Masters Course in Data Science

• Introduction to Gradient Boosting 
• Understanding boosting and the concept of weak learners. 
• How XGBoost improves upon traditional gradient boosting. 
• XGBoost Hyperparameters. 

• Introduction to Naive Bayes 
• Types of Naive Bayes 
• Probability Distributions 
• Training and Classification 

• Introduction to Support Vector Machines 
• Support Vector Classification (SVC) 
• Support Vector Machines for Regression (SVR) 
• Kernel Trick and Non-Linear SVM 

ASSESSMENT FOR EACH ALGORITHM OF MACHINE LEARNING 

UNSUPERVISED LEARNING ALGORITHMS K-MEANS ALGORITHM 

• Introduction to Clustering 
• K-Means Algorithm 
• Objective Function and Optimization 
• Challenges and Limitations 

• Introduction to K-NN 
• Distance Metrics 
• Hyperparameter K 
• Decision Boundary and Majority Voting 

ASSESSMENT FOR EACH ALGORITHM OF UNSUPERVISED LEARNING 

CAPSTONE PROJECTS FOR MACHINE LEARNING, WHERE DATA ANALYTICS PROBLEMS SOLVING IS MANDATORY. CAPSTONE PROJECTS WILL BE DISTRIBUTED ACCORDINGLY. 

• Introduction to Deep Learning and TensorFlow. 
• Convolutional Neural Networks (CNNs) with TensorFlow. 
• Recurrent Neural Networks (RNNs) and Sequence Models. 
• Advanced Deep Learning with TensorFlow and Keras. 

• Tokenization 
• Stop words removal 
• Stemming and Lemmatization 
• Text cleaning and normalization 

• Tokenization 
• Stop words removal 
• Stemming and Lemmatization 
• Text cleaning and normalization 

• Bag of Words (BoW) 
• Term Frequency-Inverse Document Frequency (TF-IDF) 
• Word embeddings (Word2Vec, GloVe) 
• Document-term matrices 

• Sentiment analysis techniques 
• Binary and multi-class classification 
• Building a sentiment analysis model