Real Time Experts
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Real time Experts offers best Data science with Python
Training in Bangalore with most experienced professionals. Our Instructors are
working in Data science with Python and related technologies for more years in
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with Python trainers offers Data science with Python in Classroom training, Data
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requirements for both beginner level to advanced level. Our training will be
handled in either weekday or weekends program depends on participants
requirement. We do offer Fast-Track Data science with Python Training in
Bangalore and One-to-One Data science with Python Training in Bangalore. Here
are the major topics we cover under this Data science with Python course Syllabus
Introduction to Data science with Python, Data science with Python Indices and
Performance Tuning, Data science with Python Application Utilities.Every topic
will be covered in mostly practical way with examples.
Real time Experts located in various places in Bangalore. We
are the best Training Institute offers certification oriented Data science with
Python Training in Bangalore. Our participants will be eligible to clear all
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timings and start date as well in below.
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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