
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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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
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 PCAHTML & 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 developmentDjango with Machine Learning
✓ Integrate Django with your machine learning projectsLive Project Experience
✓ Apply your skills to a real-world project✓ Solidify your knowledge through practical application
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
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
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
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 PCAWeek 12:
HTML & CSS Basics
✓ Master HTML syntax and file structure✓ Unveil the world of CSS: syntax, properties, colors, layouts, and more
Week 13:
Introduction to Django Web Framework
✓ Get started with Django for web developmentWeek 14:
Django with Machine Learning
✓ Integrate Django with your machine learning projectsWeek 15 -17
Live Project Experience
✓ Apply your skills to a real-world project✓ Solidify your knowledge through practical application
Advance 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: StreamlitStreamlit'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.
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.
Week 1 to 17
Same as basic data science and machine learning course
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: StreamlitStreamlit'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.
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
Professional Graduates
Working Professionals
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.