Ai Engineering Masterclass: From Zero To Ai Hero

Ai Engineering Masterclass: From Zero To Ai Hero
Published 12/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

Master AI Engineering: Build, Train, and Deploy Scalable AI Solutions with Real-World Projects and Hands-On Learning.

What you’ll learn

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

Requirements

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.

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.

Description

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 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 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 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