Statistics and Machine Learning Online Training

• What is Statistics
• Descriptive Statistics
• Central Tendency Measures
• The Story of Average
• Dispersion Measures
• Data Distributions
• Central Limit Theorem
• What is Sampling
• Why Sampling
• Sampling Methods
• Inferential Statistics
• What is Hypothesis testing
• Confidence Level
• Degrees of freedom
• what is pValue
• Chi-Square test
• What is ANOVA
• Correlation vs Regression
• Uses of Correlation & Regression

Introduction

• ML Fundamentals
• ML Common Use Cases
• Understanding Supervised and Unsupervised Learning Techniques

Clustering

• Similarity Metrics
• Distance Measure Types: Euclidean, Cosine Measures
• Creating predictive models
• Understanding K-Means Clustering
• Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
• Case study

Implementing Association rule mining

• Similarity Metrics
• What is Association Rules & its use cases?
• What is Recommendation Engine & it’s working?
• Recommendation Use-case
• Case study

Decision Tree Classifier

• How to build Decision trees
• What is Classification and its use cases?
• What is Decision Tree?
• Algorithm for Decision Tree Induction
• Creating a Decision Tree
• Confusion Matrix
• Case study

Random Forest Classifier

• What is Random Forests
• Features of Random Forest
• Out of Box Error Estimate and Variable Importance
• Case study

Naive Bayes Classifier

• Case study

Problem Statement and Analysis

• Various approaches to solve a Data Science Problem
• Pros and Cons of different approaches and algorithms.

Linear Regression

• Case study
• Introduction to Predictive Modeling
• Linear Regression Overview
• Simple Linear Regression
• Multiple Linear Regression

Logistic Regression

• Case study
• Logistic Regression Overview
• Data Partitioning
• Univariate Analysis
• Bivariate Analysis
• Multicollinearity Analysis
• Model Building
• Model Validation
• Model Performance Assessment
• Scorecard

Text Mining

• Case study

 Sentimental Analysis

• Case study

Support Vector Machines

• Case Study
• Introduction to SVMs
• SVM History
• Vectors Overview
• Decision Surfaces
• Linear SVMs
• The Kernel Trick
• Non-Linear SVMs
• The Kernel SVM

• Case Study
• Deep Learning Overview
• The Brain vs Neuron
• Introduction to Deep Learning

• The Detailed ANN
• The Activation Functions
• How do ANNs work & learn
• Gradient Descent
• Stochastic Gradient Descent
• Backpropogation

• Convolutional Operation
• Relu Layers
• What is Pooling vs Flattening
• Full Connection
• Softmax vs Cross Entropy

• Recurrent neural networks rnn
• LSTMs for beginners – understanding LSTMs
• long short term memory neural networks lstm in python

• Describe Time Series data
• Format your Time Series data
• List the different components of Time Series data
• Discuss different kind of Time Series scenarios
• Choose the model according to the Time series scenario
• Implement the model for forecasting
• Explain working and implementation of ARIMA model
• Illustrate the working and implementation of different ETS models
• Forecast the data using the respective model
• What is Time Series data?
• Time Series variables
• Different components of Time Series data
• Visualize the data to identify Time Series Components
• Implement ARIMA model for forecasting
• Exponential smoothing models
• Identifying different time series scenario based on which different Exponential Smoothing model can be applied
• Implement respective model for forecasting
• Visualizing and formatting Time Series data
• Plotting decomposed Time Series data plot
• Applying ARIMA and ETS model for Time Series forecasting
• Forecasting for given Time period
• Case Study