Home / Course catalog / Ai (KI) Development, KI Manager V.1


Get this course (€13,720.00)

Programming

Ai (KI) Development, KI Manager V.1


Content
  • Session 1 welcome session
  • Session 1 Part 1
  • Session 1 Part 2
  • Session 2 introduction
  • Session 2 Part 1
  • Session 2 Part 2
  • Session 2 Part 3
  • Session 3 Introduction to Python
  • Session 3 Part 1
  • Session 3 Part 2
  • Session 3 Part 3
  • Session 4 Data Type
  • Session 4 Part 1
  • Session 4 Part 2
  • Session 4 Part 3
  • Session 5 Sort to list
  • Session 5 Part 1
  • Session 5 Part 2
  • Session 5 Part 3
  • Session 6 List, Tuple, Dict
  • 1st Assignment
  • Session 6 Part 1
  • Session 6 Part 2
  • Session 7 Set in Python
  • Session 7 Part 1
  • Session 7 Part 2
  • Session 7 Part 3
  • Session 8 Data Type
  • Session 8 Part 1
  • Session 8 Part 2
  • Session 8 Part 3
  • Session 9 Data type 2
  • Session 9 Part 1
  • Session 9 Part 2
  • Session 9 Part 3
  • Session 10 Random
  • Session 10 Part 1
  • Session 10 Part 2
  • Session 10 Part 3
  • 1st Assignment
  • Session 11 soft skills
  • Session 11 Part 1
  • Session 11 Part 2
  • Session 11 Part 3
  • Session 12 String methods
  • Session 12 Part 1
  • Session 12 Part 2
  • Session 12 Part 3
  • Session 13 Format values
  • Session 13 Part 1
  • Session 13 Part 2
  • Session 13 Part 3
  • Session 14 Functions
  • Session 14 Part 1
  • Session 14 Part 2
  • Session 14 Part 3
  • Session 15 Python Operators
  • Session 15 Part 1
  • Session 15 Part 2
  • Session 16 Loop
  • Session 16 Part 1
  • Session 16 Part 2
  • Session 16 Part 3
  • Session 17 loop
  • Session 17 Part 1
  • Session 17 Part 2
  • Session 17 Part 3
  • Session 18 Open File
  • Session 18 Part 1
  • Session 18 Part 2
  • Session 18 Part 3
  • Session 19 open file
  • Session 19 Part 1
  • Session 19 Part 2
  • Session 19 Part 3
  • Session 20 Use With & datetime
  • Session 20 Part 1
  • Session 20 Part 2
  • Session 20 Part 3
  • Session 21 Function in Python
  • Session 21 Part 1
  • Session 21 Part 2
  • Session 21 Part 3
  • Session 22 Quiz
  • Quiz 2
  • Session 22 Part 1
  • Session 22 Part 2
  • Session 23 OS Library & Error Handling
  • Session 23 Part 1
  • Session 23 Part 2
  • Session 23 Part 3
  • Session 24 Math Library
  • Session 24 Part 1
  • Session 24 Part 2
  • Session 24 Part 3
  • Session 25 Yield, Lambda, Filter
  • Quiz 3
  • Session 25 Part1
  • Session 25 Part 2
  • Session 26 using If
  • Session 26 Part 1
  • Session 26 Part 2
  • Session 26 Part 3
  • Session 27 using if
  • Session 27 Part 1
  • Session 27 Part 2
  • Session 27 Part 3
  • Session 28 Global & Nonlocal
  • Session 28 Part 1
  • Session 28 Part 2
  • Session 28 Part 3
  • Session 29 Constructor
  • Session 29 Part 1
  • Session 29 Part 2
  • Session 29 Part 3
  • Session 30
  • Session 30 Part 1
  • Session 30 Part 2
  • Session 30 Part 3
  • Quiz 4
  • Session 31
  • Session 31 Part 1
  • Session 31 Part 2
  • Session 31 Part 3
  • Session 32 Setattr, exec & inter-class Calling methods in Python
  • Session 32 Part 1
  • Session 32 Part 2
  • Session 32 Part 3
  • Session 33 Inheritance
  • Session 33 Part 1
  • Session 33 Part 2
  • Session 33 Part 3
  • Session 34 Inheritance
  • Session 34 Part 1
  • Session 34 Part 2
  • Session 34 Part 3
  • Session 35 type of Constructor
  • Session 35 Part 1
  • Session 35 Part 2
  • Session 35 Part 3
  • Session 36 Polymorphism & Encapsulation in OOP
  • Session 36 Part 1
  • Session 36 Part 2
  • Session 36 Part 3
  • Session 37
  • Session 37 Part 1
  • Session 37 Part 2
  • Session 38
  • Session 38 Part 1
  • Session 38 Part 2
  • Session 38 Part 3
  • Session 39 Numpy
  • Session 39 Part 1
  • Session 39 Part 2
  • Session 39 Part 3
  • Session 40 Numpy
  • Session 40 Part 1
  • Session 40 Part 2
  • Session 40 Part 3
  • Session 41
  • Session 41 Part 1
  • Session 41 Part 2
  • Session 41 Part 3
  • Session 42
  • Quiz 6
  • Session 42 Part 1
  • Session 42 Part 2
  • Session 43 NumPy: Array Object Exercises, Practice, Solution
  • Session 43 Part 1
  • Session 43 Part 2
  • Session 43 Part 3
  • Session 44
  • Session 44 Part 1
  • Session 44 Part 2
  • Session 44 Part 3
  • Session 45 NumPy: Array Object Exercises, Practice, Solution
  • Session 45 Part 1
  • Session 45 Part 2
  • Session 45 Part 3
  • Session 46 Array Module
  • Session 46 Part 1
  • Session 46 Part 2
  • Session 46 Part 3
  • Session 47
  • Quiz 7
  • Session 47 Part 1
  • Session 47 Part 2
  • Session 47 Part 3
  • Session 48 Project1
  • Project1
  • Session 48 Part 1
  • Session 48 Part 2
  • Session 48 Part 3
  • Session 49 Discussing of the draft and Jupyter Intro
  • Session 49 Part 1
  • Session 49 Part 2
  • Session 50 pandas
  • Session 50 Part 1
  • Session 50 Part 2
  • Session 50 Part 3
  • Session 51 pandas and python multisets
  • Session 51 Part 1
  • Session 51 Part 2
  • Session 51 Part 3
  • Session 52
  • Quiz 8
  • Session 52 Part 1
  • Session 52 Part 2
  • Session 53 pandas
  • Session 53 Part 1
  • Session 53 Part 2
  • Session 53 Part 3
  • Session 54 pandas
  • Session 54 Part 1
  • Session 54 Part 2
  • Session 54 Part 3
  • Session 55 pandas
  • Assignment 1
  • Session 55 Part 1
  • Session 55 Part 2
  • Session 55 Part 3
  • Session 56 pandas
  • Session 56 Part 1
  • Session 56 Part 2
  • Session 56 Part 3
  • Session 57 pandas
  • Session 57 Part 1
  • Session 57 Part 2
  • Session 57 Part 3
  • Session 58 Data Preprocessing
  • Session 58 Part 1
  • Session 58 Part 2
  • Session 59 Data Preprocessing
  • Session 59 Part 1
  • Session 59 Part 2
  • Session 59 Part 3
  • Session 60 Data Preprocessing
  • Session 60 Part 1
  • Session 60 Part 2
  • Session 60 Part 3
  • Session 61 Data Preprocessing
  • Session 61 Part 1
  • Session 61 Part 2
  • Session 61 Part 3
  • Session 62
  • Session 62 Part 1
  • Session 62 Part 2
  • Session 63 Life expectancy project
  • Session 63 Part 1
  • Session 63 Part 2
  • Session 63 Part 3
  • Session 64 Life expectancy project
  • Assignment 2
  • Session 64 Part 1
  • Session 64 Part 2
  • Session 64 Part 3
  • Assignment 2 edit
  • Session 65 Part 1
  • Session 65 Part 2
  • Session 65 Part 3
  • Session 66 Life expectancy project
  • Assignment 2 final submission
  • Session 66 Part 1
  • Session 66 Part 2
  • Session 66 Part 3
  • Session 67 Life expectancy project
  • Session 67 Part 1
  • Session 67 Part 2
  • Session 68 Covid-19 project
  • Session 68 Part 1
  • Session 68 Part 2
  • Session 69
  • Session 69 Part 1
  • Session 69 Part 2
  • Session 70 kdd-cup
  • Session 70 Part 1
  • Session 70 Part 2
  • Session 71 kdd-cup
  • Session 71 Part 1
  • Session 72
  • Session 72 Part 3
  • Session 73 smart_grid
  • Session 73 Part 1
  • Session 73 Part 2
  • Session 74 Loan Prediction Problem
  • Session 74 Part 1
  • Session 74 Part 2
  • Session 75 german_credit
  • Assignment 3-German Credit
  • Session 75 Part 1
  • Session 75 Part 2
  • Session 76 kdd-cup
  • Assignment 4- Kdd cup99
  • Session 76 Part 1
  • Session 76 Part 2
  • Session 77
  • Session 77 Part 1
  • Session 77 Part 2
  • Session 78 Machine Learning intro
  • Session 78 Part 1
  • Session 78 Part 2
  • Session 79 Linear Regression
  • Session 79 Part 1
  • Session 79 Part 2
  • Session 80 Linear Regression
  • Session 80 Part 1
  • Session 80 Part 2
  • Linear Regression in Python
  • Session 81 Linear Regression
  • Session 81 Part 1
  • Session 81 Part 2
  • Session 82 Multiple linear regression
  • Session 82 Part 1
  • Session 82 Part 2
  • Session 83 Design tree
  • Design tree
  • Session 83 Part 1
  • Session 83 Part 2
  • Session 84 Random forest
  • Session 84 Part 1
  • Session 84 Part 2
  • Session 85 Regression Task
  • Session 85 Part 1
  • Session 86 Car Project
  • car details v4
  • Session 86 Part 1
  • Session 86 Part 2
  • Session 87 Cancer project game
  • Session 87 Part 1
  • Session 87 Part 2 Room 1
  • Session 87 Part 2 Room 2
  • Session 88 Car v4 project discussion
  • Session 88 Part 1
  • Session 88 Part 2
  • Session 88 Part 3
  • Session 89 Cancer project Game and discussion
  • Session 89 Part 1
  • Session 89 Part 2
  • Session 90 Random Forest Algorithm
  • Cancer-reg
  • Session 90 Part 1
  • Session 90 Part 2
  • Session 91 General Q & A about all the past lectures
  • Session 91 Part 1
  • Session 91 Part 2
  • Session 92 Pandas review part 1
  • Session 92 Part 1
  • Session 92 Part 2
  • Session 93 Pandas review part 2
  • Session 93 Part 1
  • Session 93 Part 2
  • Session 94 Visualization - Seaborn
  • Pandas Pair-1
  • Pandas Pair-2
  • Types of data + Descriptive stat
  • Seaborn
  • Session 94 Part 1
  • Session 94 Part 2
  • Session 94 Part 3
  • Session 95 Visualization - Seaborn part 2
  • Session 95 Part 1
  • Session 95 Part 2
  • Session 95 Part 3
  • Session 96 Linear Regression
  • Box Plot.pdf
  • Correlation & Scatter plot materials.pdf
  • Python - Intro to Linear Regression.pdf.pptx
  • Linear Regression equations.pdf
  • exercise Linear Regression
  • Session 96 Part 1
  • Session 96 Part 2
  • Session 97 exercise
  • Session 97 Part 1
  • Session 97 Part 2
  • Session 98 Polynomial Regression
  • Polynomial Regression
  • Polynomial regression.pptx
  • Encoding.pptx
  • Feature Scaling.pptx
  • Linear Regression with Feature Scaling
  • Session 98 Part 1
  • Session 98 Part 2
  • Session 98 Part 3
  • Session 99 support Vector Regression
  • SVR.pptx
  • SVR
  • Linear Regression
  • Session 99 Part 1
  • Session 99 Part 2
  • Session 99 Part 3
  • Session 100 Decision Trees
  • Session 100 Part 1
  • Session 100 Part 2
  • Session 100 Part 3
  • Session 101 Classification Performance Metrics
  • Session 101 Part 1
  • Session 101 Part 2
  • Session 101 Part 3
  • Session 102 Random Forest
  • Intro to Tree Methods.pdf.pptx
  • Decision Trees and Random Forest
  • Classification Performance Metrics.pptx
  • Session 102 Part 1
  • Session 102 Part 2
  • Session 102 Part 3
  • Session 103 Logistic Regression
  • Session 103 Part 1
  • Session 103 Part 2
  • Session 104 exercise
  • Session 104 Part 1
  • Session 104 Part 2
  • Session 105 K Nearest Neighbors
  • Session 105 Part 1
  • Session 105 Part 2
  • Session 106 Support Vector Machines
  • Intro to Logistic Regression.pdf.pptx
  • Logistic Regression Project
  • Intro to K Nearest Neighbors.pdf.pptx
  • KNN
  • SVM.pptx
  • Support Vector Machines Project
  • Session 106 Part 1
  • Session 106 Part 2
  • Session 107 Final Project - Supervised Learning
  • Session 107 Part 1
  • Session 107 Part 2
  • Session 108 Intro to K Means Clustering
  • Session 108 Part 1
  • Session 108 Part 2
  • Session 109 exercise
  • Session 109 Part 1
  • Session 109 Part 2
  • Session 110 K Means ++
  • Intro to K Means Clustering.pdf.pptx
  • K Means Clustering
  • Project 2 - Supervised Learning
  • HC
  • K Means ++.pptx
  • Session 110 Part 1
  • Session 111 PCA
  • Principal Component Analysis
  • Principal Component Analysis.pdf.pptx
  • Session 111 Part 1
  • Session 111 Part 2
  • Session 112
  • 911 project
  • Session 112 Part 1
  • Session 112 Part 2
  • Session 113 Natural Language Processing
  • Intro to Natural Language Processing.pdf.pptx
  • Session 113 Part 1
  • Session 113 Part 2
  • Session 114 NLP in Python
  • NLP
  • Session 114 Part 1
  • Session 114 Part 2
  • Session 115
  • Session 115 Part 1
  • Session 116 Spacy Basics
  • Session 116 Part 1
  • Session 116 Part 2
  • Session 117 Tokenization
  • Spacy Basics
  • Spacy Basics.pptx
  • Session 117 Part 1
  • Session 117 Part 2
  • Session 118 Stemming, Lemmatization and Stop Words
  • Session 118 Part 1
  • Session 118 Part 2
  • Session 119 POS, NER and Displacy
  • Session 119 Part 1
  • Session 119 Part 2
  • Session 119 Part 3
  • Session 120 POS Project Overview
  • Stemming, Lemmatization and Stop words
  • Stemming, Lemmatization and Stop words.pptx
  • POS tagging
  • Parts-of-Speech-Tagging.pptx
  • Session 120 Part 1
  • Session 120 Part 2
  • Session 121 Exercises
  • Exercises
  • Numpy Exercises
  • Pandas Exercises
  • Python Exercises
  • Session 121 Part 1
  • Session 121 Part 2
  • Session 121 Part 3
  • Session 122 Text Classification
  • Text Classification
  • Text Classification.pptx
  • Session 122 Part 1
  • Session 122 Part 2
  • Session 123 Sentiment Analysis
  • Session 123 Part 1
  • Session 123 Part 2
  • Session 123 Part 3
  • Session 124 Sentiment Analysis
  • Sentiment Analysis
  • Semantics-and-Sentiment-Analysis.pptx
  • Session 124 Part 1
  • Session 124 Part 2
  • Session 124 Part 3
  • Session 125 Topic Modeling Overview
  • LDA
  • LDA.pptx
  • Session 125 Part 1
  • Session 125 Part 2
  • Session 126 Non-Negative Matrix Factorization
  • Session 126 Part 1
  • Session 126 Part 2
  • Session 127
  • NMF
  • NNMF.pptx
  • ANN Part 1 - Perceptron.pptx
  • ANN Part 2 - Neural Network.pptx
  • Deep Learning
  • Session 127 Part 1
  • Session 127 Part 2
  • Session 128 NN, Activation functions, Cost Function and Gradient Descent
  • Session 128 Part 1
  • Session 128 Part 2
  • Session 128 Part 3
  • Session 129 Backpropagation, TensorFlow & Keras, Keras Basics
  • Session 129 Part 1
  • Session 129 Part 2
  • Sessio 130 Regression in Keras part 1
  • Session 130 Part 1
  • Session 130 Part 2
  • Session 130 Part 3
  • Session 131 Regression in Keras part 2
  • Session 131 Part 1
  • Session 131 Part 2
  • Session 132 Classification in Keras, Early Stopping & Dropout layers
  • Keras
  • Session 132 Part 1
  • Session 132 Part 2
  • Session 132 Part 3
  • Session 133 Exercises
  • Session 133 Part 1
  • Session 133 Part 2
  • Session 134 Exercises
  • Session 134 Part 1
  • Session 134 Part 2
  • Session 135 TensorBoard & CNN part 1
  • TensorBoard.pptx
  • ANN
  • CNN
  • CNN Part 1.pptx
  • Session 135 Part 1
  • Session 135 Part 2
  • Session 135 Part 3
  • Session 136 CNN parts 2 & 3
  • CNN Part 2.pptx
  • Session 136 Part 1
  • Session 136 Part 2
  • CNN Part 3.pptx
  • Session 136 Part 3
  • Session 137 CNN on MNIST & CNN on CIFAR Notebooks - Practice
  • CIFAR.pptx
  • Session 137 Part 1
  • Session 137 Part 2
  • Session 137 Part 3
  • Session 138 Exercises
  • Session 138 Part 1
  • Session 138 Part 2
  • Session 139 Exercises
  • Seaborn & Modelling project
  • Session 139 Part 1
  • Session 139 Part 2
  • Session 140 CNN on Malaria cell images Project
  • CNN Part 4.pptx
  • Session 140 Part 1
  • Session 140 Part 2
  • Session 140 Part 3
  • Session 141 Exercises
  • DL_CV_Project
  • Session 141 Part 1
  • Session 141 Part 2
  • Session 141 Part 3
  • Session 142 Exercises
  • Session 142 Part 1
  • Session 142 Part 2
  • Session 143 RNN Part 1
  • Session 143 Part 1
  • Session 143 Part 2
  • Session 143 Part 3
  • Session 144 LSTM
  • RNN Part 1.pptx
  • RNN Part 2.pptx
  • Session 144 Part 1
  • Session 144 Part 2
  • Session 145 GRU
  • Session 145 Part 1
  • Session 145 Part 2
  • Session 145 Part 3
  • Session 146 LSTM time series example - code along
  • GRU.pptx
  • RNN Project
  • Session 146 Part 1
  • Session 146 Part 2
  • Session 146 Part 3
  • Session 147 Exercises
  • Session 147 Part 1
  • Session 147 Part 2
  • Session 148 Exercises
  • Session 148 Part 1
  • Session 148 Part 2
  • Session 149 NLP with Deep Learning
  • NLP with Deep Learning
  • NLP-with-Deep-Learning.pptx
  • Session 149 Part 1
  • Session 149 Part 2
  • Session 150 Text Generation With GRU
  • Session 150 Part 1
  • Session 150 Part 2
  • Session 150 Part 3
  • Session 151 Text Generation With GRU part 2
  • Session 151 Part 1
  • Session 151 Part 2
  • Session 152 Exercises
  • Session 152 Part 1
  • Session 152 Part 2
  • Session 153 Exercises
  • Session 153 Part 1
  • Session 153 Part 2
  • Session 154 Exercises
  • melville-moby_dick
  • Session 154 Part 1
  • Session 154 Part 2
  • Session 154 Part 3
  • Session 155 AutoEncoders part 1
  • Session 155 Part 1
  • Session 155 Part 2
  • Session 155 Part 3
  • Session 156 AutoEncoders part 2
  • Session 156 Part 1
  • Session 156 Part 2
  • Session 156 Part 3
  • Session 157 GANs part 1
  • Session 157 Part 1
  • Session 157 Part 2
  • Session 157 Part 3
  • Session 158 Exercises
  • Session 158 Part 1
  • Session 158 Part 2
  • Session 159 GANs part 2
  • AutoEncoders
  • AutoEncoders.pptx
  • Generative Adversarial Networks
  • Generative Adversarial Networks
  • Sales Analysis Project
  • Session 159 Part 1
  • Session 159 Part 2
  • Session 160
  • DCGANS
  • Session 160 Part 1
  • Session 160 Part 2
  • Session 161 Communication and soft skills part1
  • Communication and soft skills.pptx
  • Session 161 Part 1
  • Session 161 Part 2
  • Session 162 Transformers
  • Session 162
  • Session 163 Session 161 Communication and soft skills part2
  • Session 163 Part 1
  • Session 163 Part 2
  • Session 163 Part 3
  • Session 164 GPT
  • Generative AI
  • Transformer.pptx
  • Session 164
  • Session 165 Fine-Tunning LLMs
  • Session 165
  • Session 166 Time Management
  • Time Management .pptx
  • Session 166 Part 1
  • Session 166 Part 2
  • Session 167 From GPT to ChatGPT and GPT-4
  • Session 167 Part 1
  • Session 167 Part 2
  • Session 167 Part 3
  • Session 168 Project Management Part1
  • Project Management
  • Session 168 Part 1
  • Session 168 Part 2
  • Session 169 Exercises
  • Creating_Cohorts_of_Songs_Clustering
  • Session 169 Part 1
  • Session 169 Part 2
  • Session 170 OpenAI API - Chat Completion
  • Session 170
  • Session 171 Session 168 Project Management Part2
  • The OpenAI API.pptx
  • Open AI API
  • Session 171 Part 1
  • Session 171 Part 2
  • Session 171 Part 3
  • Session 172 OpenAI API - Tools & Functions, Image Generation (DALL-E) & Embeddin
  • Session 172
  • Session 173 Project Management Part3
  • Session 173 Part 1
  • Session 173 Part 2
  • Session 173 Part 3
  • Session 174 Fine-Tunning via OpenAI API
  • Session 174
  • Session 175 RAG part 1
  • Session 175
  • Session 176 Project Management Part4
  • Session 176 Part 1
  • Session 176 Part 2
  • Session 176 Part 3
  • Session 177 Exercises
  • Final Project DL
  • Session 177 Part 1
  • Session 177 Part 2
  • Session 178 Agile Lecture 1
  • Agile Lecture 1.pptx
  • Session 178 Part 1
  • Session 178 Part 2
  • Session 179 Exercises
  • Session 179 Part 1
  • Session 179 Part 2
  • Session 180 RAG part 2
  • Session 180
  • Session 181 Agile Lecture 2
  • Session 181 Part 1
  • Agile Lecture 2.pptx
  • Session 182 Vector Databases using VectorDB
  • Session 182
  • Session 183
  • Session 184 Exercises
  • Session 184 Part 1
  • Session 185 LangChain
  • Session 185
  • Session 186 Agile Lecture 3
  • Agile Lecture 3.pptx
  • Session 186 Part 1
  • Session 186 Part 2
  • Session 186 Part 3
  • Session 187 LangChain, RAGAs & RAG evaluation metrics
  • Session 187
  • Session 188
  • Session 188 Part 1
  • Session 189 Advanced RAG with LangChain
  • Session 189
  • Session 190
  • Agile Lecture 4.pptx
  • Session 190 Part 1
  • Session 190 Part 2
  • Session 190 Part 3
  • Session 191 LLM Agents
  • Session 191
  • Session 192 Agile Lecture 5
  • Session 192 Part 1
  • Agile Lecture 5.pptx
  • Session 192 Part 2
  • Session 193 LLM Agents with Multiple Tools
  • Session 193
  • Session 194 Face Detection part 1
  • Computer Vision
  • Session 194
  • Session 195 Agile Lecture 6
  • Agile Lecture 6.pptx
  • Session 195 Part 1
  • Session 195 Part 2
  • Session 196 Eye Detection
  • Session 196
  • Session 197 Design Thinking Lecture 1
  • Design Thinking Lecture 1.pptx
  • Session 197 Part 1
  • Session 197 Part 2
  • Session 198 Face Detection using HOG & CNN
  • Session 198
  • Session 199 Face Detection in Videos
  • Face Detection 1.pptx
  • HOG.pptx
  • Session 199 Part 1
  • Session 199 Part 2
  • Session 200 Design Thinking Lecture 2
  • Design Thinking Lecture 2 (1).pptx
  • Session 200 Part 1
  • Session 200 Part 2
  • Session 200 Part 3
  • Session 201 Face Recognition Part 1
  • Session 201
  • Session 202 Design Thinking Lecture 3
  • Design Thinking Lecture 3.pptx
  • Session 202 Part 1
  • Session 202 Part 2
  • Session 202 Part 3
  • Session 203 Face Recognition Part 2 - CNN
  • Face Recognition.pptx
  • Session 203
  • Session 204 Face Recognition Part 3 - CNN
  • Face Recognition Project
  • Face Recognition
  • Session 204
  • Session 205 Design Thinking Lecture 4
  • Design Thinking Lecture 4.pptx
  • Session 205 Part 1
  • Session 205 Part 2
  • Session 206 Object Detection
  • Session 206 Part 1
  • Session 206 Part 2
  • Session 207 YOLO on images
  • Session 207 Part 1
  • Session 207 Part 2
  • Session 208 Introduction to git & GitHub
  • Git & GitHub
  • Introduction to Git part 1.pptx
  • VCS Introduction.pptx
  • Session 208
  • Session 209 Design Thinking Lecture 5
  • Design Thinking Lecture 5.pptx
  • Session 209 Part 1
  • Session 209 Part 2
  • Session 210 How to create & clone public or private repos
  • Session 210-20241106_091605-Meeting Recording.mp4
  • Session 211 Design Thinking Lecture 6
  • Session 211 Part 1
  • Session 211 Part 2
  • Design Thinking Lecture 6.pptx
  • Session 212 Exercises
  • mammo_masses_project
  • Session 212 Part 1
  • Session 212 Part 2
  • Session 213
  • Marketing Project
  • Session 213 Part 1
  • Session 213 Part 2
  • Session 214 Creative Thinking Lecture 1
  • Creative Thinking Lecture 1.pptx
  • Session 214 Part 1
  • Session 214 Part 2
  • Session 215 git add, commit, log, push, pull & fetch
  • Session 215
  • Session 216 Creative Thinking Lecture 2
  • Creative Thinking Lecture 2.pptx
  • Session 216 Part 1
  • Session 216 Part 2
  • Session 217 git Branches
  • Session 217
  • Session 218 German language lesson
  • Session 218
  • Basic German for Tech Professionals 1.pptx
  • Session 219 Exercises
  • Session 219 Part 1
  • Session 219 Part 2
  • Session 220 git merge
  • Python Basics Project
  • Session 220
  • Session 221 German language lesson
  • Session 221 Part 1
  • Session 221 Part 2
  • Session 222 git restore, reset, revert
  • 4 - Undoing Changes.pptx
  • Session 222
  • Session 223 German language lesson
  • Session 223 Part 1
  • Session 223 Part 2
  • Session 224 Exercises
  • Control-Flow-assignments
  • Session 224 Part 1
  • Session 224 Part 2
  • Session 225 Exercises
  • itergendecor
  • Session 225 Part 1
  • Session 225 Part 2
  • Session 226 Intermediate German for Tech Professionals 1
  • Session 226 Part 1
  • Session 226 Part 2
  • Session 227 Front end & Back end
  • Session 227
  • Session 228 Intermediate German for Tech Professionals
  • Basic German for Tech Professionals 2.pptx
  • Basic German for Tech Professionals 3.pptx
  • Intermediate German for Tech Professionals 1.pptx
  • Intermediate German for Tech Professionals
  • Session 228 Part 1
  • Session 228 Part 2
  • Session 229 MLOPs & Production Deployment
  • Production Model Package
  • prod-code-intro.pptx
  • Session 229
  • Session 230 Exercises
  • Session 230 Part 1
  • Session 230 Part 2
  • Session 231 Intermediate German for Tech Professionals
  • Session 231
  • Session 232 Exercises
  • classes & objects
  • Session 232 Part 1
  • Session 232 Part 2
  • Session 233 Intermediate German for Tech Professionals
  • Session 233 Part 1
  • Session 233 Part 2
  • Session 234 CI/CD pipelines
  • Session 234
  • Session 235 Exercises
  • Session 235 Part 1
  • Session 235 Part 2
  • Session 236 Intermediate German for Tech Professionals
  • Session 236 Part 1
  • Session 236 Part 2
  • Session 236 Part 3
  • Session 237 Exercises
  • OOP Project
  • Session 237 Part 1
  • Session 237 Part 2
  • Session 238 German for job applications and CV writing
  • German for job applications and CV writing
  • Session 238 Part 1
  • Session 238 Part 2
  • Session 239 Regulations on german labour market
  • Important links to create a CV
  • Session 239
  • Session 240
  • Session 240
  • Session 241 German for job applications and CV writing
  • Session 241 Part 1
  • Session 241 Part 2
  • Session 241 Part 3
  • Session 242
  • Session 242
  • Session 243
  • Session 243
  • Session 244
  • Session 244 Part 1
  • Session 244 Part 2
  • Session 245
  • Session 245
  • Session 246
  • Session 246 Part 1
  • Session 246 Part 2
  • Session 247
  • Session 247
  • Session 248
  • Capston project
  • Deployment & MLOPs
  • Session 248 Part 1
  • Session 248 Part 2
Completion rules
  • You must complete 90.00% of the content
Back