Data Science Course with Projects

DATA SCIENCE COURSE SYLLABUS

MODULE -1 : FUNDAMENTALS OF PROGRAMMING

<!--[if !supportLists]-->ü  <!--[endif]-->Python for Data Science Introduction

<!--[if !supportLists]-->ü  <!--[endif]-->Python for Data Science: Data Structures

<!--[if !supportLists]-->ü  <!--[endif]-->Python for Data Science: Functions

<!--[if !supportLists]-->ü  <!--[endif]-->Python for Data Science: Numpy

<!--[if !supportLists]-->ü  <!--[endif]-->Python for Data Science: Matplotlib

<!--[if !supportLists]-->ü  <!--[endif]-->Python for Data Science: Pandas

<!--[if !supportLists]-->ü  <!--[endif]-->Python for Data Science: Computational Complexity

<!--[if !supportLists]-->ü  <!--[endif]-->SQL

MODULE-2: DATA SCIENCE: Exploratory Data Analysis and Data Visualization

<!--[if !supportLists]-->ü  <!--[endif]--> Plotting for exploratory data analysis (EDA)

<!--[if !supportLists]-->ü  <!--[endif]--> Linear Algebra

<!--[if !supportLists]-->ü  <!--[endif]--> Probability and Statistics

<!--[if !supportLists]-->ü  <!--[endif]--> Interview Questions on Probability and statistics

<!--[if !supportLists]-->ü  <!--[endif]--> Dimensionality reduction and Visualization:

<!--[if !supportLists]-->ü  <!--[endif]--> PCA(principal component analysis)

<!--[if !supportLists]-->ü  <!--[endif]--> (t-SNE)T-distributed Stochastic Neighborhood Embedding

<!--[if !supportLists]-->ü  <!--[endif]--> Interview Questions on Dimensionality Reduction

MODULE-3: Fundamentals of Natural Language Processing and Machine Learning

<!--[if !supportLists]-->ü  <!--[endif]-->Real world problem: Predict rating given product reviews on Amazon

<!--[if !supportLists]-->ü  <!--[endif]-->Classification And Regression Models: K-Nearest Neighbors

<!--[if !supportLists]-->ü  <!--[endif]-->Interview Questions on K-NN(K Nearest Neighbour)

<!--[if !supportLists]-->ü  <!--[endif]-->Classification algorithms in various situations

<!--[if !supportLists]-->ü  <!--[endif]-->Performance measurement of models

<!--[if !supportLists]-->ü  <!--[endif]-->Interview Questions on Performance Measurement Models

<!--[if !supportLists]-->ü  <!--[endif]-->Naive Bayes

<!--[if !supportLists]-->ü  <!--[endif]-->Logistic Regression

<!--[if !supportLists]-->ü  <!--[endif]-->Linear Regression

<!--[if !supportLists]-->ü  <!--[endif]-->Solving Optimization Problems

<!--[if !supportLists]-->ü  <!--[endif]-->Interview Questions on Logistic Regression and Linear Regression

MODULE-4: Machine Learning – II  (Supervised Learning Methods)

<!--[if !supportLists]-->ü  <!--[endif]-->Support Vector Machines (SVM)

<!--[if !supportLists]-->ü  <!--[endif]-->Interview Questions on Support Vector Machine

<!--[if !supportLists]-->ü  <!--[endif]-->Decision Trees

<!--[if !supportLists]-->ü  <!--[endif]-->Interview Questions on decision Trees

<!--[if !supportLists]-->ü  <!--[endif]-->Ensemble Models

MODULE-5: Feature Engineering, Productionization and deployment of ML models

<!--[if !supportLists]-->ü  <!--[endif]-->Featurization and Feature engineering.

<!--[if !supportLists]-->ü  <!--[endif]-->Miscellaneous Topics

MODULE-6 : ML Real World Case studies

<!--[if !supportLists]-->ü  <!--[endif]-->Case Study 1: Quora question Pair Similarity Problem

<!--[if !supportLists]-->ü  <!--[endif]-->Case Study 2: Personalized Cancer Diagnosis

<!--[if !supportLists]-->ü  <!--[endif]-->Case Study 3:Facebook Friend Recommendation using Graph Mining

<!--[if !supportLists]-->ü  <!--[endif]-->Case study 4:Taxi demand prediction in New York City

<!--[if !supportLists]-->ü  <!--[endif]-->Case study 5: Stackoverflow tag predictor

<!--[if !supportLists]-->ü  <!--[endif]-->Case Study 6: Microsoft Malware Detection

<!--[if !supportLists]-->ü  <!--[endif]-->Case Study 7: AD-CLICK Prediction

MODULE-1: DATA MINING(Unsupervised Learning) and Recommender systems + Real world case studies

<!--[if !supportLists]-->ü  <!--[endif]-->Unsupervised learning/Clustering

<!--[if !supportLists]-->ü  <!--[endif]-->Hierarchical clustering Technique

<!--[if !supportLists]-->ü  <!--[endif]-->DBSCAN (Density based clustering) Technique

<!--[if !supportLists]-->ü  <!--[endif]-->Recommender Systems and Matrix Factorization

<!--[if !supportLists]-->ü  <!--[endif]-->Interview Questions on Recommender Systems and Matrix Factorization.

<!--[if !supportLists]-->ü  <!--[endif]-->Case Study 8: Amazon fashion discovery engine(Content Based recommendation)

<!--[if !supportLists]-->ü  <!--[endif]-->Case Study 9:Netflix Movie Recommendation System (Collaborative based recommendation)

MODULE-8 : NEURAL NETWORKS, COMPUTER VISION and DEEP LEARNING

<!--[if !supportLists]-->ü  <!--[endif]-->Deep Learning:Neural Networks.

<!--[if !supportLists]-->ü  <!--[endif]-->Deep Learning: Deep Multi-layer perceptrons

<!--[if !supportLists]-->ü  <!--[endif]-->Deep Learning: Tensorflow and Keras.

<!--[if !supportLists]-->ü  <!--[endif]-->Deep Learning: Convolutional Neural Nets.

<!--[if !supportLists]-->ü  <!--[endif]-->Deep Learning: Long Short-term memory (LSTMs)

<!--[if !supportLists]-->ü  <!--[endif]-->Deep Learning: Generative Adversarial Networks (GANs)

<!--[if !supportLists]-->ü  <!--[endif]-->Encoder-Decoder Models

<!--[if !supportLists]-->ü  <!--[endif]-->Attention Models in Deep Learning

<!--[if !supportLists]-->ü  <!--[endif]-->Interview Questions on Deep Learning

MODULE-9: DEEP LEARNING REAL WORLD CASE STUDIES

<!--[if !supportLists]-->ü  <!--[endif]-->Case Study 11: Human Activity Recognition

<!--[if !supportLists]-->ü  <!--[endif]-->Case Study 10: Self Driving Car

<!--[if !supportLists]-->ü  <!--[endif]-->Case Study 12: Music Generation using Deep-Learning

<!--[if !supportLists]-->ü  <!--[endif]-->Interview Questions

For More details:  +91 888 666 1866 /  +91 888 666 1143 / +91 888 666 1964

Web:  www.VenkysIT.com      ||   www.AiResearchLabs.in

Mail: AiCourse.in@gmail.com