DATA SCIENCE

MODULE -1 : FUNDAMENTALS OF PROGRAMMING
  • Python for Data Science Introduction
  • Python for Data Science: Data Structures
  • Python for Data Science: Functions
  • Python for Data Science: Numpy
  • Python for Data Science: Matplotlib
  • Python for Data Science: Pandas
  • Python for Data Science: Computational Complexity
  • SQL
MODULE-2: DATA SCIENCE: Exploratory Data Analysis and Data Visualization
  • Plotting for exploratory data analysis (EDA)
  • Linear Algebra
  • Probability and Statistics
  • Interview Questions on Probability and statistics
  • Dimensionality reduction and Visualization:
  • PCA(principal component analysis)
  • (t-SNE)T-distributed Stochastic Neighborhood Embedding
  • Interview Questions on Dimensionality Reduction
MODULE-3: Fundamentals of Natural Language Processing and Machine Learning
  • Real world problem: Predict rating given product reviews on Amazon
  • Classification And Regression Models: K-Nearest Neighbors
  • Interview Questions on K-NN(K Nearest Neighbour)
  • Classification algorithms in various situations
  • Performance measurement of models
  • Interview Questions on Performance Measurement Models
  • Naive Bayes
  • Logistic Regression
  • Linear Regression
  • Solving Optimization Problems
  • Interview Questions on Logistic Regression and Linear Regression
MODULE-4: Machine Learning – II  (Supervised Learning Methods)
  • Support Vector Machines (SVM)
  • Interview Questions on Support Vector Machine
  • Decision Trees
  • Interview Questions on decision Trees
  • Ensemble Models
MODULE-5: Feature Engineering, Productionization and deployment of ML models
  • Featurization and Feature engineering.
  • Miscellaneous Topics
MODULE-6 : ML Real World Case studies
  • Case Study 1: Quora question Pair Similarity Problem
  • Case Study 2: Personalized Cancer Diagnosis
  • Case Study 3:Facebook Friend Recommendation using Graph Mining
  • Case study 4:Taxi demand prediction in New York City
  • Case study 5: Stackoverflow tag predictor
  • Case Study 6: Microsoft Malware Detection
  • Case Study 7: AD-CLICK Prediction
 
MODULE-1: DATA MINING(Unsupervised Learning) and Recommender systems + Real world case studies
  • Unsupervised learning/Clustering
  • Hierarchical clustering Technique
  • DBSCAN (Density based clustering) Technique
  • Recommender Systems and Matrix Factorization
  • Interview Questions on Recommender Systems and Matrix Factorization.
  • Case Study 8: Amazon fashion discovery engine(Content Based recommendation)
  • Case Study 9:Netflix Movie Recommendation System (Collaborative based recommendation)
MODULE-8 : NEURAL NETWORKS, COMPUTER VISION and DEEP LEARNING
  • Deep Learning:Neural Networks.
  • Deep Learning: Deep Multi-layer perceptrons
  • Deep Learning: Tensorflow and Keras.
  • Deep Learning: Convolutional Neural Nets.
  • Deep Learning: Long Short-term memory (LSTMs)
  • Deep Learning: Generative Adversarial Networks (GANs)
  • Encoder-Decoder Models
  • Attention Models in Deep Learning
  • Interview Questions on Deep Learning
MODULE-9: DEEP LEARNING REAL WORLD CASE STUDIES
  • Case Study 11: Human Activity Recognition
  • Case Study 10: Self Driving Car
  • Case Study 12: Music Generation using Deep-Learning
  • Interview Questions