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