» What is AI? History, applications, and impact on industries.
» Types of AI: Narrow AI vs. General AI vs. Superintelligence.
» Subfields of AI: Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, etc.
» Problem representation (state space, search algorithms).
» Classical planning and problem-solving techniques.

» Python Syntax & Basics
» Variables, data types, operators.
» Control flow: If-else, loops.
» Functions, classes, and modules.
» NumPy: Arrays, matrix operations.
» Pandas: DataFrame manipulation, loading data, data cleaning.
» Matplotlib: Plotting, visualizing data.

» Linear Algebra.
» Vectors, matrices, matrix operations.
» Singular Value Decomposition (SVD).
» Differentiation, partial derivatives, chain rule.
» Gradients, optimization techniques (gradient descent).
» Probability theory basics: Bayes' theorem, random variables, distributions.
» Descriptive statistics, hypothesis testing, p-values.

» Supervised Learning.
» Linear Regression, Logistic Regression.
» Overfitting vs. underfitting, cross-validation.
» Unsupervised Learning.
» Clustering: K-Means, DBSCAN, Hierarchical Clustering.
» Ensemble Methods
» Decision Trees, Random Forests, Gradient Boosting (XGBoost, LightGBM).

» Introduction to Neural Networks.
» Deep Learning Architectures.

» Implementing algorithms in Scikit-Learn: Linear regression, SVM, Random Forest.
» Hyperparameter tuning, feature selection.
» Model evaluation and validation techniques.
» TensorFlow / Keras: Building and training simple neural networks.
» PyTorch: Implementing deep learning models.
» Importing, cleaning, and visualizing datasets from sources like Kaggle.

» Text preprocessing (tokenization, stemming, lemmatization).
» Bag-of-Words, TF-IDF.
» Word embeddings (Word2Vec, GloVe).
» Sentiment analysis, Named Entity Recognition (NER), Text Generation.

» Image processing basics: Filters, edge detection, image enhancement.
» Object detection, face recognition, image segmentation..
» CNNs in image classification, object detection (YOLO, SSD).

» Markov Decision Processes (MDP).
» Q-learning, Deep Q-Networks (DQN).
» Generative Adversarial Networks (GANs), Variational Autoencoders (VAE).
» Bias in AI models, fairness, transparency.
» Ethical concerns in AI deployment (privacy, safety).

» Sentiment Analysis using NLP..
» Image Classification with CNNs.
» Recommendation Systems (collaborative filtering, content-based).
» Predictive models for healthcare or finance.