Artificial Intelligence, Machine Learning, Data Science Program for Students

Experienced and qualified faculty

Classroom sessions conducted by IIT, IIM alumni with 40 years of industry experience

Focus on concepts with practicals

Key Concepts

Emphasis on concepts of maths, statistics, python and AI models with hands on practice through projects

Instructor led classroom sessions


40+ hour of instructor-led classroom sessions. Additional 60+ hours for projects and assignments

Comprehensive course coverage

Career Coach and Mentor

Wide course coverage that includes Machine Learning models: Linear Regression, Classification, Tree, SVM etc.

Discounted rates for college students

Samatrix offers the special offer prices for college students. Learn artificial intelligence, machine learning, and data science

Rs 5000

Enrollment Fee – Rs 500/- (Adjustable with Total Fee)

Know about the upcoming batches

We have designed a special training program for college students that suits their academic calendar. The training programs have been tailor-made for M. Tech, B. Tech, MCA, MBA students. The upcoming batches for college students are as follows

About Artificial Intelligence, Machine Learning, Data Science Program for students

Artificial Intelligence (AI) targets to make the computers do all sort of things that brains can do. We describe some of the things, such as reasoning, as “intelligent” whereas some others such as “vision” are not. The psychological skills such as perception, association, prediction, and planning enable humans and animals to achieve their goals.

AI is everywhere. Its practical applications are found in workplaces, banks, cars, home, hospitals, even in the space. Hollywood animation, Google’s search engine, video, and computer games use AI techniques. AI helps financers predict the stock market movement and governments in policy decisions in health and transport.

Due to improvement in machine learning algorithms, availability of data and computation power, the use cases of AI have tremendously increased in recent past. Governments around the globe and corporations have been investing heavily in the AI. Due to this, the job opportunities in AI and data science have increased exponentially.

If you want to build a career as a data scientist, or a machine learning engineer and join the upcoming technological revolution, you need to build your expertise in Artificial Intelligence, Machine Learning, and Data Science.

Our program aims at developing the required skills to help you be part of the industry that has been focusing on creating intelligent machines. The program has been crafted by experts. The classroom model of learning gives you an opportunity to interact with the faculty and other batch mates, and network with experts in different domains.

Data Analysis with Python covers the topics related to Python Programming, Numpy, Pandas, Matplotlib, Seaborn, Data Analysis using Python, Statistical tools and techniques, and Linear Algebra. The detailed training in these areas will help you solve any data analysis problems

Course Curriculum

Python Programming

  1. Installation of Python 3.0 on a local machine and setting up a programming environment
  2. Your first program
  3.  IPython Basic
    1. Running IPython Shell
    2. Running Jupyter Notebook
    3. Tab Completion
    4. Introspection
    5. %run Command
    6. Magic Commands
  4. Python Programming Basics
  5. Writing comments in Python
  6.  Understanding Data Types
  7.  Working with strings
  8.  How to format text in Python 3
  9.  String Functions in Python 3
  10.  Convert Data Types
  11.  Using Variables
  12.  String Formatters
  13.  Math in Python and Built in Functions for Numbers
  14.  Boolean Logic
  15.  Lists in Python 3
  16.  Use List Methods
  17.  List Comprehensions
  18.  Tuples and Dictionaries
  19.  How to import and write modules
  20.  Conditional statements
  21.  For, and While loops and Break, Continue and Pass Statements
  22. Functions and Lambda Functions
  23. Error and Exception Handling


  1. Figures and Subplots
  2. Colors, Markers, and Line Styles
  3. Ticks, Labels, and Legends
  4. Annotation and Drawing on a subplot


  1. Line, Bar, Histogram, Density, Scatter Plots


  1. Numpy ndarray
  2. Creating ndarrays
  3. Arithmetic with ndarrays
  4. Basic Indexing and Slicing
  5. Boolean Indexing
  6. Transposing Arrays
  7. Universal Functions
  8. Array Oriented Programming with Arrays
  9. File Input and Output
  10. Pseudorandom Number Generation


  1. Pandas Data Structures
  2. Series
  3. DataFrame
  4. Reindexing
  5. Dropping Entries from an axis
  6. Indexing, Selection and Filtering
  7. Integer Index
  8. Arithmetic and Data Alignment
  9. Function Application and Mapping
  10. Sorting and Ranking
  11. Reading and Writing Data

Machine Learning

  1. Prediction
    • Dependent Variable vs Independent Variables
    • Reducible Error and Irreducible Error
    • Expected Value and Variance
  2. Inference
    • Which Predictors are associated with Response?
    • Relationship between response and predictors
  3. Learning Methods
    • Parametric Methods
    • Non Parametric Methods
  4. Model Flexibility vs Interpretability
  5. Model Accuracy and Selection
    • Quality of Fit
    • Bias – Variance Trade Off
    • Bayes Classifier
    • K-Nearest Neighbors


  1. Graphically Displaying Single Variable
  2. Mean, Median
  3. Range, Variance, Standard Deviation
  4. Scatterplot
  5. Scatterplot Matrix
  6. Covariance and Correlation
  7. Probability
  8. Joint Probability
  9. Independent events
  10. Conditional probability
  11. Bayes’ Theorem
  12. Prior, Likelihood and Posterior
  13. Discrete Random Variable
  14. Probability Distribution of
  15. Discrete Random Variable
  16. Binomial Distribution
  17. Continuous Random Variables
  18. Probability Distribution Function
  19. Point Estimation
  20. Interval Estimation
  21. Expectation Theory
  22. Hypothesis Testing – one sided, two sided

Linear Algebra

  1. Systems of Linear Equations
  2. Row Reduction and Echelon Forms
  3. Existence and Uniqueness
  4. Vectors and Matrix Equations
  5. Vector Matrix Products
  6. Linear Independence
  7. Matrix Multiplication
  8. Transpose of Matrix
  9. Inverse of Matrix
  10. Eigenvalues and Eigenvectors

Linear Regression

  1. Basic Concepts
  2. Construction of Regression Model
    • Selection of Predictor Variables
    • Functional Form of Regression Relations
    • Scope of Model
  3. Uses of Regression Analysis
    • Description
    • Control
    • Prediction
    • Regression and Causality
  4. Formal Statement of Model
  5. Important Features of Model
  6. Meaning of Regression Parameters
  7. Steps in Regression Analysis
  8. Estimation of Regression Function
    • Least Square Estimator
    • Estimating the Coefficients
    • Gradient Descent
    • Estimation of Variance Terms
  9. Accuracy of Coefficients
  10. Accuracy of Model
    • Residual Standard Error
    • R Square Statistics


  1. Basic Concept with Example
  2. Why not Linear Regression
  3. Logistic Regression
    • Logistic Model
    • Estimating Regression Coefficients
    • Multiple Logistic Regressions
  4. Linear Discriminant Analysis
  5. Nearest Neighbour Methods

Machine Learning Models

  1. Decision Tree Model
  2. Support Vector Machine
  3. Unsupervised Learning
  4. Neural Networks


  1. Random Walks, Simulating many random Walks
  2. Determine Brand Persona for Cycle Sharing Scheme using statistical tools and techniques
  3. Build a model of housing prices in Boston based on Boston Suburb Data
  4. Predict whether an individual will default on Credit Card Payment based on annual income and monthly credit card balance

Key Learning Areas

The learning areas include artificial intelligence, python programming language, data analysis using matplotlib, seaborn, numpy and pandas. The program focuses on building a base in linear algebra, probability, and statistical distributions. The fundamentals of machine learning, regression, and classification techniques using scikit-learn. 


Learn concepts of Python programming language
Data Science

Art of analyzing big data and drawing inferences
Machine Learning

Build intelligent machine using statistical techniques

Key Program Features

Join the training program

Learn the latest technology and be part of a revolution. Avail the discounted prize offer for students.

Training Fee Rs 5000  / Enrollment Fee Rs 500

Enrollment Fee of Rs 500 is part of the training Fee.

Payment Plan

Rs 500 – Enrollment Fee

Rs 4500 – At the beginning of course

Total Duration – 48 Hours

Paytm – 8800090471

Training Location – NDIIT Kalkaji New Delhi, Gurgaon, SGT University Gurgaon                                                                                                                                                                                                                                                                                                                                         

Watch our live training sessions to know
more about our training pedagogy

Our Office Locations

Head Office - Gurgaon
Samatrix Consulting Pvt Ltd
311 Vipul Trade Centre
Sector 48, Sohna Road
Gurgaon - 122018
New Delhi
105. Nehru Pl Road,
Block 1, Kalkaji
New Delhi – India
166, Second Floor,
5th Main Road, MC Layout,
Opp BDA Complex,
Karnataka – India
Samatrix Consulting Pvt Ltd
1st Floor,
Near Shakthi Chowk,
Civil Lines
Bijnor – Uttar Pradesh – India


we want to hear from you

Don’t Be A Stranger…

Just write down some details and our customer success heroes will get back to you in a jiffy!

Contact Info