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+--- Thread: Ai Engineering Masterclass: From Zero To Ai Hero (/Thread-Ai-Engineering-Masterclass-From-Zero-To-Ai-Hero)
Ai Engineering Masterclass: From Zero To Ai Hero - AD-TEAM - 12-29-2024
![[Image: 24e9083964f5fe477ae27c161a4ca900.jpg]](https://i124.fastpic.org/big/2024/1229/00/24e9083964f5fe477ae27c161a4ca900.jpg)
Ai Engineering Masterclass: From Zero To Ai Hero
Published 12/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 17.39 GB | Duration: 31h 9m
Master AI Engineering: Build, Train, and Deploy Scalable AI Solutions with Real-World Projects and Hands-On Learning.
[b]What you'll learn[/b]
Build AI models using Python, TensorFlow, and PyTorch to create intelligent systems capable of solving real-world problems
Preprocess, clean, and analyze complex datasets to ensure high-quality input for machine learning and AI model training
Train, evaluate, and optimize machine learning models for tasks like regression, classification, and clustering
Design, implement, and fine-tune neural networks, including CNNs and RNNs, for advanced AI applications
Apply Natural Language Processing (NLP) techniques to analyze, interpret, and generate human-like text data
Leverage transfer learning to adapt pre-trained AI models for new tasks, reducing development time and resources
Deploy AI models using scalable APIs and containerization tools like Docker for seamless integration into applications
Monitor AI model performance, detect data drift, and establish retraining workflows for consistent reliability
Solve real-world business and technical challenges using AI-driven approaches and intelligent systems
Develop end-to-end AI projects, from ideation and prototyping to deployment and long-term maintenance
[b]Requirements[/b]
Basic Programming Knowledge: Familiarity with Python is recommended but not mandatory.
Curiosity and Enthusiasm: A passion for AI and a willingness to learn are essential.
Access to a Computer: A computer with internet access and sufficient processing power for AI tasks.
No Prior AI Experience Required: The course starts from foundational concepts and gradually advances.
Basic Math Skills: Understanding high school-level math concepts (e.g., algebra, basic statistics).
A Stable Internet Connection: For accessing course materials, tools, and hands-on projects.
Optional Tools: Installation of Python, Jupyter Notebook, and relevant AI libraries (guidance provided in the course).
Open Mindset: Be ready to explore, experiment, and build real-world AI applications.
[b]Description[/b]
In today's fast-paced digital world, Artificial Intelligence (AI) is revolutionizing industries, driving innovation, and transforming how businesses operate. The AI Engineering Complete Bootcamp Masterclass is a comprehensive course designed to equip you with the skills, knowledge, and hands-on experience needed to excel in the dynamic field of AI. Whether you're an aspiring AI engineer, a data scientist looking to expand your toolkit, or a professional eager to integrate AI solutions into your workflow, this course offers a structured and engaging learning path tailored for all experience levels.This bootcamp starts from the very basics, ensuring that even beginners can follow along. You'll begin with Python programming, the most widely used language in AI and machine learning, and learn how to preprocess and clean data effectively. As you progress, you'll dive deep into essential machine learning algorithms, exploring regression, classification, and clustering techniques. The course also covers advanced topics like neural networks, deep learning frameworks, and natural language processing (NLP), equipping you with the tools to tackle real-world AI challenges.One of the key highlights of this course is its focus on practical application and real-world projects. You'll work on hands-on projects using popular AI libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face. From building image recognition models with Convolutional Neural Networks (CNNs) to creating text-based chatbots with Natural Language Processing (NLP), every module includes actionable tasks that reinforce your understanding.Deployment and scalability are also crucial components of this course. You'll learn how to deploy machine learning models using APIs, Docker containers, and cloud services, ensuring your AI solutions are not only functional but also scalable and production-ready. The course also covers essential skills like model monitoring, data drift detection, and retraining workflows to maintain long-term AI performance.This masterclass isn't just about theory-it's about empowering you to build, test, and deploy real AI solutions. You'll be equipped to bridge the gap between AI research and application, enabling you to contribute effectively in professional environments.By the end of this course, you'll have a portfolio of AI projects, a deep understanding of core concepts, and the confidence to tackle AI engineering challenges head-on. Whether you're building intelligent chatbots, predictive analytics tools, or AI-powered recommendation systems, the skills you acquire here will set you apart in the competitive AI job market.If you're ready to future-proof your career, unlock exciting opportunities, and become an AI innovator, this course is your launchpad. Enroll now and take the first step towards mastering AI engineering!
Overview
Section 1: Week 1: Python Programming Basics
Lecture 1 Introduction to Week 1 Python Programming Basics
Lecture 2 Day 1: Introduction to Python and Development Setup
Lecture 3 Day 2: Control Flow in Python
Lecture 4 Day 3: Functions and Modules
Lecture 5 Day 4: Data Structures (Lists, Tuples, Dictionaries, Sets)
Lecture 6 Day 5: Working with Strings
Lecture 7 Day 6: File Handling
Lecture 8 Day 7: Pythonic Code and Project Work
Section 2: Week 2: Data Science Essentials
Lecture 9 Introduction to Week 2 Data Science Essentials
Lecture 10 Day 1: Introduction to NumPy for Numerical Computing
Lecture 11 Day 2: Advanced NumPy Operations
Lecture 12 Day 3: Introduction to Pandas for Data Manipulation
Lecture 13 Day 4: Data Cleaning and Preparation with Pandas
Lecture 14 Day 5: Data Aggregation and Grouping in Pandas
Lecture 15 Day 6: Data Visualization with Matplotlib and Seaborn
Lecture 16 Day 7: Exploratory Data Analysis (EDA) Project
Section 3: Week 3: Mathematics for Machine Learning
Lecture 17 Introduction to Week 3 Mathematics for Machine Learning
Lecture 18 Day 1: Linear Algebra Fundamentals
Lecture 19 Day 2: Advanced Linear Algebra Concepts
Lecture 20 Day 3: Calculus for Machine Learning (Derivatives)
Lecture 21 Day 4: Calculus for Machine Learning (Integrals and Optimization)
Lecture 22 Day 5: Probability Theory and Distributions
Lecture 23 Day 6: Statistics Fundamentals
Lecture 24 Day 7: Math-Driven Mini Project - Linear Regression from Scratch
Section 4: Week 4: Probability and Statistics for Machine Learning
Lecture 25 Introduction to Week 4 Probability and Statistics for Machine Learning
Lecture 26 Day 1: Probability Theory and Random Variables
Lecture 27 Day 2: Probability Distributions in Machine Learning
Lecture 28 Day 3: Statistical Inference - Estimation and Confidence Intervals
Lecture 29 Day 4: Hypothesis Testing and P-Values
Lecture 30 Day 5: Types of Hypothesis Tests
Lecture 31 Day 6: Correlation and Regression Analysis
Lecture 32 Day 7: Statistical Analysis Project - Analyzing Real-World Data
Section 5: Week 5: Introduction to Machine Learning
Lecture 33 Introduction to Week 5 Introduction to Machine Learning
Lecture 34 Day 1: Machine Learning Basics and Terminology
Lecture 35 Day 2: Introduction to Supervised Learning and Regression Models
Lecture 36 Day 3: Advanced Regression Models - Polynomial Regression and Regularization
Lecture 37 Day 4: Introduction to Classification and Logistic Regression
Lecture 38 Day 5: Model Evaluation and Cross-Validation
Lecture 39 Day 6: k-Nearest Neighbors (k-NN) Algorithm
Lecture 40 Day 7: Supervised Learning Mini Project
Section 6: Week 6: Feature Engineering and Model Evaluation
Lecture 41 Introduction to Week 6 Feature Engineering and Model Evaluation
Lecture 42 Day 1: Introduction to Feature Engineering
Lecture 43 Day 2: Data Scaling and Normalization
Lecture 44 Day 3: Encoding Categorical Variables
Lecture 45 Day 4: Feature Selection Techniques
Lecture 46 Day 5: Creating and Transforming Features
Lecture 47 Day 6: Model Evaluation Techniques
Lecture 48 Day 7: Cross-Validation and Hyperparameter Tuning
Section 7: Week 7: Advanced Machine Learning Algorithms
Lecture 49 Introduction to Week 7 Advanced Machine Learning Algorithms
Lecture 50 Day 1: Introduction to Ensemble Learning
Lecture 51 Day 2: Bagging and Random Forests
Lecture 52 Day 3: Boosting and Gradient Boosting
Lecture 53 Day 4: Introduction to XGBoost
Lecture 54 Day 5: LightGBM and CatBoost
Lecture 55 Day 6: Handling Imbalanced Data
Lecture 56 Day 7: Ensemble Learning Project - Comparing Models on a Real Dataset
Section 8: Week 8: Model Tuning and Optimization
Lecture 57 Introduction to Week 8 Model Tuning and Optimization
Lecture 58 Day 1: Introduction to Hyperparameter Tuning
Lecture 59 Day 2: Grid Search and Random Search
Lecture 60 Day 3: Advanced Hyperparameter Tuning with Bayesian Optimization
Lecture 61 Day 4: Regularization Techniques for Model Optimization
Lecture 62 Day 5: Cross-Validation and Model Evaluation Techniques
Lecture 63 Day 6: Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearchCV
Lecture 64 Day 7: Optimization Project - Building and Tuning a Final Model
Section 9: Week 9: Neural Networks and Deep Learning Fundamentals
Lecture 65 Introduction to Week 9 Neural Networks and Deep Learning Fundamentals
Lecture 66 Day 1: Introduction to Deep Learning and Neural Networks
Lecture 67 Day 2: Forward Propagation and Activation Functions
Lecture 68 Day 3: Loss Functions and Backpropagation
Lecture 69 Day 4: Gradient Descent and Optimization Techniques
Lecture 70 Day 5: Building Neural Networks with TensorFlow and Keras
Lecture 71 Day 6: Building Neural Networks with PyTorch
Lecture 72 Day 7: Neural Network Project - Image Classification on CIFAR-10
Section 10: Week 10: Convolutional Neural Networks (CNNs)
Lecture 73 Introduction to Week 10 Convolutional Neural Networks (CNNs)
Lecture 74 Day 1: Introduction to Convolutional Neural Networks
Lecture 75 Day 2: Convolutional Layers and Filters
Lecture 76 Day 3: Pooling Layers and Dimensionality Reduction
Lecture 77 Day 4: Building CNN Architectures with Keras and TensorFlow
Lecture 78 Day 5: Building CNN Architectures with PyTorch
Lecture 79 Day 6: Regularization and Data Augmentation for CNNs
Lecture 80 Day 7: CNN Project - Image Classification on Fashion MNIST or CIFAR-10
Section 11: Week 11: Recurrent Neural Networks (RNNs) and Sequence Modeling
Lecture 81 Introduction to Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling
Lecture 82 Day 1: Introduction to Sequence Modeling and RNNs
Lecture 83 Day 2: Understanding RNN Architecture and Backpropagation Through Time (BPTT)
Lecture 84 Day 3: Long Short-Term Memory (LSTM) Networks
Lecture 85 Day 4: Gated Recurrent Units (GRUs)
Lecture 86 Day 5: Text Preprocessing and Word Embeddings for RNNs
Lecture 87 Day 6: Sequence-to-Sequence Models and Applications
Lecture 88 Day 7: RNN Project - Text Generation or Sentiment Analysis
Section 12: Week 12: Transformers and Attention Mechanisms
Lecture 89 Introduction to Week 12 Transformers and Attention Mechanisms
Lecture 90 Day 1: Introduction to Attention Mechanisms
Lecture 91 Day 2: Introduction to Transformers Architecture
Lecture 92 Day 3: Self-Attention and Multi-Head Attention in Transformers
Lecture 93 Day 4: Positional Encoding and Feed-Forward Networks
Lecture 94 Day 5: Hands-On with Pre-Trained Transformers - BERT and GPT
Lecture 95 Day 6: Advanced Transformers - BERT Variants and GPT-3
Lecture 96 Day 7: Transformer Project - Text Summarization or Translation
Section 13: Week 13: Transfer Learning and Fine-Tuning
Lecture 97 Introduction to Week 13 Transfer Learning and Fine-Tuning
Lecture 98 Day 1: Introduction to Transfer Learning
Lecture 99 Day 2: Transfer Learning in Computer Vision
Lecture 100 Day 3: Fine-Tuning Techniques in Computer Vision
Lecture 101 Day 4: Transfer Learning in NLP
Lecture 102 Day 5: Fine-Tuning Techniques in NLP
Lecture 103 Day 6: Domain Adaptation and Transfer Learning Challenges
Lecture 104 Day 7: Transfer Learning Project - Fine-Tuning for a Custom Task
Aspiring AI Engineers: Individuals looking to kickstart a career in AI with hands-on skills and real-world projects.,Data Scientists & Analysts: Professionals aiming to expand their expertise with AI model building and deployment.,Software Developers: Programmers eager to integrate AI capabilities into their applications and systems.,Career Changers: Individuals from non-technical backgrounds ready to transition into the AI industry.,Graduate Students: Students in data science, computer science, or related fields seeking practical AI knowledge.,Tech Entrepreneurs: Founders and CTOs exploring AI for product innovation and business growth.,AI Enthusiasts: Anyone passionate about AI and looking to build intelligent systems from scratch.,Business Professionals: Leaders aiming to understand AI for strategic decision-making and organizational growth.
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