Best Data Science Training in Marathahalli

Datascience Curriculum

Python :

Goal – Get an overview of the python which is required to work on data science

Objectives – At the end of this Module, you should be able understand the following topics

• Lists
• Tuples
• Dictionaries
• Sets
• Importing packages
• If else
• Loops
• Comprehensions
• Functions
• Map
• Filter
• Reduce
• Numpy
• Pandas
• Merging,querying,aggregating
• Assignments for practice

R :

Goal – Get an overview of the R which is required to work on data science

Objectives – At the end of this Module, you should be able understand the following topics

• Introduction
• Basic operations in R
• Vectors
• Factors
• Matrices
• Data frames
• Lists
• Logical and Relational operators
• Conditional Statements
• Loops
• Functions
• Apply Family

Introduction

• Applications of Machine Learning
• Why Machine Learning is the Future
• Installing R and R Studio (MAC & Windows)
• Installing Python and Anaconda (MAC & Windows)

 ————————– Part  Data Preprocessing ————————–

• Welcome to Part  – Data Preprocessing
• Get the dataset
• Importing the Libraries
• Importing the Dataset
• For Python learners, summary of Object-oriented programming classes & objects
• Missing Data
• Categorical Data
• Splitting the Dataset into the Training set and Test set
• Feature Scaling
• And here is our Data Preprocessing Template!
• Quiz  Data Preprocessing

—————————— Part  Regression ——————————

• Welcome to Part  – Regression

 Simple Linear Regression

• How to get the dataset
• Dataset + Business Problem Description
• Simple Linear Regression Intuition –   
• Simple Linear Regression in Python –  
• Simple Linear Regression in R –  
• Quiz  Simple Linear Regression

Multiple Linear Regression

• How to get the dataset
• Dataset + Business Problem Description
• Multiple Linear Regression Intuition –   
• Multiple Linear Regression in Python –   
• Multiple Linear Regression in Python – Backward Elimination – Preparation
• Multiple Linear Regression in Python – Backward Elimination –  !
• Multiple Linear Regression in Python – Backward Elimination –  Solution
• Multiple Linear Regression in R –   
• Multiple Linear Regression in R – Backward Elimination –  !
• Multiple Linear Regression in R – Backward Elimination –  Solution
• Quiz  Multiple Linear Regression

Polynomial Regression

• Polynomial Regression Intuition
• How to get the dataset
• Polynomial Regression in Python –  
• Python Regression Template
• Polynomial Regression in R –   
• R Regression Template

Support Vector Regression (SVR)

• How to get the dataset
• SVR in Python
• SVR in R

Decision Tree Regression

• Decision Tree Regression Intuition
• How to get the dataset
• Decision Tree Regression in Python
• Decision Tree Regression in R

 Random Forest Regression

• Random Forest Regression Intuition
• How to get the dataset
• Random Forest Regression in Python
• Random Forest Regression in R

 Evaluating Regression Models Performance

• R-Squared Intuition
• Adjusted R-Squared Intuition
• Evaluating Regression Models Performance – ‘s Final Part
• Interpreting Linear Regression Coefficients
• Conclusion of Part  – Regression

 —————————- Part  Classification —————————-

• Welcome to Part  – Classification

 Logistic Regression

• Logistic Regression Intuition
• How to get the dataset
• Logistic Regression in Python –  
• Python Classification Template
• Logistic Regression in R –   
• R Classification Template
• Quiz  Logistic Regression

 K-Nearest Neighbors (K-NN)

• K-Nearest Neighbor Intuition
• How to get the dataset
• K-NN in Python
• K-NN in R
• Quiz  K-Nearest Neighbor

 Support Vector Machine (SVM)

• SVM Intuition
• How to get the dataset
• SVM in Python
• SVM in R
  • SVMzip

Kernel SVM

• Kernel SVM Intuition
• Mapping to a higher dimension
• The Kernel Trick
• Types of Kernel Functions
• How to get the dataset
• Kernel SVM in Python
• Kernel SVM in R

Naive Bayes

• Bayes Theorem
• Naive Bayes Intuition
• Naive Bayes Intuition (Challenge Reveal)
• Naive Bayes Intuition (Extras)
• How to get the dataset
• Naive Bayes in Python
• Naive Bayes in R

 Decision Tree Classification

• Decision Tree Classification Intuition
• How to get the dataset
• Decision Tree Classification in Python
• Decision Tree Classification in R

 Random Forest Classification

• Random Forest Classification Intuition
• How to get the dataset
• Random Forest Classification in Python
• Random Forest Classification in R

 Evaluating Classification Models Performance

• False Positives & False Negatives
• Confusion Matrix
• Accuracy Paradox
• CAP Curve
• CAP Curve Analysis
• Conclusion of Part  – Classification

 —————————- Part  Clustering —————————-

• Welcome to Part  – Clustering

 K-Means Clustering

• K-Means Clustering Intuition
• K-Means Random Initialization Trap
• K-Means Selecting The Number Of Clusters
• How to get the dataset
• K-Means Clustering in Python
• K-Means Clustering in R
• Quiz  K-Means Clustering

Hierarchical Clustering

• Hierarchical Clustering Intuition
• Hierarchical Clustering How Dendrograms Work
• Hierarchical Clustering Using Dendrograms
• How to get the dataset
• HC in Python –   
• HC in R –   
• Quiz  Hierarchical Clustering
• Conclusion of Part  – Clustering

 ———————- Part  Association Rule Learning ———————-

• Welcome to Part  – Association Rule Learning

 Apriori

• Apriori Intuition
• How to get the dataset
• Apriori in R –   
• Apriori in Python –  

Eclat

• Eclat Intuition
• How to get the dataset
• Eclat in R
  • Eclatzip

 ———————— Part  Reinforcement Learning ————————

• Welcome to Part  – Reinforcement Learning

Upper Confidence Bound (UCB)

• The Multi-Armed Bandit Problem
• Upper Confidence Bound (UCB) Intuition
• How to get the dataset
• Upper Confidence Bound in Python –   
• Upper Confidence Bound in R –  

Thompson Sampling

• Thompson Sampling Intuition
• Algorithm Comparison UCB vs Thompson Sampling
• How to get the dataset
• Thompson Sampling in Python –  
• Thompson Sampling in Python –  
• Thompson Sampling in R –  
• Thompson Sampling in R –  

 ——————— Part  Natural Language Processing ———————

• Welcome to Part  – Natural Language Processing
• How to get the dataset
• Natural Language Processing in Python –   
• Challenge
• Natural Language Processing in R –  
• Natural Language Processing in R –  
• Challenge

 —————————- Part  Deep Learning —————————-

• Welcome to Part  – Deep Learning
• What is Deep Learning?

 Artificial Neural Networks

• Plan of attack
• The Neuron
• The Activation Function
• How do Neural Networks work?
• How do Neural Networks learn?
• Gradient Descent
• Stochastic Gradient Descent
• Backpropagation
• How to get the dataset
• Business Problem Description
• ANN in Python –   – Installing Theano, Tensorflow and Keras
• ANN in R –   
• ANN in R –   (Last )

 Convolutional Neural Networks

• Plan of attack
• What are convolutional neural networks?
•  – Convolution Operation
• (b) – ReLU Layer
•  – Pooling
•  – Flattening
•  – Full Connection
• Summary
• Softmax & Cross-Entropy
• How to get the dataset
• CNN in Python –  
• CNN in R

 ———————– Part  Dimensionality Reduction ———————–

• Welcome to Part  – Dimensionality Reduction

 Principal Component Analysis (PCA)

• How to get the dataset
• PCA in Python –   
• PCA in R –  

Linear Discriminant Analysis (LDA)

• How to get the dataset
• LDA in Python
• LDA in R

 Kernel PCA

• How to get the dataset
• Kernel PCA in Python
• Kernel PCA in R

 ——————— Part  Model Selection & Boosting ———————

• Welcome to Part  – Model Selection & Boosting

Model Selection

• How to get the dataset
• k-Fold Cross Validation in Python
• k-Fold Cross Validation in R
• Grid Search in Python –   
• Grid Search in R

XGBoost

• How to get the dataset
• XGBoost in Python –   
• XGBoost in R