Data Science and Machine Learning


✓ 2.5 month Long Course

✓ 2 Hours per day

✓ Daily Assignment / Projects / Doubt solving

✓ 24/7 Private Community Support / Revision session

✓ Both Physical / Online available

✓ Verified Certificate

✓ Everyday recording available

✓ Internship guaranteed


Now to bridge the gap between industry and IT students Vrit Technologies is launching, Data Science and Machine Learning with Python.

Complete Data Science and Machine Learning Diploma Course with Python covers complete Introduction to Programming Languages, Python, Basics of Data Science, Machine Learning, Deep Learning, Natural Language Processing, Web Interfaces, and More.

Data Science is an interdisciplinary field that involves extracting insights and knowledge from data using various techniques and tools. It encompasses various stages of data processing, such as data collection, data cleaning, data analysis, and data visualization. The aim of data science is to provide valuable insights and knowledge to solve complex problems using data-driven approaches.

Machine learning is a subset of data science that involves the use of statistical algorithms and models to enable computers to learn from data without being explicitly programmed. It focuses on developing algorithms that can learn patterns and make predictions or decisions based on data.

Python is a popular programming language for data science and machine learning due to its ease of use, rich library support, and scalability. It offers a wide range of libraries and frameworks, such as Pandas, NumPy, Scikit-learn, Tensorflow, and Keras, that provide powerful tools for data manipulation, statistical analysis, and machine learning modeling. Python is also open-source, which means that it is free to use and has a large community of developers that contribute to its development and support.


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Programming Fundamentals {Python Basic}

– Core Data Structure of Python (Variable, List, Tuples, Dictionary, Set, Loop, Functions, Maps, Function Recursion)

– Python Core (List & Dictionary Comprehension, Exceptions & Exceptions Handling, File Handling, Object Oriented Programming & OOP concepts)

Python Core

– List and Dictionary Comprehension

– Exceptions and Exception Handling

– File Handling

– Object Oriented Programming (OOP)

– Introduction to Classes

– Inheritance, Encapsulation, Polymorphism, Abstraction

– Method Overloading

– Building Custom Packages and Modules

Basics to Data Science

– Introduction to Data Science, NumPy, Matplotlib,

– Matrix Operations with NumPy

– Probability, properties of Probability Distributions

– Mean, Median, Mode, Variance, Skewness, Kurtosis, Multivariate Normal Distribution, Co-Variance, Correlation

– Introduction to Scikit-Learn

– Dimensionality Reduction as Data Pre-Processing

– Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA)

Machine Learning – I

– Introduction to Reinforcement Learning

– Q-Learning with Python

– Introduction to Clustering

– K-Means Clustering

– Agglomerative Clustering

– Introduction to Supervised Learning

– Naive Bayes Classification

Machine Learning – II

– Linear and Polynomial Regression, K-Nearest Neighbors, Decision Tree

– Balancing Bias vs Variance of ML Model, Ensemble Learning, Random Forest and Adaptive Boost

– Identifying Important Features of Data

– Time Series Analysis

Deep Learning – I

– Introduction to Logistic Regression, Computation Graph and Gradient Descent

– Introduction to Artificial Neuron (Perceptron), Multi-Layer Perceptron

– Introduction to Artificial Neural Networks

– Designing Artificial Neural Networks with Keras

– Gradient Decent Variants, Classification and Regressionusing Neural Networks

Deep Learning – II

– Introduction to Convolutional Neural Network (CNN)

– Object Classification with CNN, Standard CNN Architectures

– Introduction to Object Detection, Transfer Learning

– The YOLO Algorithm, Deep Reinforcement Learning

Natural Language Processing + Web Interface

– Introduction to NLTK, Text Pre-Processing

– POS Tagging and Named-Entity Recognition, Latent Semantic Analysis

– Introduction to Recurrent Neural Network, Word2Vec Algorithm for Text Vectorization

– Natural Language Processing with LST

– Giving Web Interface to ML Application using Flask/Django/Streamlit.

Natural Language Processing and Web Interface

– Introduction to NLTK

– Text Pre-Processing

– POS Tagging and Named-Entity Recognition

– Latent Semantic Analysis

– Introduction to Recurrent Neural Network

– Word2Vec Algorithm for Text Vectorization

– Natural Language Processing with LST

– Giving Web Interface to ML Application using Flask/Django / Streamlit.

Assignment / Labs

– Each student will have a project to complete in order to demonstrate their understanding both during and after the course.

– Lab assignments will focus on the practice and mastery of contents covered in the lectures; and introduce critical and fundamental problem-solving techniques to the students.