Machine Learning With AI

Introduction to AI and Machine Learning
Understand AI vs ML vs Deep Learning, real-world applications, and key terminologies.

Types of Machine Learning
Covers supervised, unsupervised, semi-supervised, and reinforcement learning.

Data Collection and Preprocessing
Data wrangling, cleaning, normalization, feature engineering, and splitting datasets.

Exploratory Data Analysis (EDA)
Visualizing and understanding data distributions, correlations, and outliers.

Supervised Learning – Regression Models
Linear regression, decision trees, evaluation metrics (MAE, RMSE, etc.).

Supervised Learning – Classification Models
Logistic regression, k-NN, SVM, Naive Bayes, evaluation metrics (accuracy, precision, recall, F1).

Unsupervised Learning – Clustering and Dimensionality Reduction
k-means, hierarchical clustering, PCA, t-SNE.

Model Evaluation and Validation
Cross-validation, confusion matrix, bias-variance trade-off, overfitting vs underfitting.

Feature Selection and Engineering
Techniques like RFE, mutual information, domain-driven feature creation.

Ensemble Methods
Bagging, boosting, random forests, gradient boosting, stacking.

Neural Networks and Deep Learning
Perceptron, feedforward networks, backpropagation, activation functions.

Convolutional and Recurrent Neural Networks
CNNs for images, RNNs and LSTMs for sequences and time series.

Natural Language Processing (NLP)
Text preprocessing, TF-IDF, word embeddings, transformers (e.g., BERT).

Model Deployment and Production
Saving models, using Flask/FastAPI, cloud deployment (AWS, GCP), CI/CD basics.

Ethics, Explainability, and Responsible AI
Fairness, bias, interpretability (SHAP, LIME), data privacy, regulatory concerns.