• Introduction to Data Science
• Python for Data Science
• Data Manipulation and Visualization with Pandas
• Exploratory Data Analysis
• Feature Engineering and Selection
• Introduction to Statistical Modeling
• Linear Regression
• Logistic Regression
• Decision Trees and Random Forests
• Model Evaluation and Validation

• Descriptive Statistics
• Probability Distributions
• Hypothesis Testing
• Confidence Intervals
• ANOVA (Analysis of Variance)
• Correlation and Regression Analysis
• Experimental Design

• Introduction to Machine Learning
• Supervised Learning: Classification
• Supervised Learning: Regression
• Unsupervised Learning: Clustering
• Dimensionality Reduction
• Model Evaluation and Selection
• Ensemble Learning
• Support Vector Machines (SVM)
• Decision Trees and Random Forests
• Gradient Boosting Methods

• Introduction to Neural Networks
• Building Neural Networks with Keras
• Activation Functions and Optimization Techniques
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Transfer Learning
• Generative Adversarial Networks (GANs)
• Reinforcement Learning

• Advanced CNN Architectures (e.g., ResNet, Inception, etc.)
• Object Detection
• Image Segmentation
• Style Transfer
• Deep Reinforcement Learning
• Natural Language Processing (NLP) using Deep Learning

• Introduction to Reinforcement Learning
• Markov Decision Processes (MDPs)
• Value Iteration and Policy Iteration
• Q-Learning
• Deep Q-Networks (DQN)
• Policy Gradient Methods
• Actor-Critic Methods

• Introduction to NLP
• Text Preprocessing and Tokenization
• Word Embeddings (e.g., Word2Vec, GloVe)
• Text Classification
• Sentiment Analysis
• Named Entity Recognition
• Language Generation
• Sequence-to-Sequence Models

Data Science with Python:

Assignment 1:

• Analyze a dataset of your choice and perform data cleaning, exploration, and visualization.
• Apply statistical techniques to uncover patterns and insights from the data.
• Present your findings in a comprehensive report with visualizations.

Assignment 2:

• Build a machine learning model to predict customer churn for a telecom company.
• Preprocess the dataset, perform feature engineering, and split it into training and testing sets.
• Train different models (e.g., logistic regression, decision tree, random forest) and compare their performance using appropriate evaluation metrics.

Applied Statistics:

Assignment 1:

• Conduct a hypothesis test to determine if there is a significant difference in the average income between two different cities.
• Formulate the null and alternative hypotheses, choose an appropriate test statistic, and calculate the p-value.
• Interpret the results and provide conclusions based on the findings.

Assignment 2:

• Perform a regression analysis to explore the relationship between a company's advertising expenditure and its sales revenue.
• Collect the necessary data, fit a regression model, and interpret the coefficients.
• Evaluate the model's goodness of fit and make predictions based on the regression equation.

Machine Learning:

Assignment 1:

• Build a classification model using a supervised learning algorithm (e.g., logistic regression, support vector machines) to predict whether a loan applicant will default or not.
• Preprocess the data, perform feature selection, and evaluate the model's accuracy, precision, recall, and F1 score.

Assignment 2:

• Apply clustering techniques (e.g., k-means, hierarchical clustering) to segment customers based on their purchasing behavior.
• Visualize the clusters and analyze their characteristics.
• Propose marketing strategies for each segment to improve customer targeting and engagement.

Deep Learning with Tensorflow&Keras:

Assignment 1:

• Build a convolutional neural network (CNN) to classify images from the CIFAR-10 dataset.
• Train the model using transfer learning with a pre-trained network (e.g., VGG16, ResNet) and fine-tune the top layers.
• Evaluate the model's accuracy and compare it with a baseline model.

Assignment 2:

• Develop a recurrent neural network (RNN) model to generate text in the style of a given author or genre.
• Train the model on a large text corpus and generate new text samples based on a seed input.
• Evaluate the generated text for coherence and similarity to the training data.

Advance Deep Learning & Computer Vision:

Assignment 1:

• Implement a generative adversarial network (GAN) to generate realistic images of human faces.
• Train the GAN using a dataset of celebrity faces and evaluate the quality of the generated images using visual inspection and feedback from users.

Assignment 2:

• Build an object detection model using the YOLO (You Only Look Once) algorithm to detect and classify objects in images or video.
• Fine-tune the model on a specific dataset and evaluate its performance in terms of accuracy and speed.

Reinforcement Learning:

Assignment 1:

• Train an agent using the Q-learning algorithm to navigate a grid world environment and reach a goal state while avoiding obstacles.
• Implement the necessary components such as the state representation, action selection, and reward function.

Assignment 2:

• Apply deep reinforcement learning techniques (e.g., deep Q-network, policy gradient) to train an agent to play a classic video game, such as Pong or Breakout, using raw pixel inputs.