kontaktar dig snart.
Kursplan
Vardagligt arbete (på ett centraliserat sätt)
- Begår
- Bläddra
- Märkning och förgrening
- Sammanslagning
- Going fjärrkontroll
- Dela med dig av arbetet
Git-begrepp
- Git-objekt
- Distribuerade lagringsplatser
- Författare och Committers
- Sammanslagning av verk via e-post
- Sammanfoga verk från andra arkiv
- Bläckfisk sammanfogning
Vanor Migrationsprocess
- Spegling av omstörtande verksamhet
- Arbeta med Git på ett centraliserat sätt
- Växla till distribuerad modell
- Exempel på migreringsprocess (projekt med öppen källkod)
Git VVS och avancerade ämnen
- Git-kommandon
- Signaturer
- Ignorerar och exkluderar
Migrering och överbryggning
- Använda Git SVN-klon
- Arbeta med ett Subversion-arkiv och en Git-arbetskopia
Krav
Good kunskap om Subversion (SVN) krävs.
7 timmar
Vittnesmål (7)
```markdown# Introduction to Machine Learning## Course DescriptionMachine Learning is a field of study that gives computers the ability to learn without being explicitly programmed. This course will provide an introduction to the fundamental concepts and techniques in machine learning, including supervised and unsupervised learning, reinforcement learning, and deep learning. Students will gain hands-on experience with popular machine learning tools and frameworks such as Scikit-learn, TensorFlow, and Keras.## Learning ObjectivesBy the end of this course, students will be able to:- Understand the basic principles of machine learning.- Apply machine learning algorithms to real-world problems.- Evaluate the performance of machine learning models.- Use Python and relevant libraries for machine learning tasks.- Implement machine learning solutions using popular frameworks.## Course Outline### Module 1: Introduction to Machine Learning- Definition and history of machine learning.- Types of machine learning: supervised, unsupervised, and reinforcement learning.- Applications of machine learning in various industries.### Module 2: Supervised Learning- Introduction to supervised learning algorithms.- Linear regression and logistic regression.- Decision trees and random forests.- Support Vector Machines (SVM).### Module 3: Unsupervised Learning- Introduction to unsupervised learning algorithms.- Clustering techniques: K-means and hierarchical clustering.- Dimensionality reduction: Principal Component Analysis (PCA).- Association rule learning.### Module 4: Reinforcement Learning- Introduction to reinforcement learning.- Markov Decision Processes (MDP).- Q-learning and Deep Q-Networks (DQN).- Applications of reinforcement learning.### Module 5: Deep Learning- Introduction to deep learning.- Neural networks and deep neural networks.- Convolutional Neural Networks (CNN) for image processing.- Recurrent Neural Networks (RNN) for sequential data.- Transfer learning and pre-trained models.### Module 6: Model Evaluation and Selection- Techniques for evaluating machine learning models.- Cross-validation and train-test split.- Metrics for classification and regression problems.- Hyperparameter tuning.### Module 7: Practical Applications and Case Studies- Real-world applications of machine learning.- Case studies in healthcare, finance, and natural language processing.- Building end-to-end machine learning projects.- Deployment of machine learning models.### Module 8: Ethical Considerations in Machine Learning- Bias and fairness in machine learning.- Privacy and security concerns.- Ethical guidelines for responsible AI development.- Impact of machine learning on society.## Assessment- Midterm exam: 30% of the final grade.- Final project: 40% of the final grade.- Participation and assignments: 30% of the final grade.## Prerequisites- Basic understanding of Python programming.- Familiarity with statistics and linear algebra.- Completion of an introductory course in data science or a related field.```
Nicola - OHB System AG
Kurs - Git for Victims of Subversion
Maskintolkat
Övningar, när någon hade ett problem, visades det så att alla kunde lära sig av det.
Hania - OHB System AG
Kurs - Git for Victims of Subversion
Maskintolkat
Gav mig en god förståelse för skillnaderna mellan SVN och GIT.
Chris - Adder Technology Ltd
Kurs - Git for Victims of Subversion
Maskintolkat
Massor av övningar, tränare följde "flödet" av diskussioner/deltagarnas behov
Martin - OHB System AG
Kurs - Git for Victims of Subversion
Maskintolkat
A very good mix of theory and exercise
Olaf Horn - Wolfgang Metzner GmbH & Co. KG
Kurs - Git for Victims of Subversion
Luke was very personable and was willing to delve into specific examples of our issues.
Jim HABERLIN - Blume Global
Kurs - Git for Victims of Subversion
I was able to ask an expert questions that i have been trying figure out by Googling
