Statistics and Machine Learning Online Training

Statistics and Machine Learning Online Training

Statistics-and-machine-learning-online-training-nareshit

About Statistics and Machine Learning Online Training

The statistic for Machine Learning is used for searching in the Web. It is very much helpful for placing Ads, Credit scoring. It can be used for stock trading. It is a very vast concept and can be used for many other applications. Learning statistic for Machine learning is helpful in learning algorithms. Machine learning gives detail idea about IN and OUT, Implementing different algorithms in the application of machine learning. You’ll learn the theoretical and practical concepts of Machine learning. It uses computer algorithms to search for any data.

Course Objectives

What are the Course Objectives?

  • Complete knowledge of Statistic for Machine learning.
  • Principal on Statistic.
  • Get knowledge of Algorithms.
  • Learn Mathematical and Heuristic concept of Machine learning.
  • Learn reinforcement and dimensionality reduction.

Who should go for this Course?

  • Any IT experienced Professional who want to be Machine Learning developer.
  • Any B.E/ B.Tech/ BSC/ M.C.A/ M.Sc Computers/ M.Tech/ BCA/ BCom College Students in any stream.
  • Fresh Graduates.

Pre-requisites:

  • Statistic
  • Development Concept

Course Curriculum

  • 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


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