Artificial Intelligence Machine Learning with Python

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Artificial Intelligence Machine Learning with Python

Artificial intelligence and machine learning are leading the technical revolution. Those who will master the art of training and developing intelligent machines stand a chance to be ahead of competition tomorrow.

Samatrix offers a certification course in Machine Learning with Python. The course focuses on the fundamentals of machine learning. In this course, we would focus on linear algebra, statistics, python and fundamental of machine learning.

Linear algebra is also called mathematics of data. It is one of the foundation pillars of machine learning. If you want to develop a deep understanding of machine learning especially deep learning algorithms, you should have a good understanding of linear algebra. Sound knowledge in linear algebra will give your better intuition about various machine learning algorithms so that you can design and develop effective learning models.

Statistics is another foundational pillar of machine learning and deep learning. Statistics is a required prerequisite for machine learning. Statistics and machine learning are two tightly related fields. It helps to extract information from observations. In this course, you will learn about descriptive statistics for summarizing data and inferential statistics for extracting conclusions and decisions from the data sample.

Python is one of the most popular programming languages. Today, Python is the top choice among machine learning engineers and data scientists for artificial intelligence, machine learning, and deep learning projects. Python has abundant libraries and framework such as NumPy, Pandas, SciPy, and Scikit-learn that facilitates coding for machine learning and deep learning. Python is concise and known for its simplicity and ease of use. It helps programmers focus on finding solutions instead of focusing on intricacies of coding.

Artificial Intelligence with focus on

and fundamentals of machine learning

Why Samatrix?

Experienced and qualified faculty

Experienced Faculty

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

Focus on concepts with practicals

Emphasis on concepts of Data Analysis with Python, statistics, linear algebra, machine learning and neural networks

Instructor led classroom sessions

80+ hour of instructor-led classroom sessions. Additional 120+ hours for projects and assignments

Virtual classroom sessions

Virtual Session From Home

Option of attending the session from home using video conference. Join live classroom and participate in discussion

Know about the upcoming batches

We have designed the program for beginners, who do not have any prior knowledge in Machine Learning.  The concepts of linear algebra, statistics and python programming are explained in an easy way to help you learn the Machine Learning with Python

Payment Plan

Schedule Fee GST @ 18% Total Payment
At Registration
Rs 5932
Rs 1068
Rs 7000
Beginning of 1st Class
Rs 10593
Rs 1907
Rs 12500
Beginning of 4th Class
Rs 8475
Rs 1525
Rs 10000
Rs 29500

Bank Details

Account Name: Samatrix Consulting Pvt Ltd
Account Number: 50200036088820
IFS Code: HDFC0001098
Branch: Badshahpur, Gurgaon, Haryana


The registration is valid only after receipt of Registration Fee

Key Program Features

What you will learn?

Key concepts of Python programming, Python libraries, statistical analysis techniques and machine learning models such as regression, classification, support vector machines, decision trees, and neural networks. Moreover, you will get an opportunity to work on 4 projects that will help not only in consolidating the concepts learned but also the experience on how to solve real-life machine learning problems.

Course Curriculum

  • What is Intelligence?
  • What is Artificial Intelligence
    • Cognitive Modeling
    • Turing Test
    • Law of Thoughts
    • Rational Agent
  • Past, Present, and Future of AI
    • Alan Turing
    • Conference of 1956
    • Successes and Setbacks
    • Current Wave
    • AI Research Trends
  • Area of AI
    • Machine Learning
    • Deep Learning
    • Difference between Machine Learning and Deep Learning
    • Computer vision
    • Natural Language Processing
    • Internet Of Things
  • AI Use Cases – Transportation
    • Smarter Cars
    • Self Driving Cars
    • On-Demand Transportation
  • AI in Healthcare
    • Keeping Well
    • Early Detection
    • Diagnosis
    • Decision Making
    • Treatment
  • AI in Finance
    • Fraud Prevention
    • Risk Management
    • Trading Executions and Portfolio Management
    • Customer Service
  • Introduction to Python
  • Jupyter
  • Numpy
  • Matplotlib
  • Seaborn
  • Panda
  • Scikit Learn
  • Systems of Linear Equations
  • Row Reduction and Echelon Forms
  • Existence and Uniqueness
  • Vectors and Matrix Equations
  • Vector Matrix Products
  • Linear Independence
  • Matrix Multiplication
  • Transpose of Matrix
  • Inverse of Matrix
  • Vector Space, Null Space, Row Space
  • Eigenvalues and Eigenvectors
  • Orthogonality
  • Hands-on practice on linear algebra problems using Python
  • Graphically Displaying Single Variable
  • Measures of Location
    • Mean
    • Median
  • Measures of Spread
    • Range
    • Variance
    • Standard Deviation
  • Displaying relationship – Bivariate Data
    • Scatterplot
    • Scatterplot Matrix
  • Measures of association of two or more variables
    • Covariance and Correlation
  • Probability
  • Joint Probability and independent events
  • Conditional probability
  • Bayes’ Theorem
  • Prior, Likelihood and Posterior
  • Discrete Random Variable
  • Probability Distribution of Discrete Random Variable
  • Binomial Distribution
  • Continuous Random Variables
  • Probability Distribution Function
  • Uniform Distribution
  • Normal Distribution
  • Beta Distribution
  • Point Estimation
  • Interval Estimation
  • Expectation Theory
  • Hypothesis Testing
    • Testing a one-sided Hypothesis
    • Testing a two-sided Hypothesis
  • Hands-on practice problems using Python
  • What is Machine Learning
  • Machine Learning vs Computer Program
  • Define Machine Learning
  • Application of Machine Learning
  • Relation between variables
  • Supervised Learning
  • Unsupervised Learning
  • Semi-Supervised Learning
  • Reinforcement Learning
  • Prediction
    • Dependent Variable vs Independent Variables
    • Reducible Error and Irreducible Error
    • Expected Value and Variance
  • Inference
    • Which Predictors are associated with Response?
    • Relationship between response and predictors
  • Learning Methods
    • Parametric Methods
    • Non Parametric Methods
  • Model Flexibility vs Interpretability
  • Model Accuracy and Selection
    • Quality of Fit
    • Bias – Variance Trade Off
    • Bayes Classifier
    • K-Nearest Neighbors
  • Basic Concepts
  • Construction of Regression Model
    • Selection of Predictor Variables
    • Functional Form of Regression Relations
    • Scope of Model
  • Uses of Regression Analysis
    • Description
    • Control
    • Prediction
    • Regression and Causality
  • Formal Statement of Model
  • Important Features of Model
  • Meaning of Regression Parameters
  • Steps in Regression Analysis
  • Estimation of Regression Function
    • Least Square Estimator
    • Estimating the Coefficients
    • Gradient Descent
    • Estimation of Variance Terms
  • Accuracy of Coefficients
  • Accuracy of Model
    • Residual Standard Error
    • R Square Statistics
  • Basic Concept with Example
  • Why not Linear Regression
  • Logistic Regression
    • Logistic Model
    • Estimating Regression Coefficients
    • Multiple Logistic Regressions
  • Linear Discriminant Analysis
  • Nearest Neighbour Methods
  • Cross Validation
  • Bootstrap
  • Choosing Optimal Model
    • F Test
    • Likelihood Ratio Test (LRT)
    • Akaike Information Criterion (AIC)
    • Bayes Information Criterion (BIC)
    • Adjusted R2
  • Subset Selection
    • Best Subset Selection
    • Forward Stepwise Selection
    • Backward Stepwise Selection
  • Decision Tree Model
  • Support Vector Machine
  • Unsupervised Learning
  • Introduction to Neural Network
  • Perceptrons
    • NAND Gate
  • Sigmoid Neuron
  • Gradient Descent
  • Multilayer Neural Network
    • Architecture of Multilayer Network
  • Backward Propagation Algorithm
  • Cross Entropy Cost Function
  • Overfitting and Regularization
  • Weight Initialization

Key Learning Areas

The learning areas include hands on expertise in Python programming and its libraries, statistical analysis of data, and concepts of linear algebra. The program focuses on building expertise in  data analysis, machine learning and neural network concepts. Even if you do not have any expertise in Python programming and data analysis, you will learn faster from our classes.

Learn concepts of Python programming language
Concepts of probability, probability distributions
Machine Learning
Learn regression, classification, SVM, decision trees

Admission Process


Selection Process

1. Fill Application Form: Apply online using the application form
2. Application Review: Admission committee review the applications submitted
3. Personal Interview: The Admission committee can invite the applicant for a personal interview.
4. Admission Offer: The selected candidates would be communicated about their success in admission process

Placement Assistance

Samatrix has a dedicated placement assistance team. Under our placement assistance program, we help the learners, who enroll for the program, introduce to our 100+ hiring partners. 

On successful completion of the training requirements, the learner becomes eligible for the training assistance program. Our training assistance team work closely with the learners to understand their career goals and provide mentorship.

We prepare the learners for application process and interviews by conducting resume review workshops, mock HR and technical interviews with industry experts.

Join the training program

Learn the latest technology and be part of a revolution. 

Training Program Fee Rs 25000 + GST

Registration Fee Rs 7000

Frequently Asked Questions

Samatrix is a technology consulting company based at Gurgaon, and Bangalore. Samatrix is led by IIT, IIM, Intel, HP alumni with deep industry expertise. It focuses on solving real business problems and developing the ecosystem through skill development in cutting-edge technologies. We cater to finance, insurance, travel, logistics, media, entertainment, and e-commerce domains. is committed to providing innovative solutions and workforce development in cutting-edge technologies to industries, corporates, higher educational institutions, and universities.  

Samatrix serves technical, business schools, universities, and corporates by providing quality education to students and faculty in artificial intelligence, machine learning, data science, data visualization, blockchain, augmented analytics, Internet of Things (IoT), cloud computing, and virtual reality.

The key concepts covered in Artificial Intelligence Machine Learning program need a good understanding of statistical and mathematical concepts. The applicants with a good understanding of basic mathematical concepts taught at 12 standard can understand the concepts in the class.

Yes, AI has use-cases in every industry and every domain. The field has been adopted by all the industries very aggressively. If you know the concepts & methods then you will be able to define the problem & design and develop solutions to solve the business problems in every domain.

Samatrix is not just a training institute. We are a technology consulting company. We focus on providing technical consultancy to corporates, provide technical solutions, and develop the workforce in the emerging technologies.

Our training programs have been very successful. Our programs have already been integrated with the Universities. We have a team of experts. We have been working with academia on several research projects and patents.

The unique combination of industry and academia exposure and expertise makes us different from others