Data Science Training

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
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