Artificial Intelligence Mastery Complete AI

Course content

Sections: 27

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Activities: 0

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Resources: 284

1 Introduction to Week 1 Python Programming Basics
2 Day 1 Introduction to Python and Development Setup
3 Day 2 Control Flow in Python
4 Day 3 Functions and Modules
5 Day 4 Data Structures Lists Tuples Dictionaries Sets
6 Day 5 Working with Strings
7 Day 6 File Handling
8 Day 7 Pythonic Code and Project Work

1 Introduction to Week 2 Data Science Essentials
2 Day 1 Introduction to NumPy for Numerical Computing
3 Day 2 Advanced NumPy Operations
4 Day 3 Introduction to Pandas for Data Manipulation
5 Day 4 Data Cleaning and Preparation with Pandas
6 Day 5 Data Aggregation and Grouping in Pandas
7 Day 6 Data Visualization with Matplotlib and Seaborn
8 Day 7 Exploratory Data Analysis EDA Project

1 Introduction to Week 3 Mathematics for Machine Learning
2 Day 1 Linear Algebra Fundamentals
3 Day 2 Advanced Linear Algebra Concepts
4 Day 3 Calculus for Machine Learning Derivatives
5 Day 4 Calculus for Machine Learning Integrals and Optimization
6 Day 5 Probability Theory and Distributions
7 Day 6 Statistics Fundamentals
8 Day 7 Math Driven Mini Project Linear Regression from Scratch

1 Introduction to Week 4 Probability and Statistics for Machine Learning
2 Day 1 Probability Theory and Random Variables
3 Day 2 Probability Distributions in Machine Learning
4 Day 3 Statistical Inference Estimation and Confidence Intervals
5 Day 4 Hypothesis Testing and P Values
6 Day 5 Types of Hypothesis Tests
7 Day 6 Correlation and Regression Analysis
8 Day 7 Statistical Analysis Project Analyzing Real World Data

1 Introduction to Week 5 Introduction to Machine Learning
2 Day 1 Machine Learning Basics and Terminology
3 Day 2 Introduction to Supervised Learning and Regression Models
4 Day 3 Advanced Regression Models Polynomial Regression and Regularization
5 Day 4 Introduction to Classification and Logistic Regression
6 Day 5 Model Evaluation and Cross Validation
7 Day 6 k Nearest Neighbors k NN Algorithm
8 Day 7 Supervised Learning Mini Project

1 Introduction to Week 6 Feature Engineering and Model Evaluation
2 Day 1 Introduction to Feature Engineering
3 Day 2 Data Scaling and Normalization
4 Day 3 Encoding Categorical Variables
5 Day 4 Feature Selection Techniques
6 Day 5 Creating and Transforming Features
7 Day 6 Model Evaluation Techniques
8 Day 7 Cross Validation and Hyperparameter Tuning

1 Introduction to Week 7 Advanced Machine Learning Algorithms
2 Day 1 Introduction to Ensemble Learning
3 Day 2 Bagging and Random Forests
4 Day 3 Boosting and Gradient Boosting
5 Day 4 Introduction to XGBoost
6 Day 5 LightGBM and CatBoost
7 Day 6 Handling Imbalanced Data
8 Day 7 Ensemble Learning Project Comparing Models on a Real Dataset

1 Introduction to Week 8 Model Tuning and Optimization
2 Day 1 Introduction to Hyperparameter Tuning
3 Day 2 Grid Search and Random Search
4 Day 3 Advanced Hyperparameter Tuning with Bayesian Optimization
5 Day 4 Regularization Techniques for Model Optimization
6 Day 5 Cross Validation and Model Evaluation Techniques
7 Day 6 Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearchCV
8 Day 7 Optimization Project Building and Tuning a Final Model

1 Introduction to Week 9 Neural Networks and Deep Learning Fundamentals
2 Day 1 Introduction to Deep Learning and Neural Networks
3 Day 2 Forward Propagation and Activation Functions
4 Day 3 Loss Functions and Backpropagation
5 Day 4 Gradient Descent and Optimization Techniques
6 Day 5 Building Neural Networks with TensorFlow and Keras
7 Day 6 Building Neural Networks with PyTorch
8 Day 7 Neural Network Project Image Classification on CIFAR 10

1 Introduction to Week 10 Convolutional Neural Networks CNNs
2 Day 1 Introduction to Convolutional Neural Networks
3 Day 2 Convolutional Layers and Filters
4 Day 3 Pooling Layers and Dimensionality Reduction
5 Day 4 Building CNN Architectures with Keras and TensorFlow
6 Day 5 Building CNN Architectures with PyTorch
7 Day 6 Regularization and Data Augmentation for CNNs
8 Day 7 CNN Project Image Classification on Fashion MNIST or CIFAR 10

1 Introduction to Week 11 Recurrent Neural Networks RNNs and Sequence Modeling
2 Day 1 Introduction to Sequence Modeling and RNNs
3 Day 2 Understanding RNN Architecture and Backpropagation Through Time BPTT
4 Day 3 Long Short Term Memory LSTM Networks
5 Day 4 Gated Recurrent Units GRUs
6 Day 5 Text Preprocessing and Word Embeddings for RNNs
7 Day 6 Sequence to Sequence Models and Applications
8 Day 7 RNN Project Text Generation or Sentiment Analysis

1 Introduction to Week 12 Transformers and Attention Mechanisms
2 Day 1 Introduction to Attention Mechanisms
3 Day 2 Introduction to Transformers Architecture
4 Day 3 Self Attention and Multi Head Attention in Transformers
5 Day 4 Positional Encoding and Feed Forward Networks
6 Day 5 Hands On with Pre Trained Transformers BERT and GPT
7 Day 6 Advanced Transformers BERT Variants and GPT 3
8 Day 7 Transformer Project Text Summarization or Translation

1 Introduction to Week 13 Transfer Learning and Fine Tuning
2 Day 1 Introduction to Transfer Learning
3 Day 2 Transfer Learning in Computer Vision
4 Day 3 Fine Tuning Techniques in Computer Vision
5 Day 4 Transfer Learning in NLP
6 Day 5 Fine Tuning Techniques in NLP
7 Day 6 Domain Adaptation and Transfer Learning Challenges
8 Day 7 Transfer Learning Project Fine Tuning for a Custom Task

10 Random Forests Implementation in Python
11 Gradient Boosting Implementation in Python
12 Naive Bayes Implementation in Python
13 K Means Clustering Implementation in Python
14 Hierarchical Clustering Implementation in Python
15 DBSCAN Density Based Spatial Clustering of Applications w Noise Implementation
16 Gaussian Mixture ModelsGMM Implementation in Python
17 Principal Component Analysis PCA Implementation in Python
18 t Distributed Stochastic Neighbor Embedding t SNE Implementation in Python
19 Autoencoders Implementation in Python
1 Whats Next
20 Self Training Implementation in Python
21 Q Learning Implementation in Python
22 Deep Q Networks DQN Implementation in Python
23 Policy Gradient Methods Implementation in Python
24 One Class SVM Implementation in Python
25 Isolation Forest Implementation in Python
26 Convolutional Neural Networks CNNs Implementation in Python
27 Recurrent Neural Networks RNNs Implementation in Python
28 Long Short Term Memory LSTM Implementation in Python
29 Transformers Implementation in Python
2 Introduction to Machine Learning Algorithms
3 Linear Regression Implementation in Python
4 Ridge and Lasso Regression Implementation in Python
5 Polynomial Regression Implementation in Python
6 Logistic Regression Implementation in Python
7 K Nearest Neighbors KNN Implementation in Python
8 Support Vector Machines SVM Implementation in Python
9 Decision Trees Implementation in Python

1 What is Machine Learning
2 Introduction to TensorFlow
3 TensorFlow vs Other Machine Learning frameworks
4 Installing TensorFlow
5 Setting up your Development Environment
6 Verifying the Installation

1 Introduction to Tensors
2 Tensor Operations
3 Constants Variables and Placeholders
4 TensorFlow Computational Graph
5 Creating and Running a TensorFlow Session
6 Managing Graphs and Sessions
7 Building a Simple Feedforward Neural Network
8 Activation Functions
9 Loss Functions and Optimizers

1 Introduction to Keras API
2 Building Complex Models with Keras
3 Training and Evaluating Models
4 Introduction to CNNs
5 Building and Training CNNs with TensorFlow
6 Transfer Learning with Pre trained CNNs
7 Introduction to RNNs
8 Building and Training RNNs with TensorFlow
9 Applications of RNNs Language Modeling Time Series Prediction

1 Saving and Loading Models
2 TensorFlow Serving for Model Deployment
3 TensorFlow Lite for Mobile and Embedded Devices
4 Introduction to Distributed Computing with TensorFlow
5 TensorFlows Distributed Execution Framework
6 Scaling TensorFlow with TensorFlow Serving and Kubernetes
7 Introduction to TFX
8 Building End to End ML Pipelines with TFX
9 Model Validation Transform and Serving with TFX

1 Image Classification
2 Natural Language Processing
3 Recommender Systems
4 Object Detection
5 Building a Sentiment Analysis Model
6 Creating an Image Recognition System
7 Developing a Time Series Prediction Model
8 Implementing a Chatbot

1 Generative Adversarial Networks GANs
2 Reinforcement Learning with TensorFlow
3 Quantum Machine Learning with TensorFlow Quantum
4 TensorFlow Documentation and Tutorials
5 Online Courses and Books
6 TensorFlow Community and Forums
7 Summary of Key Concepts
8 Next Steps in Your TensorFlow Journey

10 10 Handling Complex Data
11 11 Model Deployment and Production
1 1 Introduction to PyTorch
12 12 Debugging and Troubleshooting
13 13 Distributed Training and Performance Optimization
14 14 Custom Layers and Loss Functions
15 15 Research oriented Techniques
16 16 Integration with Other Libraries
17 17 Contributing to PyTorch and Community Engagement
2 2 Getting Started with PyTorch
3 3 Working with Tensors
4 4 Autograd and Dynamic Computation Graphs
5 5 Building Simple Neural Networks
6 6 Loading and Preprocessing Data
7 7 Model Evaluation and Validation
8 8 Advanced Neural Network Architectures
9 9 Transfer Learning and Fine Tuning

1 1 Introduction to LangChain and Language Models
2 2 Project 1 Simple Text Based Question Answering Bot
3 3 Project 2 Sentiment Analysis with LangChain
4 4 Project 3 Document Summarization Tool
5 5 Project 4 Keyword Extraction from Text
6 6 Project 5 LangChain Powered Chatbot

10 Part 34 LangGraph for Stateful AI Agents
11 Part 35 CrewAI for Collaborative AI Agents
12 Part 41 Applications of AI Agents AI Agents in Business Operations
13 Part 42 AI Agents in Healthcare
14 Part 43 AI Agents in Financial Systems
15 Part 44 AI Agents in Entertainment
16 Part 45 AI Agents in Smart Homes and IoT
17 Part 51 Future Trends and Ethical Implications The Future of AI Agents
18 Part 52 Ethics in AI Agent Development
19 Part 53 Legal and Regulatory Challenges for AI Agents
1 Part 11 Understanding AI Agents How AI Agents Function
20 Part 61 Broader Impact of AI Agents Social and Economic Impacts of AI Agents
21 Part 62 AI Agents and Human Collaboration
22 Part 63 The Role of AI Agents in Scientific Research
23 Part 64 AI Agents in Public Safety and National Defense
2 Part 12 Introduction to AI Agents
3 Part 13 Types of AI Agents
4 Part 21 Technologies Behind AI Agents Machine Learning and AI Agents
5 Part 22 Natural Language Processing in AI Agents
6 Part 23 AI Agents in Robotics
7 Part 31 AI Agent Frameworks Architectures AI Agent Development Frameworks
8 Part 32 Overview of AutoGPT for AI Agents
9 Part 33 IBM Bee Framework for AI Agents

1 1 Hands on AutoGen IBM Bee LangGraph CrewAI AutoGPT
2 2 Hands on AutoGen
3 3 Hands on IBM Bee Framework
4 4 Hands on LangGraph
5 5 Hands on CrewAI
6 6 Hands on AutoGPT

1 Creating and Publishing GPTs to ChatGPT Store Part 1
2 Creating and Publishing GPTs to ChatGPT Store Part 2
3 Creating and Publishing GPTs to ChatGPT Store Part 3

10 Challenges in Deploying ML Models
11 Hands on Build an end to end pipeline for an ML model
12 Introduction to Infrastructure for MLOps Section
13 Introduction to Cloud Platforms AWS GCP Azure
14 Containerization with Docker
15 Kubernetes for Orchestrating ML Workloads
16 Setting up Local MLOps Environments
17 Hands on Containerize a simple ML model and deploy it locally using Kubernetes
1 Introduction to MLOps Sessions
2 Overview of MLOps and its Importance
3 Evolution of Machine Learning Operations
4 Key Concepts in MLOps Versioning Automation and Monitoring
5 MLOps vs DevOps Similarities and Differences
6 Hands on Set up a basic MLOps Project Structure Git Docker Model Pipeline
7 Introduction to Data Science to Production Pipeline Section
8 Overview of the ML Workflow Data Preparation to Deployment
9 Experimentation vs Production

10 Day 10 Basic Neural Network from scratch
11 Day 11 Stock Price Prediction using historical data w simple Linear Regression
12 Day 12 Predict Diabetes using logistic regression
13 Day 13 Dog vs Cat Classifier with CNN
14 Day 14 Tic Tac Toe AI using Minimax Algorithm
15 Day 15 Credit Card Fraud Detection using Scikit learn
16 Day 16 Iris Flower Classification using decision trees
17 Day 17 Simple Personal Assistant using Python speech libraries
18 Day 18 Text Summarizer using Gensim
19 Day 19 Fake Product Review Detection using NLP techniques
1 Day 1 Basic Calculator using Python
20 Day 20 Detect Emotion in Text using Natural Language Toolkit NLTK
21 Day 21 Book Recommendation System using collaborative filtering
22 Day 22 Predict Car Prices using Random Forest
23 Day 23 Identify Fake News using Naive Bayes
24 Day 24 Create a Resume Scanner using keyword extraction
25 Day 25 Customer Churn Prediction using classification algorithms
26 Day 26 Named Entity Recognition NER using spaCy
27 Day 27 Predict Employee Attrition using XGBoost
28 Day 28 Disease Prediction eg Heart Disease using ML Algorithms
29 Day 29 Movie Rating Prediction using Collaborative Filtering
2 Day 2 Image Classifier using Keras and TensorFlow
30 Day 30 Automatic Essay Grading using BERT
3 Day 3 Simple Chatbot using predefined responses
4 Day 4 Spam Email Detector using Scikit learn
5 Day 5 Handwritten Digit Recognition with MNIST dataset
6 Day 6 Sentiment Analysis on text data using NLTK
7 Day 7 Movie Recommendation System using cosine similarity
8 Day 8 Predict House Prices with Linear Regression
9 Day 9 Weather Forecasting using historical data

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Udemy - Artificial Intelligence Mastery Complete AI Bootcamp 2025 2025-1

Course modified date: 5 Feb 2025

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Udemy - Artificial Intelligence Mastery Complete AI Bootcamp 2025 2025-1

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