Data science with Python Training in Bangalore

Data Science with Python Course Outline:

Introduction to Data Science with Python and R

Why Machine Learning is the Future

Applications of Machine Learning

Introduction to the main tools and ideas which are required for Data Scientist/Business Analyst/Data Analyst.

Overview of the data, questions, and tools

A conceptual introduction to the ideas behind turning data into actionable knowledge

Practical introduction to the tools that will be used in the program like Python.

Introduction Quiz

Data Preprocessing

Python Basics (Datatypes,Statements,Functions,OOPS,Files,WebScraping)

Importing the Libraries

Importing the Dataset

Summary of Object-oriented programming: classes & objects

Missing Data

Categorical Data

Splitting the Dataset into the Training set and Test set

Feature Scaling

Python Numpy and Pandas.

Data Visualization(Matplotlib and Seaborn)

Assignment & Quiz

Descriptive and Inferential Statistics

Why we need Descriptive and Inferential Statistics?

Pre-requisites

Sampling Distribution and Central Limit Theorem

Hypothesis Testing

Types of Error in Hypothesis Testing

T-tests

Different types of t-test

ANOVA

Chi-Square Goodness of Fit

Regression and ANOVA

Coefficient of Determination (R-Squared)

Assignment & Quiz

Regression

Simple Linear Regression

Multiple Linear Regressions

Polynomial Regression

Decision Tree Regression

Random Forest Regression

Evaluating Regression Models Performance

R-Squared Intuition

Adjusted R-Squared Intuition

Evaluating Regression Models Performance

Assignment & Quiz

Classification

Logistic Regression

K-Nearest Neighbors (K-NN)

Support Vector Machine (SVM)

Support Vector Machines: Introduction

Support Vector Machines: Maximum Margin Hyperplane and Kernel Trick

Kernel SVM

Naive Bayes

Naive Bayes Classifier

Random Variables

Bayes Theorem

Naive Bayes Classifier

Decision Tree Classification

Planting the seed – What are Decision Trees?

Growing the Tree – Decision Tree Learning

Branching out – Information Gain

Decision Tree Algorithms

Titanic: Decision Trees predict Survival (Kaggle)

Random Forest Classification

Random Forests – Much more than trees

Back on the Titanic – Cross Validation and Random Forests

Evaluating Classification Models Performance

False Positives & False Negatives

Confusion Matrix

ROC-AUC Curve.

Clustering

K-Means Clustering

K-Nearest Neighbors

K-Nearest Neighbors: A few wrinkles

Hierarchical Clustering

Recommendation Systems

Recommendation Engines

Content-Based Filtering

Collaborative Filtering

A Neighbourhood Model for Collaborative Filtering

The Apriori Algorithm for Association Rules

Code Along – What’s my favorite movie? – Data Analysis with Pandas

Code Along – Movie Recommendation with Nearest Neighbour CF

Code Along – Top Movie Picks (Nearest Neighbour CF)

Code Along – Movie Recommendations with Matrix Factorization

Code Along – AssociationRules with the Apriori Algorithm

Assignment & quiz

Model Evolution Techniques For all types of Machine Learning.

Confusion Matrix

Gain and Lift Chart

Kolmogorov Smirnov Chart

AUC–ROC

Gini Coefficient

Concordant – Discordant Ratio

Root Mean Squared Error

Cross Validation.

Natural Language Processing (NLP)

Natural Language Processing with NLTK

Web Scraping with Beautiful Soup

A Serious NLP Application: Text Auto Summarization

Python Practical: Autosummarize News Articles

Put it to work: News Article Classification using K-Nearest Neighbors

Put it to work: News Article Classification using Naive Bayes Classifier

Python Practical: Scraping News Websites

Python Practical: Feature Extraction with NLTK

Python Practical: Classification with KNN

Python Practical: Classification with Naive Bayes

Document Distance using TF-IDF

Put it to work: News Article Clustering with K-Means and TF-IDF

Python Practical: Clustering with K Means

Assignment & quiz

Sentimental Analysis.

Sentiment Analysis – What’s all the fuss about?

ML Solutions for Sentiment Analysis – the devil is in the details

Sentiment Lexicons (with an introduction to WordNet and SentiWordNet)

Regular Expressions

Regular Expressions in Python

Put it to work : Twitter Sentiment Analysis

Twitter Sentiment Analysis – Work the API

Twitter Sentiment Analysis – Regular Expressions for Preprocessing

Twitter Sentiment Analysis – Naive Bayes, SVM and Sentiwordnet

Assignment & quiz

Dimensionality Reduction

Principal Component Analysis (PCA)

Linear Discriminant Analysis (LDA)

Kernel PCA

Model Selection & Boosting

Model Selection

XGBoost

GBM.

Assignment & quiz

Deep Learning with Keras

The Neuron

The Activation Function

How do Neural Networks work?

How do Neural Networks learn?

Gradient Descent

Stochastic Gradient Descent

Back propagation

Business Problem Description

Deep Knowledge on CNN and RNN.

Face recognition problem.

Advanced Sequence Models using LSTM.

Deep Knowledge on Deep Learning on NLP.

All kinds of RNN.

Many Use cases using Deep Learning

Assignment & quiz

Projects:

Covers Exploratory Data Analysis, Linear Regression, Logistic Regression, Decision Tree, Time Series Forecasting, Recommender Engines, Text Mining, ANN, SVM, K means Clustering, Ensemble Machine Learning Techniques

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