Artificial Intelligence & Machine Learning

Python Basics (Lists, Tuples, Dictionaries, Loops, Functions)

Numpy & Pandas for data manipulation

Matplotlib & Seaborn for data visualization

Descriptive Statistics (Mean, Median, Mode, Variance)

Probability Distributions (Normal, Binomial, Poisson)

Hypothesis Testing & p-values

Correlation & Covariance

Handling missing values

Data scaling & normalization

Encoding categorical variables

Feature selection & dimensionality reduction (PCA, LDA)

Project: Data Cleaning & Visualization on Real-World Dataset

Linear Regression, Polynomial Regression

Ridge & Lasso Regression

Evaluation Metrics (RMSE, R², MAE)

Logistic Regression, Decision Trees, Random Forest

Support Vector Machines (SVM), K-Nearest Neighbors (KNN)

Performance Metrics (Accuracy, Precision, Recall, F1-score, ROC)

📌 Project: Predicting House Prices (Regression) / Spam Email Classification

K-Means & Hierarchical Clustering

Principal Component Analysis (PCA)

Anomaly Detection

Hyperparameter tuning (GridSearchCV, RandomizedSearchCV)

Cross-validation techniques

Ensemble learning (Bagging, Boosting, Stacking)

Project: Customer Segmentation (K-Means Clustering)

Perceptron & Multi-layer Perceptron (MLP)

Activation functions (ReLU, Sigmoid, Softmax)

Backpropagation & Gradient Descent

Building Neural Networks with Keras

Optimizers (Adam, RMSprop, SGD)

Dropout & Batch Normalization

Image Processing & Convolution

Pooling, Padding, Transfer Learning (ResNet, VGG)

Object Detection & Image Classification

Understanding Time Series & Sequential Data

LSTMs & GRUs for Text Processing

Sentiment Analysis, Text Generation

Project: Sentiment Analysis on Tweets

Markov Decision Process (MDP)

Q-learning & Deep Q-Networks (DQN)

AI in Healthcare, Finance, and Robotics

Generative AI (GANs & Transformers)

Deploying models with Flask & FastAPI

CI/CD pipelines in ML (MLflow, Docker, Kubernetes)

Full end-to-end AI project

Resume & Interview Preparation for AI/ML Jobs