What is the total duration of this Master's Program in Data Science and Machine Learning course?

The course can be completed within 8 months duration.

Will the Master's Program in Data Science and Machine Learning Live Course offer placement assistance to learners?

No, the Master's Program in Data Science and Machine Learning Training Course does not offer placement assistance to learners.

Where can I access the Master's Program in Data Science and Machine Learning course?

The Master's Program in Data Science and Machine Learning Online Course can be accessed on the Skill-Lync platform.

Post Graduate Program in Data Science and Machine Learning by Skill Lync: Fee, Duration, How to Apply

Post Graduate Program in Data Science and Machine Learning

Skill Lync

Learn about the concepts and applications of data science and machine learning through the Master's Program in Data Science and Machine Learning Course.

Online

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based	Learning efforts 40-50 Hours Per Week

particular

details

                                    Medium of instructions
                                    English

                                    Mode of learning
                                    Self study

                                    Mode of Delivery
                                    Video and Text Based

                                    Learning efforts
                                    40-50 Hours Per Week
                                

Course 1: Core and Advanced Python Programming

Introduction to Python, Python Basics

Features and uses of Python
Program execution
Installation of IDE
Identifiers and keywords
Types of comments
Data types
Variables
Arithmetic operators
Assignment operators
Input and print statements

Strings, Decision Control Statements

Definition of string
Operations accessing string elements
Relational operators
Logical operators
Conditional expressions
If, If..else, If..elif

Repetition Statements and Console Input-Output

Use of while and for
Break and continue
Pass and else statements
Formatted input and output

Lists, Tuples, Sets, Dictionary

Use of while and for
Break and continue
Pass and else statements
Formatted input and output

Functions and Recursion, Functional Programming and Lambda Functions

Defining a function
Types of arguments
Global and local variables
Functions as arguments
Implementing Lambda functions
Map, Reduce, and Filter functions

File Input-Output and Modules

Read-write operations
With the keyword
File opening modes
Moving within a file
Serialization
File and directory operations
Importing a module
Variations of import
Third-party packages

Classes and Objects

Class variables
Methods
Operator overloading
Reuse
Containership
Inheritance

Exception Handling, Iterators and Generators

Iterables and iterators
Syntax errors and exceptions for:
try-except
else
finally blocks

Data Analysis with Pandas

Installing Pandas
Loading files
CSV files
JSON files
Dataframes

Numeric and Scientific Computing using NumPy

NumPy: Introduction
OpenCV
Images and NumPy Arrays

Graphical User Interfaces with Tkinter

Introduction to Tkinter
Setting up a GUI with widgets
Connecting GUI widgets with callback functions

Interacting with Databases

SQLite: Introduction
Connecting and inserting data to SQLite via Python
Selecting, deleting, and updating SQLite records

Course 2: Statistics and Probability for Data Sciences

Introduction to Machine Learning

Introduction to Artificial Intelligence
Introduction to Machine Learning
Supervised, Unsupervised, and Reinforced Learning
Introduction to Deep Learning
Modules needed to implement a Machine Learning model

Set Theory

Set Theory
Algebra of Sets
Venn Diagrams

Probability

Introduction to Probability
Axioms of Probability
Independent events
Mutually exclusive events
Conditional Probability
Bayes Theorem

Statistics

Measures of Central Tendence
Measures of Dispersion
Measures of Symmetry

Probability Distribution

Concept of Random variable
Bernoulli distribution
Binomial distribution
Negative Binomial distribution
Geometric distribution
Hypergeometric distribution
Poisson distribution
Uniform distribution
Probability mass function and cumulative distribution function
Brief intro to Gamma exponential and normal distribution

Continuous Probability Distribution

Continuous distributions
Normal Distribution
Gamma Distribution
Exponential Distribution
Lognormal Distribution
Weibull Distribution
F Distribution
T Distribution
chi square Distribution
Probabiltiy Density Function
Cumulative Distribution Function

Inferential Statistics

Sampling
Probabilistic and Nonprobabilistic methods of Sampling Estimation
Estimation
Sample size estimation

Hypothesis Testing

Introduction to hypothesis testing
Rejection region
Critical value
p-value

Hypothesis Testing

z - test
f - test
t - test
Anova - test

Non-Parametric Tests

Chi square test
Mann Whitney U test
Kruskal Wallis test
Sign test
Correlation
Chi square
Karl Pearson
Spearman Coefficient
Regression between variables
Implementation of statistical functions in Jupyter notebook

Course 3: Introduction to Machine Learning Algorithms and their Implementation in Python

Introduction to Data Science and Programming Languages (Tools) for Data Science

Data Science and Big Data: Introduction
Importance of Data Science and Big Data
Introduction to Different Programming Languages (Tools) for Data Science

Basics of Programming

Variables
Operators
Data Types
Data Structures
Control Structures in Python
Function File in Python

Essential Python Libraries

NumPy
SciPy
Pandas
Matplotlib
Seaborn

Introduction to Machine Learning Cross-Validation, Bias-Variance Tradeoff

Basics of Machine Learning
Classification
Fitting of Model with Cross-Validation
Bias Variance Tradeoff

Evaluation Metrics

Evaluation Metrics for Model Validation

Importing Data and Hands-On Imported Data

Exploratory Data Analysis (EDA)
Correlation
Feature Extraction
Hyper Parameters

Univariate and Multivariate Linear Regression

Univariate Linear Regression
Multivariate Linear Regression
Implementation in Python

Principal Component Analysis

Eigen Values
Eigen Vectors
Singular Value Decomposition
Principal Component Analysis (PCA)

Logistic Regression and k-nearest Neighbor

Explanation and Implementation of Logistic Regression and k-nearest Neighbor in Python

Decision Tree and Random Forest

Explanation and Implementation in Python
Decision Tree
Random Forest

K-Mean and Hierarchical Clustering

K-Mean
Hierarchical Clustering

Neural Networks

Logistic Regression with Neural Network Mindset

Introduction to Data Science and Programming Languages (Tools) for Data Science

Data Science and Big Data: Introduction
Importance of Data Science and Big Data
Introduction to Different Programming Languages (Tools) for Data Science

Basics of Programming

Variables
Operators
Data Types
Data Structures
Control Structures in Python
Function File in Python

Essential Python Libraries

NumPy
SciPy
Pandas
Matplotlib
Seaborn

Introduction to Machine Learning Cross-Validation, Bias-Variance Tradeoff

Basics of Machine Learning
Classification
Fitting of Model with Cross-Validation
Bias Variance Tradeoff

Evaluation Metrics

Evaluation Metrics for Model Validation

Importing Data and Hands-On Imported Data

Exploratory Data Analysis (EDA)
Correlation
Feature Extraction
Hyper Parameters

Univariate and Multivariate Linear Regression

Univariate Linear Regression
Multivariate Linear Regression
Implementation in Python

Principal Component Analysis

Eigen Values
Eigen Vectors
Singular Value Decomposition
Principal Component Analysis (PCA)

Logistic Regression and k-nearest Neighbor

Explanation and Implementation of Logistic Regression and k-nearest Neighbor in Python

Decision Tree and Random Forest

Explanation and Implementation in Python
Decision Tree
Random Forest

K-Mean and Hierarchical Clustering

K-Mean
Hierarchical Clustering

Neural Networks

Logistic Regression with Neural Network Mindset

Course 4: Machine Learning Fundamentals In Depth

Basics of Probability and Statistics

Basics of Probability
Basics of Statistics
What ML & AI is

Basics of Machine Learning (ML) & Artificial intelligence (AI)

Normal Distribution & Standard Normal Distribution: Introduction
Business Moments: Introduction
Artificial Intelligence

Supervised Learning - Prediction

Supervised learning: Introduction
What linear regression is
One hot encoding
Cost function and gradient descent

Supervised Learning - Classification

Classification problems: Introduction
What logistic regression is
Cost function and gradient descent

Supervised Learning - Classification

Decision tree
Entropy
Information gain

Supervised Learning - Classification

Support Vector Machines (SVM)
Mathematical intuition behind SVM
How SVM is different from other classifiers

Supervised Learning - Classification

K-Nearest Neighbor
Lazy Algorithm
Single-layer Neural Network

Unsupervised Learning - K-Means

What clustering is
Why clustering is important
K Means and elbow curve

Unsupervised Learning - Hierarchical

Hierarchical Clustering
Dendrogram
Evaluation of clustering algorithms

Unsupervised Learning - PCA

Feature Selection
Principal Component Analysis (PCA)
Mathematical intuition behind PCA

Supervised Learning - Classification

Artificial Neural Networks
Deep learning
Different activation functions
Understanding back propagation

Machine Learning Fundamentals In Depth

Random Forest & Model Evaluation

Random forest
Bootstrapping and majority rule
Evaluation of classifiers

Course 5: Advanced Deep Learning using Python

Artificial Neural Network (Feed Forward Neural Network)

Neural networks
Different architectures of Neural Networks
Importance of Neural Networks
Hyperparameters in Neural Networks
Different types of Gradient descent methods

Activation Functions in Neural Networks

Conic sections
Hyperbolic trigonometric functions
Sigmoid activation function
Tanhx activation function
Relu activation function
Softmax activation function

Deep Learning

Deep learning terminologies
Nomenclature
Order of vectorized forms
Forward propagation derivation with 1 layer
Back propagation derivation with 1 layer
Batch size, iteration and epoch

Evaluation of Models

Underfitting
Overfitting
Lasso regularization
Ridge regularization
Elastic Net regularization

Improvising the Model

Ensemble methods
Sparse and convex functions
Bagging to avoid overfitting
Boosting to avoid underfitting
Stacking to avoid underfitting

Optimizers

Frobenius norm regularization
Data augmentation
Early stopping
Adam optimizer
Tensorflow 2.0

Convolutional Neural Network (CNN) - Part 1

Basics of CNN
Edge detection
Padding
Stride
Simple CNN
Difference between CNN & ANN

Convolutional Neural Network (CNN)- Part 2

Pooling layers
Transfer learning
Examples of CNN architecture
Combination of different Neural network architecture
CNN in Python

Recurrent Neural Network (RNN) - Part 1

RNN Model
Different types of RNN
Gradients in RNN
Back propagation
Difference between RNN & ANN

Recurrent Neural Network (RNN) - Part 2

Gated Recurrent Unit (RNN)
Long short term memory (LSTM)
Bidirectional RNN
RNN Implementation in Python

Basics of Natural Language Processing (NLP)

Stop words
Stemming
Lemmatization
Word2vec
Implementation of word2vec in Python

End-to-End ML Project Steps

Descriptive Analytics
Diagnostic Analytics
Predictive Analytics
Prescriptive Analytics

Artificial Neural Network (Feed Forward Neural Network)

Neural networks
Different architectures of Neural Networks
Importance of Neural Networks
Hyperparameters in Neural Networks
Different types of Gradient descent methods

Activation Functions in Neural Networks

Conic sections
Hyperbolic trigonometric functions
Sigmoid activation function
Tanhx activation function
Relu activation function
Softmax activation function

Deep Learning

Deep learning terminologies
Nomenclature
Order of vectorized forms
Forward propagation derivation with 1 layer
Back propagation derivation with 1 layer
Batch size, iteration and epoch

Evaluation of Models

Underfitting
Overfitting
Lasso regularization
Ridge regularization
Elastic Net regularization

Improvising the Model

Ensemble methods
Sparse and convex functions
Bagging to avoid overfitting
Boosting to avoid underfitting
Stacking to avoid underfitting

Optimizers

Frobenius norm regularization
Data augmentation
Early stopping
Adam optimizer
Tensorflow 2.0

Convolutional Neural Network (CNN) - Part 1

Basics of CNN
Edge detection
Padding
Stride
Simple CNN
Difference between CNN & ANN

Convolutional Neural Network (CNN)- Part 2

Pooling layers
Transfer learning
Examples of CNN architecture
Combination of different Neural network architecture
CNN in Python

Recurrent Neural Network (RNN) - Part 1

RNN Model
Different types of RNN
Gradients in RNN
Back propagation
Difference between RNN & ANN

Recurrent Neural Network (RNN) - Part 2

Gated Recurrent Unit (RNN)
Long short term memory (LSTM)
Bidirectional RNN
RNN Implementation in Python

Basics of Natural Language Processing (NLP)

Stop words
Stemming
Lemmatization
Word2vec
Implementation of word2vec in Python

End-to-End ML Project Steps

Descriptive Analytics
Diagnostic Analytics
Predictive Analytics
Prescriptive Analytics

Course 6: SQL for Data Science

Introduction

Data Science: Introduction
Data Science Applications
Why SQL is required for Data Science
Database Management System (DBMS)
Relational Database Management System (RDBMS)
Basic terminology in RDBMS
Data Constraints
Entity Relationship Model
What SQL is
Categories of SQL Commands
Hands-on execution of simple SQL statements on RDBMS tool

Database Creation and Manipulation

Detailed SQL Data types
Creating databases
Create Tables
Using Constraints
Inserting Table
Altering Table structure
Dropping Database and Table
Deleting and Updating
Hands-on importing of sample database schema

Database Selection

Select statements
Removing Duplicate use of Alias
Use of Where
Use of Wildcards
Limit clause
Arithmetic Operators
Mathematical Functions
Hands-on creating of backups and restore for large database

Database Selection

Generating Strings
String Functions
Date Functions
Conversion Functions

Database Selection

Comparison Operators
Logical Operators
Order By
Group By
Aggregate Functions
Using aggregate functions with Group by clause
Union Operator
Sub-query

Querying Multiple Tables

The need to Join Multiple Tables
Cartesian Product
Inner Join
Left Join
Right Join
Self Join
Delete Join
Update Join
Hands-on demonstration of joining more than two tables in a sample database

Data Exploration

What Data Exploration is
Structure of Data
Understanding the E-R Diagram
How to Use SQL for Data Exploration
Significance of
Joins
Sub queries
Inbuilt functions
Other important capabilities of SQL for data exploration
Hands on demonstration
Working with NULL values
Making trends in Data
Identifying Outliers
Creating Data Summary

Index, View, Transaction

Creating Index
Use of Index
Type of Index and Ine
X Strategies
Views
Views for Data Analysis
Multi-user database
What is Transaction
Save points
Hands-on working on Multi user database environment

Querying with Conditions

Querying with Conditions
The Searched Case Expression
The Simple Case Expression
Applications of Case Expression
Common Error Codes
Hands-on working with Json type data

Stored Procedures

Stored Procedures for Data Analysis
Creating Stored Procedures
Removing Stored Procedures
Altering Stored Procedures
Conditional Statements
Loops
Hands-on working with cursors

Integrating SQL with Excel

Accessing MySQL data with MS Excel
Running SQL statements with Excel
Combining Excel and SQL statements for data representation

Integrating SQL with Python

Working with Python
Accessing SQL data with Python
Running basic SQL statements with Python
Running inbuilt python functions on SQL data

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Post Graduate Program in Data Science and Machine Learning

Online

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Course and certificate fees

certificate availability

certificate providing authority

Who it is for

What you will learn

The syllabus

Course 1: Core and Advanced Python Programming

Introduction to Python, Python Basics

Strings, Decision Control Statements

Repetition Statements and Console Input-Output

Lists, Tuples, Sets, Dictionary

Functions and Recursion, Functional Programming and Lambda Functions

File Input-Output and Modules

Classes and Objects

Exception Handling, Iterators and Generators

Data Analysis with Pandas

Numeric and Scientific Computing using NumPy

Graphical User Interfaces with Tkinter

Interacting with Databases

Course 2: Statistics and Probability for Data Sciences

Introduction to Machine Learning

Set Theory

Probability

Statistics

Probability Distribution

Continuous Probability Distribution

Inferential Statistics

Hypothesis Testing

Hypothesis Testing

Non-Parametric Tests

Course 3: Introduction to Machine Learning Algorithms and their Implementation in Python

Introduction to Data Science and Programming Languages (Tools) for Data Science

Basics of Programming

Essential Python Libraries

Introduction to Machine Learning Cross-Validation, Bias-Variance Tradeoff

Evaluation Metrics

Importing Data and Hands-On Imported Data

Univariate and Multivariate Linear Regression

Principal Component Analysis

Logistic Regression and k-nearest Neighbor

Decision Tree and Random Forest

K-Mean and Hierarchical Clustering

Neural Networks

Introduction to Data Science and Programming Languages (Tools) for Data Science

Basics of Programming

Essential Python Libraries

Introduction to Machine Learning Cross-Validation, Bias-Variance Tradeoff

Evaluation Metrics

Importing Data and Hands-On Imported Data

Univariate and Multivariate Linear Regression

Principal Component Analysis

Logistic Regression and k-nearest Neighbor

Decision Tree and Random Forest

K-Mean and Hierarchical Clustering

Neural Networks

Course 4: Machine Learning Fundamentals In Depth

Basics of Probability and Statistics

Basics of Machine Learning (ML) & Artificial intelligence (AI)

Supervised Learning - Prediction

Supervised Learning - Classification

Supervised Learning - Classification

Supervised Learning - Classification

Supervised Learning - Classification

Unsupervised Learning - K-Means

Unsupervised Learning - Hierarchical

Unsupervised Learning - PCA

Supervised Learning - Classification

Machine Learning Fundamentals In Depth

Random Forest & Model Evaluation

Course 5: Advanced Deep Learning using Python

Artificial Neural Network (Feed Forward Neural Network)

Activation Functions in Neural Networks

Deep Learning