Mystro

Content

Session 1:¥ Intro to the course and AI history and Applications in real life
Session 1 Part 1
Session 1 Part 2
Session 1 Part 3
Session 1 Part 4
Session1:¥ Intro to the course and AI history and Applications in real life
Session 2 intro communication skills
Session 2 Part 1
Session 2 Part 2
Session 2 Part 3
Session 2 Part 4
Session 2 Part 5
Communication and Soft Skills Lec1 (6).pptx
Session 3: Pyhton Basic Syntax part 1(8 UE)
Session 3 Part 1
Session 3 Part 2
Session 3 Part 3
Session 3 Part 4
Session3: Python - Exercise 1
Session3: Introduction to Python and environment setup
Session 4 Communication and soft skills
Session 4 Part 1
Session 4 Part 2
Session 4 Part 3
Session 4 Part 4
cammunication skills lec2
Session 5: Pyhton Basic Syntax part 2 (8 UE)
Session 5 Part 1
Session 5 Part 2
Session 5 Part 3
Session 5 Part 4
Session5: Python conditional statements-Loops-Functions
Session 6: Control flow: conditionals and loops (8 UE)
Session 6 Part 1
Session 6 Part 2
Session 6 Part 3
Session 6 Part 4
Control flow: conditionals and loops (8 UE)
Session 7 Time Management
Session 7 Part 1
Session 7 Part 2
Session 7 Part 3
Time Management Lec1.pptx
Session 8: Functions and modules (8 UE)
Session 8 Part 1
Session 8 Part 2
Session 8 Part 3
Session 8 Part 4
Functions - Modules - File Operations
Session 9 time management lec2
Session 9 Part 1
Session 9 Part 2
Session 9 Part 3
Time Management Lec2.pptx
Session 10:Object-Oriented Programming basics (8 UE)
Session 10 Part 1
Session 10 Part 2
Session 10 Part 3
Session 10 Part 4
Object-Oriented Programming basics (8 UE)
Session 11 Agile lec1
Session 11 Part 1
Session 11 Part 2
Session 11 Part 3
Session 11 Part 4
Agile Lecture 1 (1) (1).pptx
Session 12:Data structures: lists, tuples, dictionaries (8 UE)
Session 12 Part 1
Session 12 Part 2
Session 12 Part 3
Session 12 Part 4
Data structures: lists, tuples, dictionaries (8 UE)
Session 13 Agile lec2
Session 13 part 1
Session 13 Part 2
Session 13 Part 3
Session 13 Part 4
Agile Lecture 2 (1).pptx
Session 14 Advanced data structures: sets, stacks, queues (8 UE)
Session 14 Part 1
Session 14 Part 2
Session 14 Part 3
Advanced data structures: sets, stacks, queues (8 UE)
Session 15: Basic algorithms: sorting and searching (8 UE)
Session 15 Part 2
Session 15 Part 3
Session 15 Part 4
session 15 Basic Algorithms: Sorting Algorithms - Searching Algorithms
Session 16 Agile
Session 16 Part 1
Session 16 Part 2
Session 16 Part 3
Session 16 Part 4
Agile Lecture 3.pptx
Session 17: Problem-solving techniques (8 UE)
Session 17 Part 1
Session 17 Part 2
Session 17 Part 3
Session 17 Part 4
Session17 Problem-solving techniques (8 UE)
Problem-solving techniques
Session 18 Agile
Session 18 part1
Session 18 Part 2
Session 18 Part 3
Session 18 Part 4
Agile Lecture 4.pptx
Session 19: Automation and working with external APIs (8 UE)
Session 19 Part 1
Session 19 Part 2
Session 19 Part 3
Session 19 Part 4
Automation and working with external APIs (8 UE)
Session 20:Probability basics (8 UE)
Session 20 Part 1
Session 20 Part 2
Session 20 Part 3
Session 20 Part 4
Probability basics (8 UE)
Session 21Agile
Session 21 Part 1
Session 21 Part 2
Session 21 Part 3
Session 21 Part 4
Agile
Session 22:Statistics fundamentals (8 UE)
Session 22 Part 1
Session 22 Part 2
Session 22 Part 3
Session 22 Part 4
Statistics fundamentals (8 UE)
Session 23:Agile
Session 23 Part 1
Session 23 Part 2
Session 23 Part 3
Session 23 Part 4
Agile Lecture 6.pptx
Session 24:Linear algebra introduction (8 UE)
Session 24 Part 1
Session 24 Part 2
Session 24 Part 3
Session 24 Part 4
Linear algebra introduction (8 UE)
Session 25: Advanced linear algebra (8 UE)
Session 25 Part 1
Session 25 Part 2
Session 25 Part 3
Session 25 Part 4
Advanced linear algebra
Advanced linear algebra (8 UE)
Advanced linear algebra (8 UE)
Session 26 AGILE 7
Session 26 Part 1
Session 26 Part 2 sample
Session 26 Part 3
Session 26 Part 4
Session 27:Quiz
Session 27 Part 1
Session 27 Part 2
Session 27 Part 3
Session 27 Part 4
Quiz
session 28 :Project management tools and techniques
Session 28 Part 1
Session 28 Part 2
Session 28 Part 3
Session 28 Part 4
Agile
Session 29 Introduction to Numpy and data manipulation (8 UE)
Session 29 Part 1
Session 29 Part 2
Session 29 Part 3
Session 29 Part 4
Introduction to Numpy and data manipulation (8 UE)
Numpy and data manipulation (8 UE)
INumpy and data manipulation (8 UE) _Exercise_2
Exercise 2 Numpy and data manipulation (8 UE)
Exercise: Numpy and data manipulation (8 UE)
session 30 :Pandas for data analysis (8 UE)
Session 30 Part 1
Session 30 Part 2
Session 30 Part 3
Session 30 Part 4
Pandas for data analysis (8 UE)
Pandas for data analysis (8 UE)
Pandas for data analysis (8 UE) Exercise
Session 31 Agile
Session 31 Part 1
Session 31 Part 2
Session 31 Part 3
Session 31 Part 4
Session 32 Data visualization with Matplotlib and Seaborn (8 UE)
Session 32 Part 1
Session 32 Part 2
Session 32 Part 3
Session 32 Part 4
Data visualization with Matplotlib and Seaborn (8 UE)
Data visualization with Matplotlib and Seaborn Exercise
Session 33 Project Management
Session 33 Part 1
Session 33 Part 2
Session 33 Part 3
Session 33 Part 4
Project management tools and techniques
Session 34 : Exploratory Data Analysis (EDA) techniques (8 UE)
Session 34 Part 1
Session 34 Part 2
Session 34 Part 3
Session 34 Part 4
Exploratory Data Analysis (EDA) techniques (8 UE)
StExploratory Data Analysis (EDA) techniques
Exploratory Data Analysis (EDA) techniques (
Session 35:Data analysis project (8 UE)
Session 35 Part 1
Session 35 Part 2
Session 35 Part 3
Session 35 Part 4
Session 36 Project Management
Session 36 Part 1
Session 36 Part 2
Session 36 Part 3
Session 36 Part 4
Project management tools and techniques
Session 37: Quiz
Session 37 Part 1
Session 37 Part 2
Session 37 Part 3
Session 37 Part 4
Session 37 Part 5
Quiz
Session 38 project managment
Session 38 Part 1
Session 38 Part 2
Session 38 Part 3
Session 38 Part 4
Project Management
Session 39: Supervised learning techniques: regression and classification (8 UE)
Session 39 Part 1
Session 39 Part 2
Session 39 Part 3
Session 39 Part 4
Supervised learning techniques: regression and classification (8 UE)
Session 40:Unsupervised learning techniques: clustering and association (8 UE)
Session 40 Part 1
Session 40 Part 2
Session 40 Part 3
Session 40 Part 4
Session 41 Project management tools and techniques
Session 41 Part 1
Session 41 Part 2
Session 41 Part 3
Session 41 Part 4
Project Management
Session 42: Natural Language Processing (NLP) basics (8 UE)
Session 42 Part 1
Session 42 Part 2
Session 42 Part 3
Session 42 Part 4
Natural Language Processing (NLP) basics (8 UE)
Natural Language Processing (NLP) basics (8 UE)
Natural Language Processing (NLP) basics (8 UE)
Session 43Project management tools and techniques
Session 43 Part 1
Session 43 Part 2
Session 43 Part 3
Session 43 Part 4
Project management tools and techniques
Session 44: Machine learning project (8 UE)
Session 44 Part 1
Session 44 Part 2
Session 44 Part 3
Session 44 Part 4
Machine learning project (8 UE)
Machine learning project (8 UE)
Machine learning project (8 UE)
Session 45:Neural networks and deep learning concepts (8 UE)
Session 45 Part 1
Session 45 Part 2
Session 45 Part 3
Session 45 Part 4
Session 46 Design Thinking
Session 46 Part 1
Session 46 Part 2
Session 46 Part 3
Session 46 Part 4
Design Thinking
Session 47:Keras and TensorFlow basics (8 UE)
Session 47 Part 1
Session 47 Part 2
Session 47 Part 3
Session 47 Part 4
Session 48 Design Thinking
Session 48 Part 1
Session 48 Part 2
Session 48 Part 3
Session 48 Part 4
Design Thinking
Session 49: Advanced TensorFlow (8 UE)
Advanced TensorFlow (8 UE)
Advanced TensorFlow (8 UE)
Ensemble Techniques
Session 49 Part 1
Session 49 Part 2
Session 49 Part 3
Session 49 Part 4
Session 50: Convolutional Neural Networks (CNNs) (8 UE)
Convolutional Neural Networks (CNNs) (8 UE)
Convolutional Neural Networks (CNNs) (8 UE)
Convolutional Neural Networks (CNNs) (8 UE)
Session 50 Part 1
Session 50 Part 2
Session 50 Part 3
Session 50 Part 4
Session 51 Design thinking
Session 51 Part 1
Session 51 Part 2
Session 51 Part 3
Session 51 Part 4
Design Thinking
Session 52: Recurrent Neural Networks (RNNs) (8 UE)
Session 52 Part 1
Session 52 Part 2
Session 52 Part 3
Session 52 Part 4
Recurrent Neural Networks (RNNs) (8 UE)
Principal Component Analysis - Liner Discriminant Analysis
Session 53 Design thinking
Session 53 Part 1
Session 53 Part 2
Session 53 Part 3
Session 53 Part 4
Design Thinking
Session 54:Long Short-Term Memory (LSTM) networks (8 UE)
Session 54 Part 1
Session 54 Part 2
Session 55: Transfer learning (8 UE)
Transfer learning (8 UE)
Session 55 Part 1
Session 55 Part 2
Session 55 Part 3
Session 55 Part 4
Session 56 creative thinking
Session 56 Part 1
Session 56 Part 2
Session 56 Part 3
creative thinking
Session 57: Deep learning project (8 UE)
Session 57 Part 1
Session 57 Part 3
Deep learning project (8 UE)
Deep learning project (8 UE)
Deep learning project (8 UE)
Introduction to Databases - Basic SQL Quesries (1)
Session 58 creative thinking
Session 58 Part 1
Session 58 Part 2
Session 58 Part 3
creative thinking
Session 59: Introduction to Generative AI (8 UE)
Session 59 Part 1
Session 59 Part 2
Session 59 Part 3
Session 59 Part 4
Introduction to Generative AI (8 UE)
Introduction to Generative AI (8 UE)
Session 60: Image generation with Img2Img (8 UE)
Session 60 Part 1
Session 60 Part 2
Session 60 Part 3
Session 60 Part 4
Image generation with Img2Img (8 UE)
exercise
Session 61.Basic German for Tech Professionals
Session 61 Part 1
Session 61 Part 2
Session 61 Part 3
Session 61 Part 4
Session 62: Multi-model integration (8 UE
Session 62 Part 3
Multi-model integration (8 UE
Multi-model integration (8 UE
Session 63.Basic German for Tech Professionals
Session 63 Part 1
Session 63 Part 2
Session 63 Part 3
Session 63 Part 4
Session 64: Large Language Models (LLMs) (8 UE)
Session 64 Part 1
Session 64 Part 2
Session 64 Part 3
Large Language Models (LLMs) (8 UE)
Large Language Models (LLMs) (8 UE)
Session 65.Fine-tuning models (8 UE)
Session 65 Part 1
Session 65 Part 2
Session 65 Part 3
Session 65 Part 4
Session 66:OpenAI tools and applications (8 UE)
Session 66 Part 1
Session 66 Part 2
Session 66 Part 3
Session 66 Part 4
db OpenAI tools and applications (8 UE)
Fine-tuning models (8 UE)
Session 67 Intermediate German for Tech professionals
Session 67 Part 1
Session 67 Part 2
Session 67 Part 3
Session 67 Part 4
Quiz 3 - Deutsch
Session 68:Langchain and llamaIndex (8 UE)
Session 68 Part 1
Session 68 Part 2
Session 68 Part 3
Session 68 Part 4
Langchain and llamaIndex (8 UE)
DB and excrise
Session 69:Vector databases (8 UE)
Session 69 Part 1
Session 69 Part 2
Session 69 Part 3
Session 69 Part 4
Vector databases (8 UE)
Vector databases (8 UE)
Session 70 Intermediate German for Tech professionals
Session 70 Part 1
Session 70 Part 2
Session 70 Part 3
Session 70 Part 4
Intermediate German for Tech professionals
Quiz 3 - Deutsch
Session 71:Generative AI project (8 UE)
Session 71 Part 1
Session 71 Part 2
Session 71 Part 3
Session 71 Part 4
Generative AI project (8 UE)
Session 72 Intermediate German for Tech Professionals
Session 72 Part 1
Session 72 Part 2
Session 72 Part 3
Session 72 Part 4
Quiz 5 - Deutsch
Session 73 Quiz
Session 73 Part 2
Session 73 Part 3
Session 73 Part 4
Quiz
EX; Generative AI project (8 UE)
Session 74:Introduction to MLOps (8 UE)
Session 74 Part 1
Session 74 Part 2
Session 74 Part 3
Session 74 Part 4
Introduction to MLOps (8 UE)
Introduction to MLOps (8 UE)
Session 75 Intermediate German for Tech professionals - Unterricht5
Session 75 Part 1
Session 75 Part 2
Session 75 Part 3
Session 75 Part 4
Intermediate German for Tech professionals
Session 76: IVersion control with Git and GitHub (8 UE)
Session 76 Part 1
Session 76 Part 2
Session 76 Part 3
Session 76 Part 4
Version control with Git and GitHub (8 UE)
Session 77 Intermediate German for Tech professionals
Session 77 Part 1
Session 77 Part 2
Session 77 Part 3
Session 77 Part 4
Quiz 6 - Deutsch
Intermediate German for Tech professionals
Session 78:Containerization with Docker (8 UE)
Session 78 Part 1
Session 78 Part 2
Session 78 Part 3
Session 78 Part 4
Containerization with Docker (8 UE)
Version control with Git and GitHub (8 UE)
Session 79: Frontend basics (HTML, CSS, Bootstrap) (8 UE)
Session 79 Part 1
Session 79 Part 2
Session 79 Part 3
Session 79 Part 4
Frontend basics (HTML, CSS, Bootstrap) (8 UE)
Session 80German for job applications and CV writing
Session 80 Part 1
Session 80 Part 2
Session 80 Part 3
Session 80 Part 4
German for job applications and CV writing
Session 81:Backend development with FastAPI (8 UE)
Session 81 Part 1
Session 81 Part 2
Session 81 Part 3
Session 81 Part 4
Session 82 German for job applications and CV writing -
Session 82 Part 1
Session 82 Part 2
Session 82 Part 3
Session 82 Part 4
German for job applications and CV writing
Session 83 Resume and cover letter workshops
Session 83 Part 1
Session 83 Part 2
Session 83 Part 3
Session 83 Part 4
Resume and cover letter workshops
Session 84:Flask for web applications (8 UE)
Session 84 Part 1
Session 84 Part 2
Session 84 Part 3
Session 84 Part 4
Flask for web applications (8 UE)
Session 85 Resume and cover letter workshops
Session 85 Part 1
Session 85 Part 2
Session 85 Part 3
Resume and cover letter workshops
Session 86: Streamlit for data apps (8 UE)
Session 86 Part 1
Session 86 Part 2
Session 86 Part 3
Session 86 Part 4
Flask for web applications (8 UE)
Flask for web applications (8 UE)
Session 87: MlFlow for model management (8 UE)
Session 87 Part 1
Session 87 Part 2
Session 87 Part 3
Session 88 Resume and cover letter workshops
Session 88 Part 1
Session 88 Part 2
Session 88 Part 3
Resume and cover letter workshops
Session 89 Monitoring and debugging ML systems (8 UE)
Variations of RNN
Session 89 Part 1
Session 89 Part 2
Session 89 Part 3
Session 89 Part 4
Session 90:CI/CD pipelines (8 UE)
Monitoring and debugging ML systems (8 UE)
Session 90 Part 1
Session 90 Part 3
Session 90 Part 4
Session 90 Part 2
Session 91Production deployment (8 UE)
Session 91 Part 1
Session 91 Part 2
Session 91 Part 3
Session 91 Part 4
CI/CD pipelines (8 UE)
CI/CD pipelines (8 UE)
Session 92 Resume and cover letter workshops
Session 92 Part 1
Session 92 Part 2
Session 92 Part 3
Session 92 Part 4
Resume and cover letter workshops
Session 93:Azure AI Studio (8 UE
Session 93 Part 1
Session 93 Part 2
Session 93 Part 3
Production deployment (8 UE)
Session 94 German language for work integration (8 UE)
Session 94 Part 1
Session 94 Part 2
Session 94 Part 3
Session 94 Part 4
German for job applications and CV writing
Session 95:Amazon Bedrock (8 UE)
Session 95 Part 1
Session 95 Part 2
Session 95 Part 3
Session 95 Part 4
Azure AI Studio (8 UE)
Session 96:MLOps project (8 UE)
Session 96 Part 1
Session 96 Part 2
Session 96 Part 3
Session 96 Part 4
Amazon Bedrock (8 UE)
data sec 96
Session 97 German for job applications and CV writing
Session 97 Part 1
Session 97 Part 2
Session 97 Part 3
Session 97 Part 4
excrises 97
Session 98Transformer9s introduction (8 UE)
Session 98 Part 1
Session 98 Part 2
Transformer9s introduction (8 UE)
data 98
Session 99 German for job applications and CV writing
Session 99 Part 1
Session 99 Part 2
Session 99 Part 3
Session 99 Part 4
Session 100BERT and other transformer models (8 UE)
Session 100 Part 1
Session 100 Part 2
Session 100 Part 3
Session 100 Part 4
BERT and other transformer models (8 UE)
Session 101:Advanced NLP techniques (8 UE)
Session 101 Part 1
Session 101 Part 2
Session 101 Part 3
Session 101 Part 4
BERT and other transformer models (8 UE)
Session 102 German for job applications and CV writing
Session 102 Part 1
Session 102 Part 2
Session 103: Computer vision applications (8 UE)
Session 103 Part 1
Session 103 Part 2
Session 103 Part 3
Session 103 Part 4
db s 103
Session 104 Advanced computer vision techniques (8 UE)
Session 104 Part 1
Session 104 Part 2
Session 104 Part 3
Session 104 Part 4
Session 105: German for job applications and CV writing -
Session 105 Part 1
Session 105 Part 2
Session 105 Part 3
Session 105 Part 4
German for job applications and CV writing -
db s 105
Session 106:AI ethics and bias (8 UE)
Session 106 Part 1
Session 106 Part 2
Session 106 Part 3
Session 106 Part 4
Session 106 Part 5
AI ethics and bias (8 UE)
db s 106
Session 107:AI security (8 UE)
Session 107 Part 1
Session 107 Part 2
Session 107 Part 3
Session 107 Part 4
db s 107
AI security (8 UE)
AI security (8 UE)
Interview skills and job search strategies
Session 108 Part 1
Session 108 Part 2
Session 109: Quiz
Session 109 Part 1
Session 109 Part 2
Session 109 Part 3
Session 109 Part 4
Quiz
db s 109
Session 110Project ideation and planning (8 UE)
Session 110 Part 1
Session 110 Part 2
Session 110 Part 3
Session 110 Part 4
Project ideation and planning (8 UE)
db s 110
Session 111 Interview skills and job search strategies
Session 112: Data collection and preprocessing (8 UE)
Session 112 Part 1
Session 112 Part 2
Session 112 Part 3
session 113 .Interview skills and job search strategies
.Interview skills and job search strategies
Session 114 Building and training models (8 UE)
Building and training models (8 UE)
Session 114 Part 1
Session 114Part 2
Session 114 Part 3
Session 114 Part 4
Building and training models (8 UE)
session 115 .Interview skills and job search strategies
.Interview skills and job search strategies
Session 116:Model and refinement (8 UE)
Session 116 Part 1
Session 116 Part 2
Session 116 Part 3
Session 116 Part 4
session 117 Interview skills and job search strategies
Session 117
session 118Frontend and backend integration (8 UE)
Session 118
Session 118
Session 118
Session 118
session 119 Final project presentations (8 UE)
Session 119
session 120 .Arbeiten mit BA Jobbörse BERUFNET und Profilerstellun
Session 120-part 1
Session 120-part 2
Session 121 Interview skills and job search strategies
Session 121 part 1
Session 121 part 2
Session 121 part 3
session 122Arbeiten mit BA Jobbörse BERUFNET und Profilerstellun
session 122
Arbeiten mit BA Jobbörse BERUFNET und Profilerstellun
session 123 Professional online presence and networking
Session 123
session 124 Professional online presence and networking
Session 124
Professional online presence and networking
session 125 :Rückblick Maßnahme und Beendigung

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Course info

AI (KI) Development, KI Manager Weiterbildung V.5

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