Complete AI Engineer Bootcamp 2025-1

1 Building an AI tool in 5 minutes A quick demo
2 What does the course cover
3 Natural vs Artificial Intelligence
4 Brief history of AI
5 Demystifying AI Data science Machine learning and Deep learning
6 Weak vs Strong AI

1 Structured vs unstructured data
2 How we collect data
3 Labelled and unlabelled data
4 Metadata Data that describes data

1 Machine learning
2 Supervised Unsupervised and Reinforcement learning
3 Deep learning

1 Robotics
2 Computer vision
3 Traditional ML
4 Generative AI

1 The rise of Gen AI Introducing ChatGPT
2 Early approaches to Natural Language Processing NLP
3 Recent NLP advancements
4 From Language Models to Large Language Models LLMs
5 The efficiency of LLM training Supervised vs Semi supervised learning
6 From N Grams to RNNs to Transformers The Evolution of NLP
7 Phases in building LLMs
8 Prompt engineering vs Fine tuning vs RAG Techniques for AI optimization
9 The importance of foundation models
10 Buy vs Make foundation models vs private models

1 Inconsistency and hallucination
2 Budgeting and API costs
3 Latency
4 Running out of data

1 Python programming
2 Working with APIs
3 Vector databases
4 The importance of open source
5 Hugging Face
6 LangChain
7 AI evaluation tools

1 AI strategist
2 AI developer
3 AI engineer

1 AI ethics
2 Future of AI

1 Programming Explained in a Few Minutes
2 Why Python

1 Jupyter Introduction
2 Jupyter Installing Anaconda
3 Jupyter Introduction to Using Jupyter
4 Jupyter Working with Notebook Files
5 Jupyter Using Shortcuts
6 Jupyter Handling Error Messages
7 Jupyter Restarting the Kernel

1 Python Variables
7 Types of Data Numbers and Boolean Values
14 Types of Data Strings

1 Basic Python Syntax Arithmetic Operators
11 Basic Python Syntax The Double Equality Sign
14 Basic Python Syntax Reassign Values
20 Basic Python Syntax Add Comments
22 Basic Python Syntax Line Continuation
24 Basic Python Syntax Indexing Elements
28 Basic Python Syntax Indentation

1 Operators Comparison Operators
7 Operators Logical and Identity Operators

1 Conditional Statements The IF Statement
5 Conditional Statements The ELSE Statement
7 Conditional Statements The ELIF Statement
10 Conditional Statements A Note on Boolean Values

1 Functions Defining a Function in Python
2 Functions Creating a Function with a Parameter
5 Functions Another Way to Define a Function
7 Functions Using a Function in Another Function
9 Functions Combining Conditional Statements and Functions
11 Functions Creating Functions Containing a Few Arguments
12 Functions Notable Built in Functions in Python

1 Sequences Lists
8 Sequences Using Methods
14 Sequences List Slicing
22 Sequences Tuples
27 Sequences Dictionaries

1 Iteration For Loops
5 Iteration While Loops and Incrementing
7 Iteration Creatie Lists with the range Function
12 Iteraion Use Conditional Statements and Loops Together
16 Iteration Conditional Statements Functions and Loops
18 Iteration Iterating over Dictionaries

1 Introduction to Object Oriented Programming OOP
2 Modules Packages and the Python Standard Library
3 Importing Modules
5 What is Software Documentation
6 The Python Documentation

1 Introduction to the course
3 Introduction to NLP
4 NLP in everyday life
5 Supervised vs unsupervised NLP

1 The importance of data preparation
2 Lowercase
3 Removing stop words
4 Regular expressions
5 Tokenization
6 Stemming
7 Lemmatization
8 N grams
10 Practical task

1 Text tagging
2 Parts of Speech POS tagging
3 Named Entity Recognition NER
5 Practical task

1 What is sentiment analysis
2 Rule based sentiment analysis
3 Pre trained transformer models
5 Practical task

1 Numerical representation of text
2 Bag of Words model
3 TF IDF

1 What is topic modelling
2 When to use topic modelling
3 Latent Dirichlet Allocation LDA
5 LDA in Python
6 Latent Semantic Analysis LSA
7 LSA in Python
8 How many topics

1 Building a custom text classifier
2 Logistic regression
3 Naive Bayes
4 Linear support vector machine

2 Introducing the project
3 Exploring our data through POS tags
4 Extracting named entities
5 Processing the text
6 Does sentiment differ between news types
7 What topics appear in fake news Part 1
8 What topics appear in fake news Part 2
9 Categorizing fake news with a custom classifier

1 What is deep learning
2 Deep learning for NLP
3 Non English NLP
4 Whats next for NLP

1 Introduction to the course
3 What are LLMs
4 How large is an LLM
5 General purpose models
6 Pre training and fine tuning
7 What can LLMs be used for

1 Deep learning recap
2 The problem with RNNs
3 The solution attention is all you need
4 The transformer architecture
5 Input embeddings
6 Multi headed attention
7 Feed forward layer
8 Masked multihead attention
9 Predicting the final outputs

1 What does GPT mean
2 The development of ChatGPT
3 OpenAI API
4 Generating text
5 Customizing GPT output
6 Key word text summarization
7 Coding a simple chatbot
8 Introduction to LangChain in Python
9 LangChain
10 Adding custom data to our chatbot

1 Hugging Face package
2 The transformer pipeline
3 Pre trained tokenizers
4 Special tokens
5 Hugging Face and PyTorchTensorFlow
6 Saving and loading models

1 GPT vs BERT
2 BERT architecture
3 Loading the model and tokenizer
4 BERT embeddings
5 Calculating the response
6 Creating a QA bot
7 BERT RoBERTa DistilBERT

1 GPT vs BERT vs XLNET
3 Preprocessing our data
4 XLNet Embeddings
5 Fine tuning XLNet
6 Evaluating our model

1 Introduction to the course
3 Business applications of LangChain
4 What makes LangChain powerful
5 What does the course cover

1 Tokens
2 Models and Prices

1 Setting up a custom anaconda environment for Jupyter integration
2 Obtaining an OpenAI API key
3 Setting the API key as an environment variable

1 First Steps
2 System user and assistant roles
3 Creating a sarcastic chatbot
4 Temperature max tokens and streaming

1 The LangChain framework
2 ChatOpenAI
3 System and human messages
4 AI messages
5 Prompt templates and prompt values
6 Chat prompt templates and chat prompt values
7 Few shot chat message prompt templates
8 LLMChain

1 Chat message history
2 Conversation buffer memory Implementing the setup
3 Conversation buffer memory Configuring the chain
4 Conversation buffer window memory
5 Conversation summary memory
6 Combined memory

1 String output parser
2 Comma separated list output parser
3 Datetime output parser

1 Piping a prompt model and an output parser
2 Batching
3 Streaming
4 The Runnable and RunnableSequence classes
5 Piping chains and the RunnablePassthrough class
6 Graphing Runnables
7 RunnableParallel
8 Piping a RunnableParallel with other Runnables
9 RunnableLambda
10 The chain decorator
11 Adding memory to a chain Part 1 Implementing the setup
12 RunnablePassthrough with additional keys
13 Itemgetter
14 Adding memory to a chain Part 2 Creating the chain

1 How to integrate custom data into an LLM
2 Introduction to RAG
3 Introduction to document loading and splitting
4 Introduction to document embedding
5 Introduction to document storing retrieval and generation
6 Indexing Document loading with PyPDFLoader
7 Indexing Document loading with Docx2txtLoader
8 Indexing Document splitting with character text splitter Theory
9 Indexing Document splitting with character text splitter Code along
10 Indexing Document splitting with Markdown header text splitter
11 Indexing Text embedding with OpenAI
12 Indexing Creating a Chroma vectorstore
13 Indexing Inspecting and managing documents in a vectorstore
14 Retrieval Similarity search
15 Retrieval Maximal Marginal Relevance MMR search
16 Retrieval Vectorstore backed retriever
17 Generation Stuffing documents
18 Generation Generating a response

1 Introduction to reasoning chatbots
2 Tools toolkits agents and agent executors
4 Creating a Wikipedia tool and piping it to a chain
5 Creating a retriever and a custom tool
6 LangChain hub
7 Creating a tool calling agent and an agent executor
8 AgentAction and AgentFinish

1 Introduction to the course
3 Database comparison SQL NoSQL and Vector
4 Understanding vector databases

1 Introduction to vector space
2 Distance metrics in vector space
3 Vector embeddings walkthrough

1 Vector databases comparison
2 Pinecone registration walkthrough and creating an Index
3 Connecting to Pinecone using Python
5 Creating and deleting a Pinecone index using Python
6 Upserting data to a pinecone vector database
7 Getting to know the fine web data set and loading it to Jupyter
8 Upserting data from a text file and using an embedding algorithm

1 Introduction to semantic search
2 Introduction to the case study smart search for data science courses
3 Getting to know the data for the case study
4 Data loading and preprocessing
5 Pinecone Python APIs and connecting to the Pinecone server
6 Embedding Algorithms
7 Embedding the data and upserting the files to Pinecone
8 Similarity search and querying the data
9 How to update and change your vector database
10 Data preprocessing and embedding for courses with section data
12 Upserting the new updated files to Pinecone
13 Similarity search and querying courses and sections data
15 Using the BERT embedding algorithm
16 Vector database for recommendation engines
17 Vector database for semantic image search
18 Vector database for biomedical research

1 Welcome to the world of Speech Recognition
3 Course Approach
4 How it all started Formants harmonics and phonemes
5 Development and Evolution

1 How do humans recognize speech
2 Fundamentals of sound and sound waves
3 Properties of sound waves

1 Key concepts Sample Rate bit depth and bit rate
2 Audio signal processing for Machine Learning and AI

1 Time domain audio features
2 Frequency domain and time frequency domain audio features
3 Time domain feature extraction Framing and feature computation
4 Frequency domain feature extraction Fourier transform

1 Acoustic and language modeling
2 Hidden Markov Models HMMs and traditional neural networks
3 Deep learning models CNNs RNNs and LSTMs
4 Advanced speech recognition systems Transformers
5 Building a speech recognition model part I
6 Building a speech recognition model part II
7 Selecting the appropriate speech recognition tool

1 Installing Anaconda
2 Setting up a new environment
3 Installing packages for speech recognition
4 Importing the relevant packages in Jupyter

1 Audio file formats for speech recognition
2 Importing audio files in Jupyter Notebook
3 The SpeechRecognition library Google Web Speech API
4 Evaluation metrics WER and CER
5 Calculating WER and CER in Python

1 Understanding noise in audio files
2 Creating a spectrogram with Python
3 Dealing with background noise

1 91Whisper AI Transformer based speech to text
3 Transcribing multiple audio files from a directory
4 Saving audio transcriptions to CSV for easy analysis
5 Reversing the process AI powered text to speech

1 Modern practices and applications
2 Challenges and limitations
3 The future of speech recognition with AI

1 Introduction to the Course
2 What does the course cover
3 The Interview Tools Specifics

1 Hosting an LLM vs Using an API
2 Open Source vs Closed Source Models
3 Tokens
4 Pricing Hosting an LLM vs Pay by Token
5 Initial Prompt Development Part 1
6 Initial Prompt Development Part 2
7 Database Design and Schema Development
8 What Is an Activity Diagram
9 Creating an Activity Diagram
10 Concluding the Planning Stage

1 The OpenAI Playground
2 Optimizing Temperature and Top P for Different Use Cases
3 Prompt Engineering for Software Development
4 How to Test Out a Prompt Template

1 Setting up environment
2 Streamlits Pros and Cons
3 Streamlit Elements Titles Headers and Formatting
4 Streamlit Elements Text Methods
5 Streamlit Elements Chat Elements
6 Sessin State

1 Initializing an OpenAI Client
2 Implementing the Chat Functionality
3 Building the Setup Page
4 Enhancing Chatbot Interaction with Session State
5 Refining Our Project
6 Implementing Feedback Functionality Part 1
7 Implementing Feedback Functionality Part 2
8 Uploading Your Project in GitHub
9 Deploying Your Streamlit App

Udemy - The AI Engineer Course 2025 Complete AI Engineer Bootcamp 2025-1

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Udemy - The AI Engineer Course 2025 Complete AI Engineer Bootcamp 2025-1

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