The AI Engineer Course 2025: Complete AI Engineer Bootcamp
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What you'll learn
This course includes
- 11.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 194 lessons • 11.5 hours of video
The AI Engineer Course 2025: Complete AI Engineer Bootcamp
194 lessons
• 11.5 hours
The AI Engineer Course 2025: Complete AI Engineer Bootcamp
194 lessons
• 11.5 hours
- 1 Building an AI tool in 5 minutes 10:17
- 2 What does the course cover 03:18
- 3 Natural vs Artificial Intelligence 02:08
- 4 Brief history of AI 04:42
- 7 Structured vs unstructured data 01:48
- 5 Demystifying AI Data science 02:28
- 6 Weak vs Strong AI 02:44
- 8 How we collect data 04:03
- 9 Labelled and unlabelled data 02:07
- 10 Metadata Data that describes data 01:44
- 11 Machine learning 06:16
- 12 Supervised Unsupervised and Reinforcement learning 05:36
- 13 Deep learning 08:28
- 14 Robotics 04:36
- 19 Early approaches to Natural Language Processing NLP 02:43
- 15 Computer vision 04:36
- 16 Traditional ML 01:20
- 17 Generative AI 04:06
- 18 The rise of Gen AI Introducing ChatGPT 02:10
- 20 Recent NLP advancements 03:02
- 21 From Language Models to Large Language Models LLMs 06:12
- 22 The efficiency of LLM training Supervised vs Semisupervised learning 03:36
- 23 From NGrams to RNNs to Transformers The Evolution of NLP 05:23
- 24 Phases in building LLMs 04:41
- 25 Prompt engineering vs Finetuning vs RAG Techniques for AI optimization 04:25
- 26 The importance of foundation models 02:51
- 27 Buy vs Make foundation models vs private models 02:37
- 28 Inconsistency and hallucination 02:44
- 29 Budgeting and API costs 02:59
- 30 Latency 01:27
- 31 Running out of data 02:26
- 32 Python programming 02:08
- 33 Working with APIs 01:36
- 34 Vector databases 03:12
- 35 The importance of open source 06:12
- 36 Hugging Face 01:48
- 37 LangChain 02:56
- 38 AI evaluation tools 03:08
- 39 AI strategist 05:09
- 40 AI developer 04:28
- 41 AI engineer 03:54
- 42 AI ethics 05:40
- 43 Future of AI 04:40
- 44 Programming Explained in a Few Minutes 05:05
- 45 Why Python 04:33
- 46 Jupyter Introduction 03:30
- 47 Jupyter Installing Anaconda 03:35
- 48 Jupyter Introduction to Using Jupyter 03:12
- 49 Jupyter Working with Notebook Files 06:10
- 50 Jupyter Using Shortcuts 03:08
- 51 Jupyter Handling Error Messages 05:53
- 52 Jupyter Restarting the Kernel 02:19
- 53 Python Variables 03:38
- 54 Types of Data Numbers and Boolean Values 03:06
- 55 Types of Data Strings 05:41
- 56 Basic Python Syntax Arithmetic Operators 03:24
- 57 Basic Python Syntax The Double Equality Sign 01:34
- 58 Basic Python Syntax Reassign Values 01:09
- 59 Basic Python Syntax Add Comments 01:35
- 60 Basic Python Syntax Line Continuation 00:51
- 61 Basic Python Syntax Indexing Elements 01:19
- 62 Basic Python Syntax Indentation 01:45
- 63 Operators Comparison Operators 02:11
- 64 Operators Logical and Identity Operators 05:37
- 65 Conditional Statements The IF Statement 03:03
- 66 Conditional Statements The ELSE Statement 02:46
- 67 Conditional Statements The ELIF Statement 05:35
- 68 Conditional Statements A Note on Boolean Values 02:15
- 69 Functions Defining a Function in Python 02:03
- 70 Functions Creating a Function with a Parameter 03:50
- 71 Functions Another Way to Define a Function 02:37
- 72 Functions Using a Function in Another Function 01:50
- 73 Functions Combining Conditional Statements and Functions 03:07
- 74 Functions Creating Functions Containing a Few Arguments 01:18
- 75 Functions Notable Builtin Functions in Python 03:57
- 76 Sequences Lists 04:03
- 77 Sequences Using Methods 03:20
- 78 Sequences List Slicing 04:32
- 79 Sequences Tuples 03:12
- 80 Sequences Dictionaries 04:05
- 81 Iteration For Loops 02:57
- 82 Iteration While Loops and Incrementing 02:27
- 83 Iteration Creatie Lists with the range Function 03:50
- 84 Iteration Use Conditional Statements and Loops Together 03:13
- 85 Iteration Conditional Statements Functions and Loops 02:28
- 86 Iteration Iterating over Dictionaries 03:08
- 87 Introduction to Object Oriented Programming OOP 05:01
- 88 Modules Packages and the Python Standard Library 04:25
- 89 Importing Modules 03:26
- 90 What is Software Documentation 03:59
- 91 The Python Documentation 06:24
- 92 Introduction to the NLP course 02:40
- 94 Introduction to NLP 01:37
- 95 NLP in everyday life 01:15
- 96 Supervised vs unsupervised NLP 01:47
- 97 The importance of data preparation 01:46
- 98 Lowercase 02:11
- 99 Removing stop words 03:54
- 100 Regular expressions 09:57
- 101 Tokenization 03:06
- 102 Stemming 02:46
- 103 Lemmatization 02:22
- 104 Ngrams 04:00
- 106 Practical task 10:16
- 107 Text tagging 01:25
- 108 Parts of Speech POS tagging 04:26
- 109 Named Entity Recognition NER 03:45
- 111 Practical task 09:44
- 112 What is sentiment analysis 02:00
- 113 Rulebased sentiment analysis 05:25
- 114 Pretrained transformer models 04:23
- 116 Practical task 05:43
- 117 Numerical representation of text 01:40
- 118 Bag of Words model 03:04
- 119 TFIDF 03:37
- 120 What is topic modelling 02:57
- 121 When to use topic modelling 01:34
- 122 Latent Dirichlet Allocation LDA 02:20
- 124 LDA in Python 04:26
- 125 Latent Semantic Analysis LSA 01:40
- 126 LSA in Python 01:22
- 127 How many topics 03:49
- 128 Building a custom text classifier 00:57
- 129 Logistic regression 04:39
- 130 Naive Bayes 01:34
- 131 Linear support vector machine 02:25
- 133 Introducing the project 03:33
- 134 Exploring our data through POS tags 09:25
- 135 Extracting named entities 04:52
- 136 Processing the text 08:31
- 137 Does sentiment differ between news types 05:12
- 138 What topics appear in fake news Part 1 06:12
- 139 What topics appear in fake news Part 2 05:57
- 140 Categorizing fake news with a custom classifier 05:49
- 141 What is deep learning 03:06
- 142 Deep learning for NLP 01:52
- 143 NonEnglish NLP 01:49
- 144 Whats next for NLP 01:40
- 145 Introduction to the course 02:21
- 147 - What are LLMs 02:56
- 148 How large is an LLM 02:57
- 149 General purpose models 01:09
- 150 Pretraining and fine tuning 02:40
- 151 What can LLMs be used for 03:18
- 152 Deep learning recap 02:33
- 153 The problem with RNNs 03:36
- 154 The solution attention is all you need 02:51
- 155 The transformer architecture 01:02
- 156 Input embeddings 02:55
- 157 Multiheaded attention 04:00
- 158 Feedforward layer 02:39
- 159 Masked multihead attention 01:24
- 160 Predicting the final outputs 01:45
- 161 What does GPT mean 01:28
- 162 The development of ChatGPT 02:29
- 163 OpenAI API 02:59
- 164 Generating text 02:26
- 165 Customizing GPT output 04:04
- 166 Key word text summarization 03:47
- 167 Coding a simple chatbot 06:17
- 168 Introduction to LangChain in Python 01:28
- 169 LangChain 02:50
- 170 Adding custom data to our chatbot 05:21
- 171 Hugging Face package 02:41
- 172 The transformer pipeline 05:50
- 173 Pretrained tokenizers 09:02
- 174 Special tokens 02:54
- 175 Hugging Face and PyTorchTensorFlow 04:33
- 176 Saving and loading models 01:26
- 177 GPT vs BERT 03:03
- 178 BERT architecture 04:40
- 179 Loading the model and tokenizer 01:48
- 180 BERT embeddings 03:43
- 181 Calculating the response 05:33
- 182 Creating a QA bot 08:41
- 183 BERT RoBERTa DistilBERT 03:07
- 184 GPT vs BERT vs XLNET 04:18
- 186 Preprocessing our data 09:59
- 187 XLNet Embeddings 04:25
- 188 Fine tuning XLNet 03:56
- 189 Evaluating our model 03:03
- 190 Introduction to the course 04:54
- 192 Business applications of LangChain 05:23
- 193 What makes LangChain powerful 04:33
- 194 What does the course cover 05:33
- 195 Tokens 06:08
- 196 Models and Prices 03:29
- 197 Setting up a custom anaconda environment for Jupyter integration 03:43
- 198 Obtaining an OpenAI API key 02:05
- 199 Setting the API key as an environment variable 07:12
- 200 First Steps 03:52
- 201 System user and assistant roles 03:38
- 202 Creating a sarcastic chatbot 02:47
- 203 Temperature max tokens and streaming 06:28
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