Build Your Own AI:

Learn Data Science and

Machine Learning in One Course

Are you looking to learn the skills of the future? Data Science and Machine Learning are most in-demand skills in the world, and this course will teach you everything you need to know to get started

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Our Recently Placed Students

Data Science and Machine Learning are two of the most in-demand skills in the tech industry today. This course will teach you the fundamentals of data science and machine learning, from data wrangling and cleaning to machine learning algorithms and deep learning. Python is a powerful programming language that is well-suited for data science tasks, such as data manipulation, analysis, and visualization.

This course is a comprehensive introduction to data science and machine learning. It covers the basics of Python programming, exploratory data analysis, machine learning algorithms, and real-world projects. By the end of this course, you will be able to use Python to solve a variety of data science problems. You will also be well-prepared for a career in data science.

If you are interested in learning more about data science and machine learning, then enrol in our Data Science and Machine Learning Course today!

Data Science

Stacks Covered

Document

Data Science and Machine Learning Course

Basic
Data Science and Machine Learning Course

Duration: 3 - 3.5 Months
Mode: Online and Offline
Live Project & Internship: 15 days to 1 month
Hours: 2-3 hours per day, 5 days a week

Python Basics and Data Structures

Dive into Python fundamentals
Setup your Python environment.
Learn Anaconda, Jupyter Notebook, and Google Colab
Explore Python data structures: Numbers, Strings, Lists, Tuples, Sets, Dictionaries

Advanced Python Programming

Work with files, modules, and packages
Master control flow and decision-making
Discover loop structures and control statements

Advanced Python Programming (Part 1)

Get a grip on functions and their power
Explore object-oriented programming (OOP)
Understand classes and objects

Advanced Python Programming (Part 2)

Go deeper into OOP concepts
Handle exceptions effectively


Advanced Python Programming (Part 3)

Dive into exception handling and user-defined exceptions
Learn to work with JSON data

Exploratory Data Analysis (EDA) with Python

Master Pandas, Numpy, Matplotlib, and Seaborn
Gain hands-on experience with detailed data analysis

Databases, Web Scraping, and Statistics

Unlock the power of SQL
Harness web scraping with BeautifulSoup
Explore essential statistics for data science and machine learning integration.


Introduction to Machine Learning

Enhance your grasp of statistics while also exploring its role.
Embark on your machine learning journey

Machine Learning Algorithms (Part 1)

Explore supervised learning algorithms
Study Regression and Classification

Machine Learning Algorithms (Part 2)

Delve into unsupervised learning and reinforcement learning
Discover clustering and association algorithms

Real-world Projects with Kaggle

Experience SVM, Naïve Bayes, KNN, and more on Kaggle. Hands-on projects featuring K-Means and PCA

HTML & CSS Basics

Master HTML syntax and file structure
Unveil the world of CSS: syntax, properties, colors, layouts, and more

Introduction to Django Web Framework

Get started with Django for web development

Django with Machine Learning

Integrate Django with your machine learning projects

Live Project Experience

Apply your skills to a real-world project
Solidify your knowledge through practical application
Course Details

Duration: 3 - 3.5 Months
Mode: Online and Offline
Live Project & Internship: 15 days to 1 month
Hours: 2-3 hours per day, 5 days a week

First Month

Week 1:

Python Basics and Data Structures
Dive into Python fundamentals
Setup your Python environment.
Learn Anaconda, Jupyter Notebook, and Google Colab
Explore Python data structures: Numbers, Strings, Lists, Tuples, Sets, Dictionaries

Week 2:

Advanced Python Programming
Work with files, modules, and packages
Master control flow and decision-making
Discover loop structures and control statements

Week 3:

Advanced Python Programming (Part 1)
Get a grip on functions and their power
Explore object-oriented programming (OOP)
Understand classes and objects

Week 4:

Advanced Python Programming (Part 2)
Go deeper into OOP concepts
Handle exceptions effectively


Second Month

Week 5:

Advanced Python Programming (Part 3)
Dive into exception handling and user-defined exceptions
Learn to work with JSON data


Week 6:

Exploratory Data Analysis (EDA) with Python
Master Pandas, Numpy, Matplotlib, and Seaborn
Gain hands-on experience with detailed data analysis
matrix.

Week 7:

Databases, Web Scraping, and Statistics
Unlock the power of SQL
Harness web scraping with BeautifulSoup
Explore essential statistics for data science and machine learning integration.

Week 8:

Introduction to Machine Learning
Enhance your grasp of statistics while also exploring its role.
Embark on your machine learning journey

Third Month

Week 9:

Machine Learning Algorithms (Part 1)
Explore supervised learning algorithms
Study Regression and Classification


Week 10:

Machine Learning Algorithms (Part 2)
Delve into unsupervised learning and reinforcement learning
Discover clustering and association algorithms

Week 11:

Real-world Projects with Kaggle
Experience SVM, Naïve Bayes, KNN, and more on Kaggle. Hands-on projects featuring K-Means and PCA

Week 12:

HTML & CSS Basics
Master HTML syntax and file structure
Unveil the world of CSS: syntax, properties, colors, layouts, and more


Fourth Month

Week 13:

Introduction to Django Web Framework
Get started with Django for web development

Week 14:

Django with Machine Learning
Integrate Django with your machine learning projects

Week 15 -17

Live Project Experience
Apply your skills to a real-world project
Solidify your knowledge through practical application

CORS, aka Cross-Origin Resource Sharing, is a mechanism that enables many resources (e.g. images, stylesheets, scripts, fonts) on a web page to be requested from another domain outside the domain from which the resource originated.



Advance AI Engineering

Advanced
AI Engineering

Duration: 6 to 6.5 Months
Mode: Online and Offline
Live Project & Internship: 1 month
Hours: 3-4 hours per day, 5 days a week.




Same as basic data science and machine learning course

NLP Foundations

Part 1: Introduction to NLP & Real-life Applications
Part 2: Language Basics & Sentiment Analysis
Part 3: Text Processing & Classification

Advanced NLP

Part 1: Transformer Insights Discover Transformers, Huggingface, and RNNs in NLP.
Part 2: Mastering NLP with Transformers Implement BERT, GPT-2. Excel in NLP tasks.

Interactive NLP Apps

Part 1:Build NLP Web Apps: Streamlit
Streamlit's intro & setup
NLP integration & visuals
Easy deployment via Streamlit sharing
Part 2: Create APIs: Django Rest Framework
DRF overview
NLP APIs, Serializers, ViewSets
Streamlit-DRF seamless integration

API Integration

Deploy NLP models with Vertex AI.
Utilize Huggingface, OpenAI, and Vertex APIs.
Integrate the Transformer model on Vertex AI.


Advanced Techniques

Unleash Language Models' power.
Master neural machine translation.
Create an emotion-aware chatbot with Language Models.


Live NLP Project

Define, develop, and deploy an NLP project.
Frontend-backend integration.
Present your live NLP project.


Course Details

Duration: 6 to 6.5 Months
Mode: Online and Offline
Live Project & Internship: 1 month
Hours: 3-4 hours per day, 5 days a week.

Month 1 to 4

Week 1 to 17

Same as basic data science and machine learning course

Month 5

Week 18

NLP Foundations
Part 1: Introduction to NLP & Real-life Applications
Part 2: Language Basics & Sentiment Analysis
Part 3: Text Processing & Classification

Week 19

Advanced NLP
Part 1: Transformer Insights Discover Transformers, Huggingface, and RNNs in NLP.
Part 2: Mastering NLP with Transformers Implement BERT, GPT-2. Excel in NLP tasks.

Week 20

Interactive NLP Apps
Part 1:Build NLP Web Apps: Streamlit
Streamlit's intro & setup
NLP integration & visuals
Easy deployment via Streamlit sharing
Part 2: Create APIs: Django Rest Framework
DRF overview
NLP APIs, Serializers, ViewSets
Streamlit-DRF seamless integration

Week 21

API Integration
Deploy NLP models with Vertex AI.
Utilize Huggingface, OpenAI, and Vertex APIs.
Integrate the Transformer model on Vertex AI.

Month 6

Week 22

Advanced Techniques
Unleash Language Models' power.
Master neural machine translation.
Create an emotion-aware chatbot with Language Models.


Week 23 - 25

Live NLP Project
Define, develop, and deploy an NLP project.
Frontend-backend integration.
Present your live NLP project.


Data Science

What is Data Science?

Machine Learning

What is Machine Learning?

Data Science

Why GALTech?

Classes from Industry Experts

Project-based

Job-focused

Placement Assistance

Internship Programs

HD Video Recordings

Weekly Projects & Assignments

Industry-specific Curriculum

Peer-to-peer

Self-paced

Certification

Affordable Fees

Data Science

Who Can Join?

Graduates

Students who has completed their science degree. BSC, MSC etc.

Professional Graduates

Students who has completed their BTech, BE, MTech, MCA, Diploma in CS/IT etc.

Working Professionals

Software Engineers or Developers working in IT field planning to switch their career or add new skillsets.

Common Queries

Frequently Asked Questions

The course is designed to provide a comprehensive understanding of data science and machine learning concepts. It covers topics such as data analysis, machine learning algorithms, data collection, web scraping, and advanced techniques like natural language processing and deep learning.

You’ll learn essential skills such as data analysis with SQL, web scraping using Beautiful Soup, building machine learning models, and working with natural language processing techniques. The course also covers advanced topics like deploying models and integrating APIs.

Some basic programming experience will be helpful, but the course is designed to accommodate learners with various levels of experience. It starts with fundamentals and progresses to more advanced concepts.

Throughout the course, you’ll work on a variety of projects. Examples include building a sentiment analysis model, predicting restaurant ratings based on reviews, and developing a next-generation chatbot using advanced NLP techniques.

The recommended time commitment can vary, but generally, you should expect to spend several hours per week on lectures, working on assignments, and completing projects effectively.

Absolutely. The course is designed to equip you with practical skills that are highly relevant in today’s data-driven world. You’ll be able to apply what you’ve learned to real-world data analysis and machine-learning projects.

Completing this course can open doors to various roles in data science, machine learning engineering, data analysis, and AI development. These fields are in high demand across industries.

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