The AI Engineer Course 2024 – Complete AI Engineer Bootcamp

What you'll learn
The course provides the entire toolbox you need to become an AI Engineer
Understand key Artificial Intelligence concepts and build a solid foundation
Start coding in Python and learn how to use it for NLP and AI
Impress interviewers by showing an understanding of the AI field
Apply your skills to real-life business cases
Harness the power of Large Language Models
Leverage LangChain for seamless development of AI-driven applications by chaining interoperable components
Become familiar with Hugging Face and the AI tools it offers
Use APIs and connect to powerful foundation models
Requirements
No prior experience is required. We will start from the very basics
You'll need to install Anaconda. We will show you how to do that step by step
Description
The ProblemAI Engineers are best suited to thrive in the age of AI. It helps businesses utilize Generative AI by building AI-driven applications on top of their existing websites, apps, and databases. Therefore, it's no surprise that the demand for AI Engineers has been surging in the job marketplace.Supply, however, has been minimal, and acquiring the skills necessary to be hired as an AI Engineer can be challenging.So, how is this achievable?Universities have been slow to create specialized programs focused on practical AI Engineering skills. The few attempts that exist tend to be costly and time-consuming.Most online courses offer ChatGPT hacks and isolated technical skills, yet integrating these skills remains challenging.The SolutionAI Engineering is a multidisciplinary field covering:AI principles and practical applicationsPython programmingNatural Language Processing in PythonLarge Language Models and TransformersDeveloping apps with orchestration tools like LangChainVector databases using PineConeCreating AI-driven applicationsEach topic builds on the previous one, and skipping steps can lead to confusion. For instance, applying large language models requires familiarity with Langchain—just as studying natural language processing can be overwhelming without basic Python coding skills.So, we created the AI Engineer Bootcamp 2024 to provide the most effective, time-efficient, and structured AI engineering training available online.This pioneering training program overcomes the most significant barrier to entering the AI Engineering field by consolidating all essential resources in one place.Our course is designed to teach interconnected topics seamlessly—providing all you need to become an AI Engineer at a significantly lower cost and time investment than traditional programs.The Skills1. Intro to Artificial IntelligenceStructured and unstructured data, supervised and unsupervised machine learning, Generative AI, and foundational models—these familiar AI buzzwords; what exactly do they mean?Why study AI? Gain deep insights into the field through a guided exploration that covers AI fundamentals, the significance of quality data, essential techniques, Generative AI, and the development of advanced models like GPT, Llama, Gemini, and Claude.2. Python ProgrammingMastering Python programming is essential to becoming a skilled AI developer—no-code tools are insufficient.Python is a modern, general-purpose programming language suited for creating web applications, computer games, and data science tasks. Its extensive library ecosystem makes it ideal for developing AI models.Why study Python programming?Python programming will become your essential tool for communicating with AI models and integrating their capabilities into your products.3. Intro to NLP in PythonExplore Natural Language Processing (NLP) and learn techniques that empower computers to comprehend, generate, and categorize human language.Why study NLP?NLP forms the basis of cutting-edge Generative AI models. This program equips you with essential skills to develop AI systems that meaningfully interact with human language.4. Introduction to Large Language ModelsThis program section enhances your natural language processing skills by teaching you to utilize the powerful capabilities of Large Language Models (LLMs). Learn critical tools like Transformers Architecture, GPT, Langchain, HuggingFace, BERT, and XLNet.Why study LLMs?This module is your gateway to understanding how large language models work and how they can be applied to solve complex language-related tasks that require deep contextual understanding.5. Building Applications with LangChainLangChain is a framework that allows for seamless development of AI-driven applications by chaining interoperable components.Why study LangChain?Learn how to create applications that can reason. LangChain facilitates the creation of systems where individual pieces—such as language models, databases, and reasoning algorithms—can be interconnected to enhance overall functionality.6. Vector DatabasesWith emerging AI technologies, the importance of vectorization and vector databases is set to increase significantly. In this Vector Databases with Pinecone module, you'll have the opportunity to explore the Pinecone database—a leading vector database solution.Why study vector databases?Learning about vector databases is crucial because it equips you to efficiently manage and query large volumes of high-dimensional data—typical in machine learning and AI applications. These technical skills allow you to deploy performance-optimized AI-driven applications.What You Get$1,250 AI Engineering training programActive Q&A supportEssential skills for AI engineering employmentAI learner community accessCompletion certificateFuture updatesReal-world business case solutions for job readinessWe're excited to help you become an AI Engineer from scratch—offering an unconditional 30-day full money-back guarantee.With excellent course content and no risk involved, we're confident you'll love it.Why delay? Each day is a lost opportunity. Click the 'Buy Now' button and join our AI Engineer program today.
Overview
Section 1: Intro to AI Module: Getting started
Lecture 1 What does the course cover
Lecture 2 Natural vs Artificial Intelligence
Lecture 3 Brief history of AI
Lecture 4 Demystifying AI, Data science, Machine learning, and Deep learning
Lecture 5 Weak vs Strong AI
Section 2: Intro to AI Module: Data is essential for building AI
Lecture 6 Structured vs unstructured data
Lecture 7 How we collect data
Lecture 8 Labelled and unlabelled data
Lecture 9 Metadаta: Data that describes data
Section 3: Intro to AI Module: Key AI techniques
Lecture 10 Machine learning
Lecture 11 Supervised, Unsupervised, and Reinforcement learning
Lecture 12 Deep learning
Section 4: Intro to AI Module: Important AI branches
Lecture 13 Robotics
Lecture 14 Computer vision
Lecture 15 Traditional ML
Lecture 16 Generative AI
Section 5: Intro to AI Module: Understanding Generative AI
Lecture 17 The rise of Gen AI: Introducing ChatGPT
Lecture 18 Early approaches to Natural Language Processing (NLP)
Lecture 19 Recent NLP advancements
Lecture 20 From Language Models to Large Language Models (LLMs)
Lecture 21 The efficiency of LLM training. Supervised vs Semi-supervised learning
Lecture 22 From N-Grams to RNNs to Transformers: The Evolution of NLP
Lecture 23 Phases in building LLMs
Lecture 24 Prompt engineering vs Fine-tuning vs RAG: Techniques for AI optimization
Lecture 25 The importance of foundation models
Lecture 26 Buy vs Make: foundation models vs private models
Section 6: Intro to AI Module: Practical challenges in Generative AI
Lecture 27 Inconsistency and hallucination
Lecture 28 Budgeting and API costs
Lecture 29 Latency
Lecture 30 Running out of data
Section 7: Intro to AI Module: The AI tech stack
Lecture 31 Python programming
Lecture 32 Working with APIs
Lecture 33 Vector databases
Lecture 34 The importance of open source
Lecture 35 Hugging Face
Lecture 36 LangChain
Lecture 37 AI evaluation tools
Section 8: AI job positions
Lecture 38 AI strategist
Lecture 39 AI developer
Lecture 40 AI engineer
Section 9: Looking ahead
Lecture 41 AI ethics
Lecture 42 Future of AI
Section 10: Python Module: Why Python?
Lecture 43 Programming Explained in a Few Minutes
Lecture 44 Why Python
Section 11: Python Module: Setting Up the Environment
Lecture 45 Jupyter - Introduction
Lecture 46 Jupyter - Installing Anaconda
Lecture 47 Jupyter - Introduction to Using Jupyter
Lecture 48 Jupyter - Working with Notebook Files
Lecture 49 Jupyter - Using Shortcuts
Lecture 50 Jupyter - Handling Error Messages
Lecture 51 Jupyter - Restarting the Kernel
Section 12: Python Module: Python Variables and Data Types
Lecture 52 Python Variables
Lecture 53 Types of Data - Numbers and Boolean Values
Lecture 54 Types of Data - Strings
Section 13: Python Module: Basic Python Syntax
Lecture 55 Basic Python Syntax - Arithmetic Operators
Lecture 56 Basic Python Syntax - The Double Equality Sign
Lecture 57 Basic Python Syntax - Reassign Values
Lecture 58 Basic Python Syntax - Add Comments
Lecture 59 Basic Python Syntax - Line Continuation
Lecture 60 Basic Python Syntax - Indexing Elements
Lecture 61 Basic Python Syntax - Indentation
Section 14: Python Module: More on Operators
Lecture 62 Operators - Comparison Operators
Lecture 63 Operators - Logical and Identity Operators
Section 15: Python Module: Conditional Statements
Lecture 64 Conditional Statements - The IF Statement
Lecture 65 Conditional Statements - The ELSE Statement
Lecture 66 Conditional Statements - The ELIF Statement
Lecture 67 Conditional Statements - A Note on Boolean Values
Section 16: Python Module: Functions
Lecture 68 Functions - Defining a Function in Python
Lecture 69 Functions - Creating a Function with a Parameter
Lecture 70 Functions - Another Way to Define a Function
Lecture 71 Functions - Using a Function in Another Function
Lecture 72 Functions - Combining Conditional Statements and Functions
Lecture 73 Functions - Creating Functions Containing a Few Arguments
Lecture 74 Functions - Notable Built-in Functions in Python
Section 17: Python Module: Sequences
Lecture 75 Sequences - Lists
Lecture 76 Sequences - Using Methods
Lecture 77 Sequences - List Slicing
Lecture 78 Sequences - Tuples
Lecture 79 Sequences - Dictionaries
Section 18: Python Module: Iteration
Lecture 80 Iteration - For Loops
Lecture 81 Iteration - While Loops and Incrementing
Lecture 82 Iteration - Creatie Lists with the range() Function
Lecture 83 Iteraion - Use Conditional Statements and Loops Together
Lecture 84 Iteration - Conditional Statements, Functions, and Loops
Lecture 85 Iteration - Iterating over Dictionaries
Section 19: Python Module: A Few Important Python Concepts and Terms
Lecture 86 Introduction to Object Oriented Programming (OOP)
Lecture 87 Modules, Packages, and the Python Standard Library
Lecture 88 Importing Modules
Lecture 89 What is Software Documentation
Lecture 90 The Python Documentation
Section 20: NLP Module: Introduction
Lecture 91 Introduction to the course
Lecture 92 Course materials and notebooks
Lecture 93 Introduction to NLP
Lecture 94 NLP in everyday life
Lecture 95 Supervised vs unsupervised NLP
Section 21: NLP Module: Text Preprocessing
Lecture 96 The importance of data preparation
Lecture 97 Lowercase
Lecture 98 Removing stop words
Lecture 99 Regular expressions
Lecture 100 Tokenization
Lecture 101 Stemming
Lecture 102 Lemmatization
Lecture 103 N-grams
Lecture 104 Practical task
Section 22: NLP Module: Identifying Parts of Speech and Named Entities
Lecture 105 Text tagging
Lecture 106 Parts of Speech (POS) tagging
Lecture 107 Named Entity Recognition (NER)
Lecture 108 Practical task
Section 23: NLP Module: Sentiment Analysis
Lecture 109 What is sentiment analysis?
Lecture 110 Rule-based sentiment analysis
Lecture 111 Pre-trained transformer models
Lecture 112 Practical task
Section 24: NLP Module: Vectorizing Text
Lecture 113 Numerical representation of text
Lecture 114 Bag of Words model
Lecture 115 TF-IDF
Section 25: NLP Module: Topic Modelling
Lecture 116 What is topic modelling?
Lecture 117 When to use topic modelling?
Lecture 118 Latent Dirichlet Allocation (LDA)
Lecture 119 LDA in Python
Lecture 120 Latent Semantic Analysis (LSA)
Lecture 121 LSA in Python
Lecture 122 How many topics?
Section 26: NLP Module: Building Your Own Text Classifier
Lecture 123 Building a custom text classifier
Lecture 124 Logistic regression
Lecture 125 Naive Bayes
Lecture 126 Linear support vector machine
Section 27: NLP Module: Categorizing Fake News (Case Study)
Lecture 127 Introducing the project
Lecture 128 Exploring our data through POS tags
Lecture 129 Extracting named entities
Lecture 130 Processing the text
Lecture 131 Does sentiment differ between news types?
Lecture 132 What topics appear in fake news? (Part 1)
Lecture 133 What topics appear in fake news? (Part 2)
Lecture 134 Categorizing fake news with a custom classifier
Section 28: NLP Module: The Future of NLP
Lecture 135 What is deep learning?
Lecture 136 Deep learning for NLP
Lecture 137 Non-English NLP
Lecture 138 What's next for NLP?
Section 29: LLMs Module: Introduction to Large Language Models
Lecture 139 Introduction to the course
Lecture 140 Course materials and notebooks
Lecture 141 What are LLMs?
Lecture 142 How large is an LLM?
Lecture 143 General purpose models
Lecture 144 Pre-training and fine tuning
Lecture 145 What can LLMs be used for?
Section 30: LLMs Module: The Transformer Architecture
Lecture 146 Deep learning recap
Lecture 147 The problem with RNNs
Lecture 148 The solution: attention is all you need
Lecture 149 The transformer architecture
Lecture 150 Input embeddings
Lecture 151 Multi-headed attention
Lecture 152 Feed-forward layer
Lecture 153 Masked multihead attention
Lecture 154 Predicting the final outputs
Section 31: LLMs Module: Getting Started With GPT Models
Lecture 155 What does GPT mean?
Lecture 156 The development of ChatGPT
Lecture 157 OpenAI API
Lecture 158 Generating text
Lecture 159 Customizing GPT output
Lecture 160 Key word text summarization
Lecture 161 Coding a simple chatbot
Lecture 162 Introduction to LangChain in Python
Lecture 163 LangChain
Lecture 164 Adding custom data to our chatbot
Section 32: LLMs Module: Hugging Face Transformers
Lecture 165 Hugging Face package
Lecture 166 The transformer pipeline
Lecture 167 Pre-trained tokenizers
Lecture 168 Special tokens
Lecture 169 Hugging Face and PyTorch/TensorFlow
Lecture 170 Saving and loading models
Section 33: LLMs Module: Question and Answer Models With BERT
Lecture 171 GPT vs BERT
Lecture 172 BERT architecture
Lecture 173 Loading the model and tokenizer
Lecture 174 BERT embeddings
Lecture 175 Calculating the response
Lecture 176 Creating a QA bot
Lecture 177 BERT, RoBERTa, DistilBERT
Section 34: LLMs Module: Text Classification With XLNet
Lecture 178 GPT vs BERT vs XLNET
Lecture 179 Preprocessing our data
Lecture 180 XLNet Embeddings
Lecture 181 Fine tuning XLNet
Lecture 182 Evaluating our model
Section 35: LangChain Module: Introduction
Lecture 183 Introduction to the course
Lecture 184 Business applications of LangChain
Lecture 185 What makes LangChain powerful?
Lecture 186 What does the course cover?
Section 36: LangChain Module: Tokens, Models, and Prices
Lecture 187 Tokens
Lecture 188 Models and Prices
Section 37: LangChain Module: Setting Up the Environment
Lecture 189 Setting up a custom anaconda environment for Jupyter integration
Lecture 190 Obtaining an OpenAI API key
Lecture 191 Setting the API key as an environment variable
Section 38: LangChain Module: The OpenAI API
Lecture 192 First steps
Lecture 193 System, user, and assistant roles
Lecture 194 Creating a sarcastic chatbot
Lecture 195 Temperature, max tokens, and streaming
Section 39: LangChain Module: Model Inputs
Lecture 196 The LangChain framework
Lecture 197 ChatOpenAI
Lecture 198 System and human messages
Lecture 199 AI messages
Lecture 200 Prompt templates and prompt values
Lecture 201 Chat prompt templates and chat prompt values
Lecture 202 Few-shot chat message prompt templates
Lecture 203 LLMChain
Section 40: LangChain Module: Message History and Chatbot Memory
Lecture 204 Chat message history
Lecture 205 Conversation buffer memory: Implementing the setup
Lecture 206 Conversation buffer memory: Configuring the chain
Lecture 207 Conversation buffer window memory
Lecture 208 Conversation summary memory
Lecture 209 Combined memory
Section 41: LangChain Module: Output Parsers
Lecture 210 String output parser
Lecture 211 Comma-separated list output parser
Lecture 212 Datetime output parser
Section 42: LangChain Module: LangChain Expression Language (LCEL)
Lecture 213 Piping a prompt, model, and an output parser
Lecture 214 Batching
Lecture 215 Streaming
Lecture 216 The Runnable and RunnableSequence classes
Lecture 217 Piping chains and the RunnablePassthrough class
Lecture 218 Graphing Runnables
Lecture 219 RunnableParallel
Lecture 220 Piping a RunnableParallel with other Runnables
Lecture 221 RunnableLambda
Lecture 222 The @chain decorator
Lecture 223 Adding memory to a chain (Part 1): Implementing the setup
Lecture 224 RunnablePassthrough with additional keys
Lecture 225 Itemgetter
Lecture 226 Adding memory to a chain (Part 2): Creating the chain
Section 43: LangChain Module: Retrieval Augmented Generation (RAG)
Lecture 227 How to integrate custom data into an LLM
Lecture 228 Introduction to RAG
Lecture 229 Introduction to document loading and splitting
Lecture 230 Introduction to document embedding
Lecture 231 Introduction to document storing, retrieval, and generation
Lecture 232 Indexing: Document loading with PyPDFLoader
Lecture 233 Indexing: Document loading with Docx2txtLoader
Lecture 234 Indexing: Document splitting with character text splitter (Theory)
Lecture 235 Indexing: Document splitting with character text splitter (Code along)
Lecture 236 Indexing: Document splitting with Markdown header text splitter
Lecture 237 Indexing: Text embedding with OpenAI
Lecture 238 Indexing: Creating a Chroma vectorstore
Lecture 239 Indexing: Inspecting and managing documents in a vectorstore
Lecture 240 Retrieval: Similarity search
Lecture 241 Retrieval: Maximal Marginal Relevance (MMR) search
Lecture 242 Retrieval: Vectorstore-backed retriever
Lecture 243 Generation: Stuffing documents
Lecture 244 Generation: Generating a response
Section 44: LangChain Module: Tools and Agents
Lecture 245 Introduction to reasoning chatbots
Lecture 246 Tools, toolkits, agents, and agent executors
Lecture 247 Fixing the GuessedAtParserWarning
Lecture 248 Creating a Wikipedia tool and piping it to a chain
Lecture 249 Creating a retriever and a custom tool
Lecture 250 LangChain hub
Lecture 251 Creating a tool calling agent and an agent executor
Lecture 252 AgentAction and AgentFinish
Section 45: Vector Databases Module: Introduction
Lecture 253 Introduction to the course
Lecture 254 Database comparison: SQL, NoSQL, and Vector
Lecture 255 Understanding vector databases
Section 46: Vector Databases Module: Basics of Vector Space and High-Dimensional Data
Lecture 256 Introduction to vector space
Lecture 257 Distance metrics in vector space
Lecture 258 Vector embeddings walkthrough
Section 47: Vector Databases Module: Introduction to The Pinecone Vector Database
Lecture 259 Vector databases, comparison
Lecture 260 Pinecone registration, walkthrough and creating an Index
Lecture 261 Connecting to Pinecone using Python
Lecture 262 Assignment
Lecture 263 Creating and deleting a Pinecone index using Python
Lecture 264 Upserting data to a pinecone vector database
Lecture 265 Getting to know the fine web data set and loading it to Jupyter
Lecture 266 Upserting data from a text file and using an embedding algorithm
Section 48: Vector Databases Module: Semantic Search with Pinecone and Custom (Case Study)
Lecture 267 Introduction to semantic search
Lecture 268 Introduction to the case study – smart search for data science courses
Lecture 269 Getting to know the data for the case study
Lecture 270 Data loading and preprocessing
Lecture 271 Pinecone Python APIs and connecting to the Pinecone server
Lecture 272 Embedding Algorithms
Lecture 273 Embedding the data and upserting the files to Pinecone
Lecture 274 Similarity search and querying the data
Lecture 275 How to update and change your vector database
Lecture 276 Data preprocessing and embedding for courses with section data
Lecture 277 Assignment 2
Lecture 278 Upserting the new updated files to Pinecone
Lecture 279 Similarity search and querying courses and sections data
Lecture 280 Assignment 3
Lecture 281 Using the BERT embedding algorithm
Lecture 282 Vector database for recommendation engines
Lecture 283 Vector database for semantic image search
Lecture 284 Vector database for biomedical research
You should take this course if you want to become an AI Engineer or if you want to learn about the field,This course is for you if you want a great career,The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills
Homepage

https://www.udemy.com/course/the-ai-engineer-course-complete-ai-engineer-bootcamp/

No Password - Links are Interchangeable