Post Graduate Program in Data 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.
Program Overview
- 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
Syllabus
On a daily basis we talk to companies to fine tune our curriculum. Here are the list of courses that are part of this program
Why enrol in the 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
Course Syllabus
On a daily basis we talk to companies expert in these domains to fine tune our curriculum. Here are the list of courses that are part of this program. In total, there are 6 courses that are available in this program
Course 1 - Core and Advanced Python Programming
Week 1 – 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
Week 2 – Strings, Decision Control Statements
- Definition of string
- Operations accessing string elements
- Relational operators
- Logical operators
- Conditional expressions
- If, If..else, If..elif
Week 3 – Repetition Statements and Console Input-Output
- Use of while and for
- Break and continue
- Pass and else statements
- Formatted input and output
Week 4 – Lists, Tuples, Sets, Dictionary
- Use of while and for
- Break and continue
- Pass and else statements
- Formatted input and output
Week 5 – 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
Week 6 – 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
Week 7 – Classes and Objects
- Class variables
- Methods
- Operator overloading
- Reuse
- Containership
- Inheritance
Week 8 – Exception Handling, Iterators and Generators
- Iterables and iterators
- Syntax errors and exceptions for:
- try-except
- else
- finally blocks
Week 9 – Data Analysis with Pandas
- Installing Pandas
- Loading files
- CSV files
- JSON files
- Dataframes
Week 10 – Numeric and Scientific Computing using NumPy
- NumPy: Introduction
- OpenCV
- Images and NumPy Arrays
Week 11 – Graphical User Interfaces with Tkinter
- Introduction to Tkinter
- Setting up a GUI with widgets
- Connecting GUI widgets with callback functions
Week 12 – 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
Week 01 – Introduction to Machine Learning
- 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
Week 02 – Set Theory
- This week we will learn about
- Set Theory
- Algebra of Sets
- Venn Diagrams
Week 03 – Probability
- This week we will learn about
- Introduction to Probability
- Axioms of Probability
- Independent events
- Mutually exclusive events
- Conditional Probability
- Bayes Theorem
Week 04 – Statistics
- This week we will learn about
- Measures of Central Tendence
- Measures of Dispersion
- Measures of Symmetry
Week 05 – Probability Distribution
- 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
Week 06 – Continuous Probability Distribution
- 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
Week 07 – Inferential Statistics
- This week we will learn about
- Sampling
- Probabilistic and Nonprobabilistic methods of Sampling Estimation
- Estimation
- Sample size estimation
Week 08 – Hypothesis Testing
- This week we will learn about
- Introduction to hypothesis testing
- Rejection region
- Critical value
- p-value
Week 09 – Hypothesis Testing
- This week we will learn about,
- z – test
- f – test
- t – test
- Anova – test
Week 10 – Non-Parametric Tests
- 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
Course 3 - Introduction to Machine Learning Algorithms and their Implementation in Python
Week 1 – Introduction to Data Science and Programming Languages (Tools) for Data Science
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
Week 2 – Basics of Programming
This week will cover
- Variables
- Operators
- Data Types
- Data Structures
- Control Structures in Python
- Function File in Python
Week 3 – Essential Python Libraries
This week will cover
- NumPy
- SciPy
- Pandas
- Matplotlib
- Seaborn
Week 4 – Introduction to Machine Learning Cross-Validation, Bias-Variance Tradeoff
This week will cover
- Basics of Machine Learning
- Classification
- Fitting of Model with Cross-Validation
- Bias Variance Tradeoff
Week 5 – Evaluation Metrics
This week will cover
- Evaluation Metrics for Model Validation
Week 6 – Importing Data and Hands-On Imported Data
This week will cover
- Exploratory Data Analysis (EDA)
- Correlation
- Feature Extraction
- Hyper Parameters
Week 7 – Univariate and Multivariate Linear Regression
This week will cover
- Univariate Linear Regression
- Multivariate Linear Regression
- Implementation in Python
Week 8 – Principal Component Analysis
This week will cover
- Eigen Values
- Eigen Vectors
- Singular Value Decomposition
- Principal Component Analysis (PCA)
Week 9 – Logistic Regression and k-nearest Neighbor
This week will cover
- Explanation and Implementation of Logistic Regression and k-nearest Neighbor in Python
Week 10 – Decision Tree and Random Forest
This week will cover
- Explanation and Implementation in Python
- Decision Tree
- Random Forest
Week 11 – K-Mean and Hierarchical Clustering
This week will cover
- K-Mean
- Hierarchical Clustering
Week 12 – Neural Networks
This week will cover
- Logistic Regression with Neural Network Mindset
Course 4 - Machine Learning Fundamentals In Depth
Week 1 – Basics of Probability and Statistics
This week, you will learn about
- Basics of Probability
- Basics of Statistics
- What ML & AI is
Week 2 – Basics of Machine Learning (ML) & Artificial intelligence (AI)
This week, you will learn about
- Normal Distribution & Standard Normal Distribution: Introduction
- Business Moments: Introduction
- Artificial Intelligence
Week 3 – Supervised Learning – Prediction
This week, you will learn about
- Supervised learning: Introduction
- What linear regression is
- One hot encoding
- Cost function and gradient descent
Week 4 – Supervised Learning – Classification
This week, you will learn about
- Classification problems: Introduction
- What logistic regression is
- Cost function and gradient descent
Week 5 – Supervised Learning – Classification
This week, you will learn about
- Decision tree
- Entropy
- Information gain
Week 6 – Random Forest & Model Evaluation
This week, you will learn about
- Random forest
- Bootstrapping and majority rule
- Evaluation of classifiers
Week 7- Supervised Learning – Classification
This week, you will learn about
- Support Vector Machines (SVM)
- Mathematical intuition behind SVM
- How SVM is different from other classifiers
Week 8 – Supervised Learning – Classification
This week, you will learn about
- K-Nearest Neighbor
- Lazy Algorithm
- Single-layer Neural Network
Week 9 – Unsupervised Learning – K-Means
This week, you will learn about
- What clustering is
- Why clustering is important
- K Means and elbow curve
Week 10 – Unsupervised Learning – Hierarchical
This week, you will learn about
- Hierarchical Clustering
- Dendrogram
- Evaluation of clustering algorithms
Week 11 – Unsupervised Learning – PCA
This week, you will learn about
- Feature Selection
- Principal Component Analysis (PCA)
- Mathematical intuition behind PCA
Week 12 – Supervised Learning – Classification
This week, you will learn about
- Artificial Neural Networks
- Deep learning
- Different activation functions
- Understanding back propagation
Course 5 - Advanced Deep Learning using Python
Week 1 – Artificial Neural Network (Feed Forward Neural Network)
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
Week 2 – Activation Functions in Neural Networks
This week will cover
- Conic sections
- Hyperbolic trigonometric functions
- Sigmoid activation function
- Tanhx activation function
- Relu activation function
- Softmax activation function
Week 3 – Deep Learning
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
Week 4 – Evaluation of Models
This week will cover
- Underfitting
- Overfitting
- Lasso regularization
- Ridge regularization
- Elastic Net regularization
Week 5 – Improvising the Model
This week will cover
- Ensemble methods
- Sparse and convex functions
- Bagging to avoid overfitting
- Boosting to avoid underfitting
- Stacking to avoid underfitting
Week 6 – Optimizers
This week will cover
- Frobenius norm regularization
- Data augmentation
- Early stopping
- Adam optimizer
- Tensorflow 2.0
Week 7 – Convolutional Neural Network (CNN) – Part 1
This week will cover
- Basics of CNN
- Edge detection
- Padding
- Stride
- Simple CNN
- Difference between CNN & ANN
Week 8 – Convolutional Neural Network (CNN)- Part 2
This week will cover
- Pooling layers
- Transfer learning
- Examples of CNN architecture
- Combination of different Neural network architecture
- CNN in Python
Week 9 – Recurrent Neural Network (RNN) – Part 1
This week will cover
- RNN Model
- Different types of RNN
- Gradients in RNN
- Back propagation
- Difference between RNN & ANN
Week 10 – Recurrent Neural Network (RNN) – Part 2
This week will cover
- Gated Recurrent Unit (RNN)
- Long short term memory (LSTM)
- Bidirectional RNN
- RNN Implementation in Python
Week 11 – Basics of Natural Language Processing (NLP)
This week will cover
- Stop words
- Stemming
- Lemmatization
- Word2vec
- Implementation of word2vec in Python
Week 12 – End-to-End ML Project Steps
This week will cover
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
Course 6 - SQL for Data Science
Week 1 – Introduction
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
Week 2 – Database Creation and Manipulation
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
Week 3 – Database Selection
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
Week 4 – Database Selection
In this week, students will learn about
- Generating Strings
- String Functions
- Date Functions
- Conversion Functions
Week 5 – Database Selection
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
Week 6 – Querying Multiple Tables
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
Week 7 – Data Exploration
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
- 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
Week 8 – Index, View, Transaction
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
Week 9 – Querying with Conditions
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
Week 10 – Stored Procedures
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
Week 11 – Integrating SQL with Excel
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
Week 12 – Integrating SQL with Python
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
Our courses have been designed by industry experts to help students achieve their dream careers
Companies where our students got jobs
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Predict the things that are most likely to happen!
The fast-developing fields of Data Science and Machine Learning beckon students and multinational organisations. Opportunities in this field are showing an exponential rise, creating many openings. Garner the data science skill, network, and knowledge needed to succeed with the Post Graduate Program in Data Science and Machine Learning. Tech-Lync has introduced an online data science course in India to cater for the growing demand of industries.
The Post Graduate Program in Data Science and Machine Learning is designed to coach you on the fundamentals. The main topics covered in this program are:
- SQL for Data Science
- Core and Advanced Python Programming
- Introduction to Machine Learning Algorithms and their Implementation in Python
- Machine Learning Fundamentals In Depth
- Advanced Deep Learning.
Who Should Take This Course?
A software programmer or a developer who wants to venture into Data Science can apply for this course.
This course is suitable for a python developer who is just starting and wants to pursue a career in Data Science. If you want a transition in your career from non-technical to technical, then this course is for you.
Data Science is not about excelling in programming. It is in writing efficient code to analyse big data. A candidate with a basic understanding of programming languages can opt for this course.
What Will You Learn?
The Post Graduate Program in Data Science and Machine Learning is the go-to course for students to understand Deep Learning, Artificial Intelligence, SQL, and Python Programming. With the help of mathematics and statistics, you will learn to identify and create a pattern from pieces of data. Blocks of data are made available through Data Science, and this Machine Learning crash course helps you test and train your models.
The data science course syllabus focuses on teaching you how to analyse and process gathered data. You will develop the skill to derive insights based on observations to make decisions. Machine Learning and Artificial Intelligence give you skills to test and train models to make predictions that elevate businesses.
In this Machine Learning certification course, after each section of theoretical learning, you will be given real-time projects for practice to help implement theoretical data into practical data. You will learn to clean the input data of any outliers. With various cluster algorithms like Bayes, K-Mean, KNN, etc., you will also learn to classify data. With the multiple regression methods included in this course, you will grasp the techniques needed to make predictions.
Thisis one of the best data science courses that provide multiple opportunities to work on industry-relevant projects in SQL, Statistics and Probability, ML Algorithms, and Python programming. This data science machine learning course helps learners to master advanced tools through real-time project experience. Having a well-defined project portfolio is also integral to boosting your chances of being noticed by recruiters as a versatile candidate. Aspiring engineers must undergo training through project-oriented online data science courses like this one to establish themselves as job-ready professionals.
Skills You Will Gain
The following are the data science skills you can acquire from this course.
- The course will give you the confidence to develop your structured models using Deep Learning.
- It covers basic SQL that will help you in analyzing large datasets.
- It helps to get a command over the python programming language, even for those who do not have prior knowledge.
- It gives you the necessary learning to train models with the help of Machine Learning.
Key Highlights of the Program
- We coach you with all the mathematics you will ever need in Machine Learning, from basics to advanced, and have timely doubt resolution sessions.
- The course teaches the basics to those without prior knowledge of programming. Learn where to implement algorithms related to Machine Learning and Artificial Intelligence.
- The unique feature of our program is the hands-on training we provide. After every section, you will receive real-time projects to put your theoretical knowledge to practice.
- We will award a merit certification to the top 5% of the class. All students will receive a course completion certificate.
Why Should You Pursue Our Machine Learning and Data Science Course?
Tech-Lync’s machine learning certification course focuses on imparting the key technical skills that are required by the industry. We constantly revise our machine learning online course curriculum to make it aligned with the new trends that are being discovered.
We provide one of the best machine learning courses in India for engineers like you with a mission to level up the engineering workforce. This deep learning online training delves deep into the essential technicalities and teaches the most in-demand software tools that industry professionals use.
Combined with our unique pedagogy, this machine learning online training can help you either kick-start or advance your career if you are an aspiring professional.
Career Opportunities after Taking the Course
Data Science has not moved from the number one job position over the past 4 years. The corporate world today is in need of qualified Data Scientists. You only have to pursue a post-graduation in machine learning to bag these high-profile jobs with handsome salaries.
A Post Graduate in Data Science and Machine Learning gives you in-depth knowledge to become:
- Data Scientist
- Machine Learning Engineer
- Machine Learning Scientist
- Applications Architect
- Enterprise Architect
- Data Architect
- Infrastructure Architect
- Data Engineer
- Business Intelligence(BI) Developer
- Statistician
- Data Analyst
The need for big data by businesses and Government security services further increases the demand for Data Science and Machine Learning personnel. And it looks like it will stay that way for a long time.
FAQs on the Post Graduate Program in Data Science and Machine Learning
Program Fees
Connect with our career counselors to explore flexible payment options that suit your financial needs.
INR 1,20,000
Inclusive of all charges
Achieve Job Readiness with Our Extensive Industry-Aligned Program for Fresh Graduates & Early Career Professionals
Low cost EMIs and full payment discount available
EMIs starting
INR 10,000/month
- Career Services Assurance with access to our extensive network of 500+ recruitment partners for abundant interview opportunities
- Personalized Career Services Mentorship, including Resume Building, LinkedIn Profile Optimization, Mock Interviews, Communication & Aptitude Training, and an Interview Practice Toolkit.
- 5 Years Accessto Tech-Lync’s Learning Management System (LMS)
- Personalized Page to showcase Projects & Certifications
Showcase Your Projects & Certifications on a Personalized Page
Live Individual & Group Sessions to Address Queries, Discuss Progress, and Plan Studies.
- 3-Month Unpaid Internship at Tech-Lync Focused on Industry-Oriented Projects.
- Personalized and hands-on support via email and telephone ensures prompt query resolution and continuous monitoring of learner progress.
- Gain access to a job-oriented, industry-relevant curriculum curated by global industry experts, complemented by interactive live sessions.
Full Fee Upfront Payment
EMI Payment Plans
| PAYMENT TYPE | DURATION | INSTALLMENT |
|---|---|---|
| Zero Cost EMI | 3 Months | ₹40,000 |
| 6 Months | ₹20,000 | |
| EMI Loan Plans | 12 Months | ₹10,000* |
| 18 Months | ₹6,666* |
*Inclusive of Interest
Instructors profiles
Our courses are designed by leading academicians and experienced industry professionals.
6 industry experts
Our instructors are industry experts along with a passion to teach.
8 - 25 years in the experience range
Instructors with 8 – 25 years extensive industry experience.
Areas of expertise
- Machine Learning
- Deep Learning
- Electric vehicles
- Full Stack development
- SQL
- Biomedical Engineering
- Physics
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