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Artificial Intelligence and Machine Learning
Artificial Intelligence and Machine Learning
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XGraduVationX
Last Update:
January 23, 2024
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Course Content
Session 1: Introduction to Artificial Intelligence and Machine Learning
What is Artificial Intelligence?
What is Machine Learning?
vTypes of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
Introduction to the course curriculum and learning objectives
Session 2: Introduction to Python Programming for AI and ML
Installing Python and Anaconda
Introduction to Python syntax, data types, and variables
Basic arithmetic operations in Python
Session 3: Control Flow Statements in Python
Conditional statements (if-else)
Looping statements (for and while loops)
Nested loops and loop control statements
Session 4: Data Structures in Python
Lists: indexing, slicing, and manipulation
Tuples: creating, accessing, and modifying
Dictionaries: key-value pairs and operations
Sets: creating, adding, and removing elements
Session 5: Introduction to NumPy
NumPy arrays: creation, indexing, and slicing
Mathematical operations with NumPy arrays
Array manipulation and reshaping
Session 6: Introduction to Pandas
Pandas data structures: Series and DataFrame
Reading data from different sources
Data indexing, selection, and filtering in Pandas
Session 7: Data Preprocessing and Cleaning
Handling missing values
Removing duplicates
Dealing with outliers
Feature scaling and normalization
Session 8: Introduction to Machine Learning
What is Machine Learning?
Steps in the ML process: Data preprocessing, model training, evaluation, and prediction
Supervised learning, unsupervised learning, and reinforcement learning
Session 9: Linear Regression
Introduction to Linear Regression
Simple Linear Regression: concepts, implementation, and evaluation
Multiple Linear Regression: concepts, implementation, and evaluation
Session 10: Logistic Regression
Introduction to Logistic Regression
Logistic Regression for binary classification: concepts, implementation, and evaluation
Multinomial Logistic Regression: concepts, implementation, and evaluation
Session 11: Decision Trees and Random Forests
Introduction to Decision Trees
Decision Tree algorithm: concepts, implementation, and evaluation
Ensemble methods: Introduction to Random Forests
Session 12: Naive Bayes Classifier
Introduction to Naive Bayes algorithm
Types of Naive Bayes classifiers: Gaussian, Multinomial, and Bernoulli
Implementing Naive Bayes algorithm for classification
Session 13: Support Vector Machines
Introduction to Support Vector Machines
Linear SVM: concepts, implementation, and evaluation
Non-linear SVM: Kernel trick and implementation
Session 14: Clustering Algorithms
Introduction to Clustering
K-means clustering: concepts, implementation, and evaluation
Hierarchical clustering: concepts, implementation, and evaluation
Density-based clustering: DBSCAN algorithm
Session 15: Dimensionality Reduction Techniques
Curse of dimensionality
Principal Component Analysis (PCA): concepts, implementation, and applications
Evaluation and visualization of PCA results
Session 16: Introduction to Deep Learning
What is Deep Learning?
Artificial Neural Networks (ANNs)
Activation functions and backpropagation algorithm
Session 17: Convolutional Neural Networks (CNNs)
Introduction to CNNs
Architecture of CNNs: convolutional layers, pooling layers, and fully connected layers
Training and evaluating CNNs for image classification
Session 18: Recurrent Neural Networks (RNNs)
Introduction to RNNs
Architecture of RNNs: recurrent layers, LSTM, and GRU
Training and evaluating RNNs for sequence prediction tasks
Session 19: Introduction to Natural Language Processing(NLP)
Introduction to NLP and its applications
Text preprocessing techniques: tokenization, stemming, and lemmatization
Text feature extraction: Bag-of-Words, TF-IDF, and Word Embeddings
Session 20: Sentiment Analysis
Introduction to Sentiment Analysis
Building a sentiment analysis model using supervised learning techniques
Evaluating the sentiment analysis model’s performance
Fine-tuning the model for better accuracy
Session 21: Introduction to Reinforcement Learning
What is Reinforcement Learning?
Markov Decision Process (MDP)
Q-Learning algorithm: concepts and implementation
Session 22: Introduction to Computer Vision
Overview of Computer Vision applications
Image classification using CNNs
Object detection using techniques like R-CNN or YOLO
Session 23: Project 1: Build a Linear Regression Model to Predict Housing Prices
Collecting and preprocessing a housing dataset
Implementing a Linear Regression model using Python and NumPy
Training and evaluating the model
Presenting and discussing the Linear Regression Model project
Session 24: Project 1: Present and Discuss the Linear Regression Model Project
Session 25: Project 2: Build a Sentiment Analysis Model for Movie Reviews
Collecting and preprocessing a movie review dataset
Implementing a Sentiment Analysis model using Python and NLP techniques
Training and evaluating the model
Presenting and discussing the Sentiment Analysis Model project
Session 26: Project 2: Present and Discuss the Sentiment Analysis Model Project
Session 27: Introduction to Transfer Learning
Overview of Transfer Learning
Pretrained models and their applications
Fine-tuning pretrained models for specific tasks
Session 28: Introduction to Generative Adversarial Networks (GANs)
Introduction to GANs and their applications
GAN architecture: generator and discriminator networks
Training and generating new data with GANs
Session 29: Introduction to Reinforcement Learning in Robotics
Applications of Reinforcement Learning in Robotics
Challenges and considerations in training RL agents for robotic tasks
Real-world examples of RL in robotics
Session 30: Ethics and Responsible AI
Ethical considerations in AI and ML
Bias, fairness, and transparency in AI systems
Responsible use of AI and mitigating risks
Session 31: Model Deployment and Serving
Overview of model deployment strategies
Containerization with Docker
Building REST APIs for model serving
Deploying models on cloud platforms like AWS or Azure
Session 32: Introduction to Autoencoders
Introduction to Autoencoders and their applications
Implementing an Autoencoder model for dimensionality reduction or image denoising
Session 33: Hyperparameter Optimization and Model Evaluation Techniques
Strategies for hyperparameter optimization
Cross-validation and model evaluation techniques
Fine-tuning models for improved performance
Session 34: Advanced Topics in Machine Learning and AI
Exploring advanced topics such as Reinforcement Learning, Natural Language Processing, and Computer Vision in depth
Discussing recent advancements and research papers in these fields
Session 35: Emerging Trends in AI and ML
Overview of emerging trends and applications in AI and ML
Current research and breakthroughs in the field
Real-world examples of AI and ML impacting various industries
Session 36: Recap and Review of the Course
Review of key concepts and techniques covered in the course
Q&A session for clarifying doubts and addressing queries
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12,000.00
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Instructor
XGraduVationX
Language
English
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