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Programming

AI (KI) Development, KI Manager Weiterbildung V.4


Content
  • Intro to the course and AI history and Applications in real life
  • Introduction to AI - Mystro.pptx
  • seesion1 Part 1
  • Session 1 Part 2
  • Session 1 Part 3
  • Session 1 Part 4
  • Session 1 Part 5
  • Communication and soft skills
  • Communication and Soft Skills Lec1.pptx
  • Session 2 Part 1
  • Session 2 Part 2
  • Session 2 Part 3
  • Session 2 Part 4
  • Introduction to Python and environment setup
  • Py1_Data&Control.pptx
  • Session 3 Part 1
  • Session 3 Part 2
  • Py-T01.pdf
  • Session 3 Part 3
  • Session 3 Part 4
  • Communication and soft skills
  • Communication and Soft Skills Lec2.pptx
  • Session 4 Part 1
  • Session 4 Part 2
  • Session 4 Part 3
  • test1 commenication
  • Python syntax, variables, and data types
  • Session 5 Part 1
  • Session 5 Part 2
  • Session 5 Part 3
  • Session 5 Part 4
  • Session 6 If statement For loop While Loop
  • Control flow: conditionals and loop
  • Session 6 Part 2
  • python03-task.pdf
  • W02-S06-Python3.pdf
  • Session 6 Part 3
  • Session 6 Part 4
  • Time management and productivity
  • Session 7 Part 1
  • Session 7 Part 2
  • Session 7 Part 3
  • Session 7 Part 4
  • Communication Skills Test
  • Time Management Lec1.pptx
  • Functions and modules
  • Session 8 Part 1
  • Session 8 Part 2
  • List of Tasks on python Lists.pdf
  • Session 8 Part 3
  • Session 8 Part 4
  • Time management and productivity
  • Session 9 Part 1
  • Session 9 Part 2
  • Session 9 Part 3
  • Session 9 Part 4
  • Time Management Lec2.pptx
  • Python Loops Recap on Functions Python Lists Data Structure
  • Session 10 Part 1
  • Session 10 Part 2
  • Session 10 Part 3
  • Session 10 Part 4
  • Session 11 Data structures: lists, tuples, dictionaries
  • Session 11 Part 1
  • Session 11 Part 2
  • Session 11 Part 3
  • Session 11 Part 4
  • Session 12 Agile1
  • Session 12 Part 1
  • Session 12 Part 2
  • Session 12 Part 3
  • Agile Lecture 1 (1).pptx
  • Time Management and Productivity Exam
  • Session 12 Part 4
  • Session 13 Intoduction to OOP with Python Python Classes and Objects
  • Session 13 Part 1
  • Python ex1.pdf
  • Session 13 Part 2
  • Session 13 Part 3
  • Session 13 Part 4
  • Session 14 agile 2
  • Session 14 Part 1
  • Session 14 Part 2
  • Agile Lecture 2.pptx
  • Session 15 OOP Project 1 OOP Project 2 , Simple Dice Game
  • OOP_Task1.pdf
  • Session 15 Part 1
  • Session 15 Part 2
  • OOP_Task2
  • Session 15 Part 3
  • W04-S15-OOP2.pdf
  • OOP_Task2.pdf
  • Session 15 Part 4
  • Session 16 agile3
  • Session 16 Part 1
  • Session 16 Part 2
  • Session 16 Part 3
  • Session 16 Part 4
  • Agile Lecture 3.pptx
  • Session 17 Lambda Function Map , Filter , Reduce Stack and Queue
  • Session 17 Part 1
  • Session 17 Part 2
  • Session 17 Part 3
  • Session 17 Part 4
  • Session 18 agile 4
  • Session 18 Part 1
  • Session 18 Part 2
  • Agile Lecture 4.pdf
  • Session 18 Part 3
  • Session 18 Part 4
  • Agile Lecture 4.pdf
  • Agile Test 1
  • Session 19 Error Handling& Exeption Library Problem Solving
  • Session 19 Part 1
  • Session 19 Part 2
  • Session 19 Part 3
  • Session 19 Part 4
  • Session 20 Inroduction to Data Analysis Probabilty
  • Session 20 Part 1
  • Session 20 Part 2
  • Bayes Theorem.pptx
  • Probability 1.pptx
  • Session 20 Part 3
  • Session 20 Part 4
  • Session 21 Agile 5
  • Agile Lecture 5.pptx
  • Session 21 Part 1
  • Session 21 Part 2
  • Session 21 Part 3
  • Session 21 Part 4
  • Session 22 Bayes Theorem Data PreProcessing Part 1 (handling Nulll values )
  • Session 22 Part 1
  • Session 22 Part 2
  • Session 22 Part 3
  • Session 22 Part 4
  • Session 23 Agile 6
  • Session 23 Part 1
  • Session 23 Part 2
  • Session 23 Part 3
  • Agile Lecture 6.pptx
  • Session 24 Random Variables Data PreProcessing Part 2 (handling Outlier ) IQR
  • Session 24 Part 1
  • Session 24 Part 2
  • Session 24 Part 3
  • Session 24 Part 4
  • Data Cleaning, and Transformation Session 25
  • Session 25 Part 1
  • Data Cleaning, and Transformation [anottated].pdf
  • Session 25 Part 2
  • Session 25 Part 3
  • Session 25 Part 4
  • Session 26 Agile Revision
  • Session 26 Part 1
  • Session 26 Part 2
  • Session 26 Part 3
  • Data preproccesing & Encoding Session 27
  • Session 27 Part 1
  • Session 27 Part 2
  • Session 27 Part 3
  • Session 27 Part 4
  • Session 28 project management 1
  • Session 28 Part 1
  • Session 28 Part 2
  • Project Management Lecture 1.pptx
  • Agile Test 2
  • Session 28 Part 3
  • Session 28 Part 4
  • EDA Project 1 on Insurance Data - Smoke Detection Session 29
  • Session 29 Part 1
  • Session 29 Part 2
  • Session 29 Part 3
  • Kaggle Workspace & Documntation EDA Project 2 on Loan Approval Session 30
  • Session 30 Part 1
  • Session 30 Part 2
  • Session 30 Part 3
  • Session 30 Part 4
  • Session 31 project managment 2
  • Session 31 Part 1
  • Session 31 Part 2
  • Session 31 Part 3
  • Session 31 Part 4
  • Session 31 Part 5
  • EDA Project 3 on House Pricing Session 32
  • Session 32 Part 1
  • Session 32 Part 2
  • Session 32 Part 3
  • Session 32 Part 4
  • EDA Final Project on Customer churn - Telco Data Github & Session 33
  • Session 33 Part 1
  • Session 33 Part 2
  • Session 33 Part 3
  • Session 33 Part 4
  • Session 33 Part 5
  • Machine Learning Supervied Vs Unsupervised Learning KNN Session 34
  • Session 34 Part 1
  • Session 34 Part 2
  • Session 34 Part 3
  • Session 34 Part 4
  • Session 35 project managment 3
  • Session 35 Part 1
  • Session 35 Part 2
  • Session 35 Part 3
  • Session 35 Part 4
  • Project Management Lecture 3.pptx
  • Supervised learning techniques: Regression Linear Regression Model Session 36
  • Session 36 Part 1
  • Session 36 Part 2
  • Session 36 Part 3
  • Session 36 Part 4
  • Session 37 project managment 4
  • Session 37 Part 1
  • Session 37 Part 2
  • Session 37 Part 3
  • Session 37 Part 4
  • Project Management Lecture 4.pptx
  • web-content_1730989779
  • Supervised learning Classification Logistic & Model Evaluation Session 38
  • Session 38 Part 1
  • Session 38 Part 2
  • Session 38 Part 3
  • Session 38 Part 4
  • Joint Propability & Naïve Bayes Classifier ML project 1 Session 39
  • Session 39 Part 1
  • Session 39 Part 2
  • Session 39 Part 3
  • Session 39 Part 4
  • ML1 - Introduction
  • ML2 - train_test_split & Linear Regression
  • ML3  - Classisfication ( Logistic Regression and Model Evaluation )
  • Session 40 - project management 5
  • project management 5
  • Session 40 Part 1
  • Session 40 Part 2
  • Session 40 Part 3
  • Session 40 Part 4
  • Session 41- Decision Tree Classifier and the modal automation
  • PyCaret Model Evaluation
  • Decision Tree Classifier Documentation
  • Session 41 Part 1
  • Session 41 Part 2
  • Session 41 Part 3
  • Session 41 Part 4
  • Session 42 - Design Thinking lec 1
  • Design Thinking
  • Session 42 Part 1
  • Session 42 Part 2
  • Session 42 Part 3
  • Session 42 Part 4
  • Session 43
  • Session 43 Part 1
  • Session 43 Part 2
  • Session 43 Part 3
  • Session 43 Part 4
  • Session 44 - Supervised ML
  • Intro to Unsupervised Learning
  • Session 44 Part 1
  • Session 44 Part 2
  • Session 44 Part 3
  • Session 44 Part 4
  • Session 45 - Design Thinking lec 2
  • Session 45 Part 1
  • Session 45 Part 2
  • Session 45 Part 3
  • Session 46 - Unsupervised Machine learning project
  • Session 46 Part 1
  • Session 46 Part 2
  • Session 46 Part 3
  • Session 46 Part 4
  • Session 47 - Design Thinking lec 3
  • Session 47 Part 1
  • Session 47 Part 2
  • Session 47 Part 3
  • Session 47 Part 4
  • Session 48 - Introduction to neural Network and Deep learning
  • Session 48 Part 1
  • Session 48 Part 2
  • Session 48 Part 3
  • Session 48 Part 4
  • Session 48 Part 5
  • Session 49 - Artificial neural network Project 1 on house prising data
  • Session 49 Part 1
  • Session 49 Part 2
  • Session 49 Part 3
  • Session 49 Part 4
  • Session 50 - project managment review
  • Session 50 Part 1
  • Session 50 Part 2
  • Session 50 Part 3
  • Session 50 Part 4
  • Session 51 - Artificial neural networks project 2 - modle evaluation
  • Session 51 Part 1
  • Session 51 Part 2
  • Session 51 Part 3
  • Session 51 Part 4
  • Session 52 - Design Thinking lec 4
  • Session 52 Part 1
  • Session 52 Part 2
  • Session 52 Part 3
  • Session 53 - Convolutional Neural Network
  • Session 53 Part 1
  • Session 53 Part 2
  • Session 53 Part 3
  • Session 53 Part 4
  • ConvNets and TransferLearning
  • Introduction TConvolutional NeuralNetwork
  • Session 54- CNN practical project - Handwritten digital Recognizer
  • Session 54 Part 1
  • Session 54 Part 2
  • Session 54 Part 3
  • Session 54 Part 4
  • Session 55 - Design Thinking lec 5
  • Session 55 Part 1
  • Session 55 Part 2
  • Session 55 Part 3
  • Session 55 Part 4
  • Session 56 - Recurrent neural network LSTM
  • Session 56 Part 1
  • Session 56 Part 2
  • Session 56 Part 3
  • Session 56 Part 4
  • Session 57 Design Thinking lec 6
  • Session 57 Part 1
  • Session 57 Part 2
  • Session 57 Part 3
  • Design Thinking Lecture 6.pptx
  • Session 58 LSTM Practical - Text Classification on Qoura Insincere
  • Session 58 Part 1
  • Session 58 Part 2
  • Session 58 Part 3
  • Session 58 Part 4
  • Session 59 LSTM Practical - Text Classification on Qoura Insincere
  • Session 59 Part 1
  • Session 59 Part 2
  • Session 59 Part 3
  • Session 59 Part 4
  • Session 60 Creative Thinking lec 1
  • Creative Thinking Lecture 1.pptx
  • Session 60 Part 1
  • Session 60 Part 2
  • Session 60 Part 3
  • Session 60 Part 4
  • Session 61 - Deep Learning projects discussion and Evaluation
  • Session 61 Part 1
  • Session 61 Part 2
  • Session 61 Part 3
  • Session 61 Part 4
  • Session 62 - revession
  • Session 62 Part 1
  • Session 62 Part 2
  • Session 62 Part 3
  • Session 62 Part 4
  • Session 63 - Deep learning project Fine Tuning
  • Session 63 Part 1
  • Session 63 Part 2
  • Session 63 Part 3
  • Session 63 Part 4
  • Session 64 - Genirative AI Introdaction anf open AI aplication
  • Session 64 Part 1
  • Session 64 Part 2
  • Session 64 Part 3
  • Session 64 Part 4
  • Session 65 - creative thinking 2
  • Creative Thinking Lecture 2.pdf
  • Session 65 Part 1
  • Session 65 Part 2
  • Session 65 Part 3
  • Session 65 Part 4
  • Session 66 - Genirative AI & Git and GitHub
  • Session 66 Part 1
  • Session 66 Part 2
  • Session 66 Part 3
  • Session 66 Part 4
  • Session 67 review desine thinking
  • Session 67 Part 1
  • Session 67 Part 2
  • Session 67 Part 3
  • Session 67 Part 4
  • Session 68 - Genirative AI & Image generation
  • Session 68 Part 1
  • Session 68 Part 2
  • Session 68 Part 3
  • Session 68 Part 4
  • Introduction to Generative AI
  • Image generation with Img2Img
  • Multi-model integration
  • Session 69
  • Session 69 Part 1
  • Session 69 Part 2
  • Session 69 Part 3
  • Session 69 Part 4
  • Session 70 Interview skills and job search strategies lec 1
  • Session 70 Part 1
  • Session 70 Part 2
  • Session 71 review & presentation creative thinking
  • Session 71 Part 1
  • Session 71 Part 2
  • Session 71 Part 3
  • Session 72 Fine-tuning models
  • Session 72 Part 1
  • Session 72 Part 2
  • Session 72 Part 3
  • Session 72 Part 4
  • Session 73
  • Session 73 Part 1
  • Interview skills Lecture 1+2.pdf
  • Session 73 Part 2
  • Session 73 Part 3
  • Session 73 Part 4
  • Session 74
  • Session 74 Part 1
  • Session 74 Part 2
  • Session 74 Part 3
  • Session 74 Part 4
Completion rules
  • All units must be completed
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