Regression Models Linear Regression Polynomial Regression Ridge and Lasso Regression Classification Models Logistic Regression Decision Trees Random Forests Support Vector Machines (SVM) Naïve Bayes Classifier Performance Metrics Accuracy, Precision, Recall, F1 Score Confusion Matrix ROC Curve and AUC

Introduction to Neural Networks Activation Functions: Sigmoid, ReLU, Tanh Training Neural Networks: Backpropagation, Optimizers Convolutional Neural Networks (CNNs) for Image Data Recurrent Neural Networks (RNNs) for Sequence Data Long Short-Term Memory (LSTM) Networks Generative Adversarial Networks (GANs) Frameworks: TensorFlow and PyTorch Basics

Basics of Image Processing Object Detection and Recognition Image Classification with CNNs Transfer Learning with Pre-trained Models (e.g., VGG, ResNet) Applications: Facial Recognition, Autonomous Vehicles

Predictive Analytics: Predicting House Prices Image Classification: Building a Dog vs. Cat Classifier Sentiment Analysis on Tweets Recommender System for E-commerce Time Series Forecasting: Stock Price Prediction Reinforcement Learning: AI for Game Playing

Machine Learning and AI

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Beginner's Guide to Machine Learning and AI

These tutorials will provide you with a solid foundation in Machine Learning and AI and prepare you for your career goals.

Introduction to Machine Learning and AI

What is Machine Learning (ML)?
Evolution of Artificial Intelligence (AI)
Difference Between AI, ML, and Deep Learning
Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
Applications of AI and ML in Various Industries
Tools and Frameworks for ML/AI Development

Mathematics and Statistics for ML/AI

Linear Algebra
Vectors, Matrices, and Tensor Operations
Eigenvalues and Eigenvectors
Probability and Statistics
Probability Basics: Conditional Probability, Bayes’ Theorem
Descriptive and Inferential Statistics
Distributions: Normal, Binomial, Poisson Calculus
Derivatives and Gradients
Optimization Techniques: Gradient Descent
Linear Regression
Least Squares Method
Correlation vs Causation

Data Preprocessing and Exploration

Data Cleaning: Handling Missing and Outlier Data
Data Transformation: Encoding Categorical Variables, Scaling
Feature Selection and Engineering
Exploratory Data Analysis (EDA) Using Python (Pandas, NumPy, Matplotlib, Seaborn)
Splitting Data into Training and Testing Sets

Supervised Learning

Regression Models
Linear Regression
Polynomial Regression
Ridge and Lasso Regression
Classification Models
Logistic Regression
Decision Trees
Random Forests
Support Vector Machines (SVM)
Naïve Bayes Classifier
Performance Metrics
Accuracy, Precision, Recall, F1 Score
Confusion Matrix
ROC Curve and AUC

Unsupervised Learning

Clustering
k-Means Clustering
Hierarchical Clustering
DBSCAN
Dimensionality Reduction
Principal Component Analysis (PCA) t-SNE and UMAP
Applications: Market Segmentation, Anomaly Detection

Reinforcement Learning

Basics of Reinforcement Learning (RL)
Markov Decision Processes (MDP)
Q-Learning and Deep Q-Learning
Applications of RL: Robotics, Game Playing

Deep Learning

Introduction to Neural Networks
Activation Functions: Sigmoid, ReLU, Tanh
Training Neural Networks: Backpropagation, Optimizers
Convolutional Neural Networks (CNNs) for Image Data
Recurrent Neural Networks (RNNs) for Sequence Data
Long Short-Term Memory (LSTM) Networks
Generative Adversarial Networks (GANs)
Frameworks: TensorFlow and PyTorch Basics

Natural Language Processing (NLP)

Text Preprocessing: Tokenization, Lemmatization, Stemming
Word Embeddings: Word2Vec, GloVe
Sentiment Analysis
Sequence-to-Sequence Models
Transformers and Attention Mechanisms
Chatbots and Virtual Assistants

Computer Vision

Basics of Image Processing
Object Detection and Recognition
Image Classification with CNNs
Transfer Learning with Pre-trained Models (e.g., VGG, ResNet)
Applications: Facial Recognition, Autonomous Vehicles

AI Ethics and Fairness

Ethical Challenges in AI
Bias and Fairness in Machine Learning Models
Explainability and Interpretability of AI Systems
Responsible AI Development

Big Data and Scalable ML

Introduction to Big Data Concepts
Working with Spark MLlib
Cloud Platforms for ML/AI (AWS SageMaker, Google AI, Azure ML)
Distributed Training of ML Models

Model Deployment and Monitoring

Saving and Loading Trained Models
Deploying Models as APIs Using Flask/Django
Using Docker for Model Deployment
Monitoring Model Performance in Production

Hands-On Projects

Predictive Analytics: Predicting House Prices
Image Classification: Building a Dog vs. Cat Classifier
Sentiment Analysis on Tweets
Recommender System for E-commerce
Time Series Forecasting: Stock Price Prediction
Reinforcement Learning: AI for Game Playing

Python Tutorial

Python Data Type

Python Tutorial

Python Data Type

Python Tutorial

Python Data Type

Python Tutorial

Python Data Type

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