Computer Science · PG program

Post Graduate Program inData Science and Machine Learning

Expert curated, our online data science course helps you develop fundamental skill sets in machine learning and data science. Work on industry-relevant data sets and develop domain expertise in machine learning, artificial intelligence, and deep learning.

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Duration: 24 weeks
Content: 151 hours
Courses: 6
Projects: 12
Mode: Online

Overview

What you'll
be able to do.

Understanding the Mathematics behind different Machine Learning Algorithms
Exploring SQL from basics to advanced level to utilize its powers for the Data Science domain
Understanding Machine Learning and its implementation in Python
Hands-on experience in Python Programming
Understanding Advanced Machine Learning Algorithms and Implementation of Tensorflow

Why this program

The students will have a thorough knowledge of Data Science and Machine Learning.
They can specialize in the domain and gain complete in-depth knowledge of it.
The students are exposed to the modern trends & the standard practices followed in the industry right now.
After completing this course, the students will gain a better understanding of the concepts of Machine Learning, Artificial Intelligence, and Deep Learning.
The software used in this course such as Python and SQL will help the students to gain a better understanding of what is being taught to them and make them industry-ready.

Job Opportunities
- Data Scientist
- Machine Learning Engineer
- Data Analyst
- Business Analyst
- Data Engineer
Certifications
- Masters in Data Science
- Masters in Artificial Intelligence
- Masters in Data Science with Specialization in Machine Learning

Tools you'll use

PythonMySQLAnacondaSQLiteJupyter Notebook

Curriculum

6 courses,
70 modules.

Get the full syllabus ↓

01Core and Advanced Python Programming12 modules
1. Introduction to Python, Python BasicsW01
  - 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
2. Strings, Decision Control StatementsW02
  - Definition of string
  - Operations accessing string elements
  - Relational operators
  - Logical operators
  - Conditional expressions
  - If, If..else, If..elif
3. Repetition Statements and Console Input-OutputW03
  - Use of while and for
  - Break and continue
  - Pass and else statements
  - Formatted input and output
4. Lists, Tuples, Sets, DictionaryW04
  - Use of while and for
  - Break and continue
  - Pass and else statements
  - Formatted input and output
5. Functions and Recursion, Functional Programming and Lambda FunctionsW05
  - Defining a function
  - Types of arguments
  - Global and local variables
  - Functions as arguments
  - Implementing Lambda functions
  - Map, Reduce, and Filter functions
6. File Input-Output and ModulesW06
  - Read-write operations
  - Serialization
  - Importing a module
7. Classes and ObjectsW07
  - Class variables
  - Methods
  - Operator overloading
  - Reuse
  - Containership
  - Inheritance
8. Exception Handling, Iterators and GeneratorsW08
  - Iterables and iterators
  - Syntax errors and exceptions for:
9. Data Analysis with PandasW09
  - Installing Pandas
  - Loading files
  - Dataframes
10. Numeric and Scientific Computing using NumPyW10
  - NumPy: Introduction
  - OpenCV
  - Images and NumPy Arrays
11. Graphical User Interfaces with TkinterW11
  - Introduction to Tkinter
  - Setting up a GUI with widgets
  - Connecting GUI widgets with callback functions
12. Interacting with DatabasesW12
  - SQLite: Introduction
  - Connecting and inserting data to SQLite via Python
  - Selecting, deleting, and updating SQLite records
02Statistics and Probability for Data Sciences10 modules
1. Introduction to Machine LearningW01
  - This week we will learn about
    
    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
2. Set TheoryW02
  - This week we will learn about
    
    Set Theory
    
    Algebra of Sets
    
    Venn Diagrams
3. ProbabilityW03
  - This week we will learn about
    
    Introduction to Probability
    
    Axioms of Probability
    
    Independent events
    
    Mutually exclusive events
    
    Conditional Probability
    
    Bayes Theorem
4. StatisticsW04
  - This week we will learn about
    
    Measures of Central Tendence
    
    Measures of Dispersion
    
    Measures of Symmetry
5. Probability Distribution W05
  - This week we will learn about
    
    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
6. Continuous Probability DistributionW06
  - In this week, we will learn
    
    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
7. Inferential StatisticsW07
  - This week we will learn about
    
    Sampling
    
    Probabilistic and Nonprobabilistic methods of Sampling Estimation
    
    Estimation
    
    Sample size estimation
8. Hypothesis TestingW08
  - This week we will learn about
    
    Introduction to hypothesis testing
    
    Rejection region
    
    Critical value
    
    p-value
9. Hypothesis TestingW09
  - This week we will learn about,
    
    z - test
    
    f - test
    
    t - test
    
    Anova - test
10. Non-Parametric TestsW10
  - This week we will learn about
    
    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
03Introduction to Machine Learning Algorithms and their Implementation in Python12 modules
1. Introduction to Data Science and Programming Languages (Tools) for Data ScienceW01
  This week will cover
  - Data Science and Big Data: Introduction
  - Importance of Data Science and Big Data
  - Introduction to Different Programming Languages (Tools) for Data Science
2. Basics of ProgrammingW02
  This week will cover
  - Variables
  - Operators
  - Data Types
  - Data Structures
  - Control Structures in Python
  - Function File in Python
3. Essential Python LibrariesW03
  This week will cover
  - NumPy
  - SciPy
  - Pandas
  - Matplotlib
  - Seaborn
4. Introduction to Machine Learning Cross-Validation, Bias-Variance TradeoffW04
  This week will cover
  - Basics of Machine Learning
  - Classification
  - Fitting of Model with Cross-Validation
  - Bias Variance Tradeoff
5. Evaluation MetricsW05
  This week will cover
  - Evaluation Metrics for Model Validation
6. Importing Data and Hands-On Imported DataW06
  This week will cover
  - Exploratory Data Analysis (EDA)
  - Correlation
  - Feature Extraction
  - Hyper Parameters
7. Univariate and Multivariate Linear RegressionW07
  This week will cover
  - Univariate Linear Regression
  - Multivariate Linear Regression
  - Implementation in Python
8. Principal Component AnalysisW08
  This week will cover
  - Eigen Values
  - Eigen Vectors
  - Singular Value Decomposition
  - Principal Component Analysis (PCA)
9. Logistic Regression and k-nearest NeighborW09
  This week will cover
  - Explanation and Implementation of Logistic Regression and k-nearest Neighbor in Python
10. Decision Tree and Random ForestW10
  This week will cover
  - Explanation and Implementation in Python
11. K-Mean and Hierarchical ClusteringW11
  This week will cover
  - K-Mean
  - Hierarchical Clustering
12. Neural NetworksW12
  This week will cover
  - Logistic Regression with Neural Network Mindset
04Machine Learning Fundamentals In Depth12 modules
1. Basics of Probability and StatisticsW01
  This week, you will learn about
  - Basics of Probability
  - Basics of Statistics
  - What ML & AI is
2. Basics of Machine Learning (ML) & Artificial intelligence (AI)W02
  This week, you will learn about
  - Normal Distribution & Standard Normal Distribution: Introduction
  - Business Moments: Introduction
  - Artificial Intelligence
3. Supervised Learning - PredictionW03
  This week, you will learn about
  - Supervised learning: Introduction
  - What linear regression is
  - One hot encoding
  - Cost function and gradient descent
4. Supervised Learning - ClassificationW04
  This week, you will learn about
  - Classification problems: Introduction
  - What logistic regression is
  - Cost function and gradient descent
5. Supervised Learning - ClassificationW05
  This week, you will learn about
  - Decision tree
  - Entropy
  - Information gain
6. Random Forest & Model EvaluationW06
  This week, you will learn about
  - Random forest
  - Bootstrapping and majority rule
  - Evaluation of classifiers
7. Supervised Learning - ClassificationW07
  This week, you will learn about
  - Support Vector Machines (SVM)
  - Mathematical intuition behind SVM
  - How SVM is different from other classifiers
8. Supervised Learning - ClassificationW08
  This week, you will learn about
  - K-Nearest Neighbor
  - Lazy Algorithm
  - Single-layer Neural Network
9. Unsupervised Learning - K-MeansW09
  This week, you will learn about
  - What clustering is
  - Why clustering is important
  - K Means and elbow curve
10. Unsupervised Learning - HierarchicalW10
  This week, you will learn about
  - Hierarchical Clustering
  - Dendrogram
  - Evaluation of clustering algorithms
11. Unsupervised Learning - PCAW11
  This week, you will learn about
  - Feature Selection
  - Principal Component Analysis (PCA)
  - Mathematical intuition behind PCA
12. Supervised Learning - ClassificationW12
  This week, you will learn about
  - Artificial Neural Networks
  - Deep learning
  - Different activation functions
  - Understanding back propagation
05Advanced Deep Learning using Python12 modules
1. Artificial Neural Network (Feed Forward Neural Network)W01
  This week will cover
  - Neural networks
  - Different architectures of Neural Networks
  - Importance of Neural Networks
  - Hyperparameters in Neural Networks
  - Different types of Gradient descent methods
2. Activation Functions in Neural NetworksW02
  This week will cover
  - Conic sections
  - Hyperbolic trigonometric functions
  - Sigmoid activation function
  - Tanhx activation function
  - Relu activation function
  - Softmax activation function
3. Deep LearningW03
  This week will cover
  - 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
4. Evaluation of ModelsW04
  This week will cover
  - Underfitting
  - Overfitting
  - Lasso regularization
  - Ridge regularization
  - Elastic Net regularization
5. Improvising the ModelW05
  This week will cover
  - Ensemble methods
  - Sparse and convex functions
  - Bagging to avoid overfitting
  - Boosting to avoid underfitting
  - Stacking to avoid underfitting
6. OptimizersW06
  This week will cover
  - Frobenius norm regularization
  - Data augmentation
  - Early stopping
  - Adam optimizer
  - Tensorflow 2.0
7. Convolutional Neural Network (CNN) - Part 1W07
  This week will cover
  - Basics of CNN
  - Edge detection
  - Padding
  - Stride
  - Simple CNN
  - Difference between CNN & ANN
8. Convolutional Neural Network (CNN)- Part 2W08
  This week will cover
  - Pooling layers
  - Transfer learning
  - Examples of CNN architecture
  - Combination of different Neural network architecture
  - CNN in Python
9. Recurrent Neural Network (RNN) - Part 1W09
  This week will cover
  - RNN Model
  - Different types of RNN
  - Gradients in RNN
  - Back propagation
  - Difference between RNN & ANN
10. Recurrent Neural Network (RNN) - Part 2W10
  This week will cover
  - Gated Recurrent Unit (RNN)
  - Long short term memory (LSTM)
  - Bidirectional RNN
  - RNN Implementation in Python
11. Basics of Natural Language Processing (NLP)W11
  This week will cover
  - Stop words
  - Stemming
  - Lemmatization
  - Word2vec
  - Implementation of word2vec in Python
12. End-to-End ML Project StepsW12
  This week will cover
  - Descriptive Analytics
  - Diagnostic Analytics
  - Predictive Analytics
  - Prescriptive Analytics
06SQL for Data Science12 modules
1. IntroductionW01
  In this week, students will learn about
  - 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
2. Database Creation and ManipulationW02
  In this week, students will learn about
  - 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
3. Database SelectionW03
  In this week, you will learn about
  - 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
4. Database SelectionW04
  In this week, students will learn about
  - Generating Strings
  - String Functions
  - Date Functions
  - Conversion Functions
5. Database SelectionW05
  In this week, students will learn about
  - Comparison Operators
  - Logical Operators
  - Order By
  - Group By
  - Aggregate Functions
  - Using aggregate functions with Group by clause
  - Union Operator
  - Sub-query
6. Querying Multiple TablesW06
  In this week, students will learn about
  - 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
7. Data ExplorationW07
  In this week, students will learn about
  - What Data Exploration is
  - Structure of Data
  - Understanding the E-R Diagram
  - How to Use SQL for Data Exploration
  - Significance of
  - Other important capabilities of SQL for data exploration
  - Hands on demonstration
  - Working with NULL values
  - Making trends in Data
  - Identifying Outliers
  - Creating Data Summary
8. Index, View, TransactionW08
  In this week, students will learn about
  - 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
9. Querying with ConditionsW09
  In this week, students will learn about
  - 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
10. Stored ProceduresW10
  In this week, students will learn about
  - Stored Procedures for Data Analysis
  - Creating Stored Procedures
  - Removing Stored Procedures
  - Altering Stored Procedures
  - Conditional Statements
  - Loops
  - Hands-on working with cursors
11. Integrating SQL with ExcelW11
  In this week, you will get a hands-on understanding of the following
  - Accessing MySQL data with MS Excel
  - Running SQL statements with Excel
  - Combining Excel and SQL statements for data representation
12. Integrating SQL with PythonW12
  In this week, you will get a hands-on understanding of the following
  - Working with Python
  - Accessing SQL data with Python
  - Running basic SQL statements with Python
  - Running inbuilt python functions on SQL data

Industry projects

12 projects
you'll ship.

Our projects are designed by experts in the industry to reflect industry standards. By working through our projects, Learners will gain a practical understanding of what they will take on at a larger-scale in the industry. In total, there are 12 Projects that are available in this program.

Core and Advanced Python Programming

English Dictionary App

You will have to create an English Dictionary App that responds with definitions of English words according to the given input.

Core and Advanced Python Programming

Library Book Management System

You have to develop a Library Book Management System based on given inputs. The Management System will be implemented with the front-end GUI developed using Tkinter and the backend…

Statistics and Probability for Data Sciences

Analysis of Medical Insurance Data

For this project, learners are to do the following: The objective is to find the insurance premium and give the patients' details. The data, which contains various parameters such …

Statistics and Probability for Data Sciences

Analysis of Fermented Drink Data

For this project, learners are to do the following: Analyze the quality of two types of fermented drinks. The student will perform descriptive and inferential statistics on the dat…

Introduction to Machine Learning Algorithms and their Implementation in Python

Stock Market Data Analysis and Visualization

You will be given a scenario in which you will work as a data scientist for a stock forecasting company. With this data, you will have to perform data visualization and analysis on…

Introduction to Machine Learning Algorithms and their Implementation in Python

Developing an Early Warning System with Given Data

In this project, you are expected to develop an Early Warning System (EWS) to predict the likely rejection of products manufactured by a company using the given data.

Machine Learning Fundamentals In Depth

Descriptive Analysis and Regression Analysis

In this project, you will perform descriptive analysis on given data regarding cars. Using the data, you will have to classify cars based on various parameters and analyze the pric…

Machine Learning Fundamentals In Depth

K-Means Clustering

In this project, you will have to clean a data set regarding cars and implement the clustering algorithm using K-Means. You will then have to explain the results and find the optim…

SQL for Data Science

C2C E-Commerce User’s Behaviour Analysis

In this project, students are expected to analyze user behavior in a C2C E-Commerce business. Analyzing the user’s database will help understand the business perspective. The…

SQL for Data Science

Analyzing the choice of career for the Alumni of Engineering College.

In this project, students are expected to analyze the career choices of alumni from an institution based on given data.

Advanced Deep Learning using Python

Logistic Regression and Gradient Descent

In this project, you will perform logistic regression and gradient descent on a given dataset using Python.

Advanced Deep Learning using Python

ANN, CNN and RNN

In this project, you will have to work on prediction of machine failure for a given dataset using ANN (Hyperparameters is completely dependent on individuals to come up with the be…

Placements

4 learners placed
from this program.

Companies: 4
Median CTC: 4.78 LPA

20235 LPA

Actualise

Trainee Data Analytics

Ganesh Kumar

20232.18 LPA

Computech Business Solutions Pvt Ltd

Jr. Data Enngineer

Ashfaq Ali

20223.5 LPA

CRG Solutions

Tableau Developer

Roshin Ajith

20224.78 LPA

Renault Nissan Technology & Business Centre India Pvt ltd

Data Analyst

Raj Vishaal

Fee

One fee.
Everything included.

Become job ready with our comprehensive industry focused curriculum for freshers & early career professionals

Premium

₹2,75,000

Inclusive of all chargesEMI from ₹14,375/month

Career Service Assurance
Personalized Career Services Mentorship
1 Year Access
Personalized Page
Live Individual & Group Sessions
Personalized & Hands-On
Job-Oriented Industry Relevant Curriculum

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FAQ

Questions,
answered.

What is a data science course?

The best data science course must teach you all the basics of machine learning algorithms and would entail project work. Skill-Lync’s data science and machine learning course in India included industry projects to provide you with a hands-on experience with the tools.

Is this course industry-oriented?

Yes, Skill-Lync’s data science and machine learning course in India will teach you to code and process data based on the tools used by organisations.

What are the prerequisites needed regarding mathematics in this course?

There are no specific prerequisites for pursuing this course. However, you must have basic knowledge of mathematics.

How many projects will we work on in this course?

A total of 11 projects are included in this data science with machine learning course based on real-time scenarios. We also have projects included after each topic so that it helps you understand all the subjects correctly.

Do you teach to implement Machine Learning algorithms from scratch?

Yes, all the coding taught about any topic will be from scratch.

Can I land a job as a data scientist with this course?

Skill-Lync’s course is one of the best data science courses that covers all the information one needs, from basics to advanced, you can crack any interview and land a job that you desire.

How many hours a week do I need to allocate for my studies?

40 -50 hours a week should be enough. It also depends on how fast you can grasp the topics. If you find it difficult, you need to allocate more time for your studies.

Can you tell me more about Skill-Lync?

Skill-Lync is among India's leading EdTech platforms dedicated to transforming engineering education. We equip young engineers with the latest skill sets and cutting-edge tools in new-age technologies.

The brainchild of two engineers from Chennai, Skill-Lync, is on a mission to bridge the skill gap between aspiring professionals and the industry's demands through job-oriented courses.

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