University FAQ

Frequently asked questions

Machine Learning with Python


Introduction to Machine Learning

  • Introduction to Machine Learning

  • Python for Machine Learning

  • Supervised vs Unsupervised

Regression
  • Introduction to Regression

  • Simple Linear Regression

  • Model Evaluation in Regression Models

  • Evaluation Metrics in Regression Models

  • Multiple Linear Regression

  • Non-Linear Regression

Classification
  • Introduction to Classification

  • K-Nearest Neighbours

  • Evaluation Metrics in Classification

  • Introduction to Decision Trees

  • Building Decision Trees

  • Intro to Logistic Regression

  • Logistic regression vs Linear regression

  • Logistic Regression Training

  • Support Vector Machine

Clustering
  • Intro to Clustering

  • Intro to k-Means

  • More on k-Means

  • Intro to Hierarchical Clustering

  • More on Hierarchical Clustering

  • DBSCAN

Recommender Systems
  • Intro to Recommender Systems

  • Content-based Recommender Systems

  • Collaborative Filtering




Introduction to Data Science & Analytics Techniques


  • Introduction to Data Science
  • Introduction to Python
  • Data Analysis Pipeline
  • What is Data Extraction
  • Types of Data
  • Raw and Processed Data
  • Data Wrangling
  • Overview of the Analytics Techniques
  • Analytics
  • Business Intelligence
  • Business Analytics
  • Industry Examples




SQL for Data Science


Basic SQL

  • Introduction to SQL

  • DDL & DML Statement

  • SELECT Statement AGGREGATE functions

  • WHERE, ORDER BY, DISTINCT, GROUP BY, LIKE, AND & OR clause

  • UPDATE & DELETE query

Advanced SQL

  • JOINS

  • UNION, UNION ALL, INTERSECT

  • Using VIEWS & INDEXES

  • Sub Queries

  • NULL values & DATE function




Numpy


  • Indexing
  • ndarray
  • Array Creation
  • Data Type Objects
  • Data type Object (dtype) in NumPy
  • Basic Slicing and ALinear Algebra
  • Sorting, Searching and Counting
  • Set 1 (Introduction)dvanced Indexing
  • Iterating Over Array
  • Binary Operations
  • Mathematical Function
  • String Operations

  • Set 2 (Advanced)
  • Multiplication of two Matrices in Single line using Numpy in Python




6 Month Internship


  • Live Projects in an Internship Company with access to Virtual Cloud based Linux System




Statistics & Probability


About Data

  • Data definition
  • Raw and Processed data
  • Data Types (NOIR)

Descriptive Stats

  • Measure of Central Tendency
  • Measure of Dispersion
  • Measure of Association

Probability

  • Basic terminology
  • Rules and Events
  • Conditional probability and Bayes theorem

Data Distribution

  • Skewness
  • t-Distribution
  • Uniform Distribution
  • Binomial Distribution
  • Poisson Distribution
  • Geometric Distribution
  • Gaussian Distribution
  • Standard Normal Distribution
  • Central Limit Theorem

Inferential Stats

  • Estimation technique
  • Hypothesis Testing (t-statistic calculations)

Sampling tecniques

  • Random Sampling,
  • Stratified Sampling

Statistical Tests

  • ANOVA
  • Chi-Square




Introduction to NLP


  • Syntactical Parsing
  • Text Preprocessing
  • Text to Features (Feature Engineering on text data)
  • Noise Removal
  • Lexicon Normalization
  • Lemmatization
  • Stemming
  • Object Standardization
  • Dependency Grammar
  • Part of Speech Tagging
  • Entity Parsing
  • Phrase Detection
  • Named Entity Recognition
  • Topic Modelling
  • N-Grams
  • Statistical features
  • TF – IDF
  • Frequency / Density Features
  • Readability Features
  • Word Embeddings
  • Important tasks of NLP
  • Text Classification
  • Text Matching
  • Levenshtein Distance
  • Phonetic Matching
  • Flexible String Matching
  • Important NLP libraries




Pandas


Pandas DataFrame

  • Creating a Pandas DataFrame
  • Dealing with Rows and Columns in Pandas DataFrame
  • Indexing and Selecting Data with Pandas
  • Boolean Indexing in Pandas
  • Conversion Functions in Pandas DataFrame
  • Iterating over rows and columns in Pandas DataFrame
  • Working with Missing Data in Pandas
  • Working With Text Data
  • Working with Dates and Times
  • Merging, Joining and Concatenating

Data Analysis

  • Data visualization using Bokeh
  • Exploratory Data Analysis in Python
  • Data visualization with different Charts in Python
  • Data Analysis and Visualization with Python
  • Math operations for Data analysis




Data Analysis & Wrangling


  • Data Analysis Pipeline
  • What is Data Extraction
  • Types of Data
  • Raw and Processed Data
  • Data Wrangling
  • Exploratory Data Analysis