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DATA SCIENCE WITH MACHINE LEARNING

REAL TIME SIGNALS TECHNOLOGIES PVT LTD

Data Analytics/Data Science/Machine Learning

25000

7y ago

Course Duration:3 Months

Course Details

1. What is Data Science

Demand of Data Science

Venn Diagram

Pipeline

Roles

Team

Knowledge Check

2. Field of study

Big Data overview

Programming involvement in Data Science

Statistics

Knowledge check

3. Ethics

Ethical issues

Knowledge check

4. Data Sources (Getting Data)

Data Metrics

Existing data

APIs

Scraping

Creating Data

Knowledge check

5. Data Exploration (Cleaning Data)

Exploratory graphs

Exploratory statistics

Knowledge check

6. Programming

Spreadsheets

R programming

Python

SQL

Web formats

Knowledge check

7.Mathematics

Algebra

Systems of equations

Calculus

Big O

Bayes probability

Knowledge check

8. Applied Statistics

Hypothesis

Confidence

Problems

Validating

Knowledge check

9. Machine Learning

Linear Regression with one and multiple variables.

Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent method for learning.

Cost function

Gradient descent

Normal Equations

Logistic regression. What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features.

Cost Function

Gradient descent solution.

Neural Networks. Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech;

Back propagation

Application of Neural Network

Support Vector Machines (SVM). Support vector machines, or SVMs, is a machine learning algorithm for classification. We introduce the idea and intuitions behind SVMs and discuss how to use it in practice.

Large Margin classification

Kernels

UNSUPERVISED

Clustering

Gaussian Mixture Models

HMM

10. R Programming

Writing code and setting your working directory

Getting started and R nuts and Bolts

R console Input and evaluation

Data types – R Objects and attributes

Data types – Vectors and Lists

Data types – Matrices

Data types – Factors

Data types – Missing values

Data types – Data frames

Data types – Names Attributes

Data types – summary

Reading Tabular Data

Reading large tables

Textual data formats

Connections: Interfaces to outside world

Subsettings – Basics

Subsettings – Lists

Subsettings – Matrices

Subsettings – Partial Matching

Subsettings – Removing Missing values

Vectorized Operations

10. Communicating

Interpretability

Actionable insights

Visualization for presentation

Reproducible research

Knowledge check

Conclusion and final test

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