Integrated Program in Data Science Machine Learning and Artificial Intelligence Part Time Course at Hero Vired

Quick Facts

particular	details
Collaborators MIT Cambridge	Medium of instructions English	Mode of learning Self study, Virtual Classroom	Mode of Delivery Video and Text Based	Frequency of Classes Weekends	Learning efforts 14-16 Hours Per Week

Course overview

Integrated Program in Data Science, Machine Learning and Artificial Intelligence (IDMA – Part Time) by Hero Vired is a unique offering concerning dealing with all three domains equally. This immersive part-time course is delivered by industry experts to make learners proficient in Python, Tableau, R, PowerBI, Excel, Azure, TensorFlow, SQL, PyTorch and KNIME. Integrated Program in Data Science, Machine Learning and Artificial Intelligence (IDMA – Part Time) Certification Syllabus shall be covered by faculty from institutions like NITI Aayog and Shine.com and is integrated with Data Science MicroMasters from MIT.

Course and certificate fees

Fees information

₹ 425,000 (Inclusive of GST)

Integrated Program in Data Science, Machine Learning and Artificial Intelligence (IDMA – Part Time) Fee is given below-

The course fee is Rs. 4,25,000.
The fee is exclusive of applicable taxes.

Fee Details for Integrated Program in Data Science, Machine Learning and Artificial Intelligence (IDMA – Part-Time)

Fee	Amount in Rupees
Course Fee	Rs. 4,25,000

certificate availability

Yes

certificate providing authority

Hero Vired

Who it is for

Integrated Program in Data Science, Machine Learning and Artificial Intelligence (IDMA – Part Time) Online Course is suitable for the following people-

Candidates willing to start their career or polish their skills in Data Science, Artificial Intelligence or Machine Learning.
STEM graduates
Working professional

Eligibility criteria

Education

Candidates willing to enroll for the Integrated Program in Data Science, Machine Learning and Artificial Intelligence (IDMA – Part Time) Online Course must possess an undergraduate degree in science, engineering, technology or mathematics.

Certification qualifying details

Integrated Program in Data Science, Machine Learning and Artificial Intelligence (IDMA – Part Time) Certification shall be issued to learners after successfully completing the programme.

What you will learn

Integrated Program in Data Science, Machine Learning and Artificial Intelligence (IDMA – Part Time) Training will help learners pivot their career in the relevant domain by knowledge of -

Candidates will study the key principles and algorithms for turning training data into effective automated predictions
They shall receive a background on the goal of machine learning, the difference between the training set and the test set, elements of supervised learning and the difference between classification and regression
This course will allow them to get a grasp over data formatting and data description
They will be able to examine how businesses deploy analytics through use-cases, the qualities of a good analyst and the CRISP-OM architecture
Learners will be able to strengthen their basic probability concepts, including continuous random or multiple discrete variables
Based on a Python environment, they will study ML packages and the concept of Object-Oriented Programming
They shall get acquainted with business intelligence tools and the key concepts contributing to working on Python
This course shall also deal with the common methods and models, including specific types of data in epigenetic codes and data visualisation, prices, criminal networks and network analysis, environmental data and spatial statistics and economics and time series

The syllabus

Python Programming

Installation and set-up, Python Basics - Variables, built-in functions (print, type, help, range, input,...)
Loops and simple operations, Data Structures and Operations (Lists and Tuple)
Data Structures and Operations (Dictionary, Set), Conditional Statements, Functions and methods
Python Modules overview - Pandas, Numpy - DataFrames & Arrays. Functions, Numpy Array Operations, OOPs and Debugging concepts, Overview of ML Libraries

Data Analysis with Python

Pandas Basics - DataFrames & Arrays, reading files, row/column selection, sub-setting, EDA, variable creation, data summaries
Pandas Advanced - Visualization with matplotlib and Seaborn, Data profiling and analysis, variable correlation. Handling data anomalies, feature engineering

ML Mathematics

Mathematics Basics and Advanced
Linear Algebra: Vectors and Scalars, Matrices and Matrix operations, 2D/3D Plots, Functions, Limits and Derivatives, Notations, Numbers, Sequences, Points, Lines and Planes, Gaussian Distribution, Probability Density Functions

Predictive Modeling and ML

Industry Application, Core Concepts (Train & Test samples, model metrics)
Linear/ Non-linear Regression models and implementation in Scikit-learn
Classification models (Logistic, SVM) and implementation in Scikit-learn
Classification models (Decision Trees) and implementation in Scikit-learn
Ensemble Learning: Tree-based and others

Machine Learning with Python-From Linear Models to Deep Learning

Working with Linear Classifiers and Linearly Seperable Data
Estimatiing Model Parameters using Perceptron Algorithm and Gradient Descent
Working with Linear SVM
Linear Regression
Non Linear Models and Feature Maps
SVM with Kernels
Recommendation Engines using Memory Based and Model Based Methods (Matrix Factorization)
Introduction to Neural Networks: Activation Functions, Forward pass, Backward pass
Recurrent Neural Networks and Sequence Models
Convolutional Neural Networks, Using Pytorch for Neural Network Models Implementation
KMeans Clustering and EM Algorithm
Gaussian Mixture Models for Collaborative Filtering
Reinforcement Learning

Statistics Basics

Statistics Concepts - Descriptive Statistics (mean, median, variance, std. dev, percentiles)
Understanding Univariate and Multivariate Distributions through plots (Histograms, Bar Plots, Box Plots, Two-way Tables, Scatter Plots, Q-Q Plots)
Correlation, Inferential Statistics (Point & Interval Estimation and use of various statistics)
CLT and Law of large numbers

Foundation of Statistics

Parametric Statistical Models, Parametric Estimation and Confidence Interval
Delta Method and Confidence Intervals
Introduction to Hypothesis Testing, and Type 1 and Type 2 Errors
Total Variation Distance, Kullback-Leibler (KL) Divergence, and the Maximum Likelihood Principle, MLE
Covariance Matrices, Multivariate Statistics, and Fisher Information
Maximum Likelihood Estimation (Continued) and the Method of Moments, M Estimation
Hypothesis Testing: χ2 distribution and T-test, Hypothesis Testing: Wald’s test, Likelihood Ratio Test, and Implicit Hypothesis
Hypothesis Testing: χ2-test for Multinomial Distribution, Goodness of Fit Test; Hypothesis Testing: Kolmogorov-Smirnov Test, Kolmogorov Lilliefors Test, QQ-plot
Introduction to Bayesian Statistics; Jeffrey’s Prior and Bayesian Confidence
Linear Regression 1; Linear Regression 2
Introduction to Generalized Linear Model: Exponential Families; The Canonical Link Function

Deep Learning

Basic Text Processing and NLP: Using Regex, creating tfidf features, POS Tagging and dependency parsing
DL in Practice: Using tf/pytorch to build simple neural networks, understand automatic differentiation, carry out gradient computations
DL in NLP 1: LSTMs and GRUs, Encoder Decoder Architecture for Translation
DL in NLP 2: BERT based models
DL In Computer Vision1: Use transfer learning to build image classifiers. Build multiclass and multilabel classifiers
DL In Computer Vision2: Single Shot Object Detection, measuring Object Detector Performance, custom labelling and custom training

Admission details

Candidates registering for Integrated Program in Data Science, Machine Learning and Artificial Intelligence (IDMA – Part Time) Training need to follow the given steps-

Step 1: Go to the course link- https://herovired.com/idma-part-time-program/ and sign up/sign in via the Hero Vired portal.

Step 2: Verify your account through the verification mail sent to your mail.

Step 3: Choose the said programme and add it to your cart.

Step 4: Pay for the course and begin learning.

Scholarship Details

Scholarships will be offered to learners of the Integrated Program in Data Science, Machine Learning and Artificial Intelligence (IDMA – Part Time) Programme. Candidates will have to contact a counsellor for more details on the same.

How it helps

Integrated Program in Data Science, Machine Learning and Artificial Intelligence (IDMA – Part Time) Certification benefits learners by helping them be at the forefront of innovation and revamping their organisation by becoming the game changer and thought leader. This course is saturated with a strong academic and industry focus and assists learners with hands-on project-based learning. Apart from the certification, they will be eligible for MicroMaster credentials.

Instructors

Mr Subhashis Majumder
Instructor
Freelancer

M.E /M.Tech., Ph.D

FAQs

What are the payment alternatives available for Integrated Program in Data Science, Machine Learning and Artificial Intelligence (IDMA – Part Time) Fee?

As an alternative, candidates can either opt for zero interest EMI or self-payment in 2-3 instalments.

What can be done in case a candidate misses any class?

In such cases, the candidate can access the recording under the “Learning Outline” section and watch it at their convenience.

How can candidates address their doubts during the session?

Candidates can raise a ticket which will be directed to the concerned faculty. Integrated Program in Data Science, Machine Learning and Artificial Intelligence (IDMA – Part Time) benefits include an individual manager responsible for resolving all the doubts of candidates.