Machine learning and data science fields are gaining popularity and importance nowadays. The data science field uses techniques, algorithms, processes, and systems to extract information and insights from data. Machine learning is a subdivision of data science that deals with creating algorithms and models. These algorithms can learn patterns from data and make predictions. Data scientists use many tools and techniques to uncover meaningful insights from the data. They collect and clean data, then interpret it to make informed decisions. Tasks like classification, regression, clustering, and recommendation are done by making use of machine learning algorithms. A wide range of industries like healthcare, finance, retail, manufacturing, etc. use machine learning and data science for enhanced decision-making.
In the 21st century, data science ranks among the most sought-after skills; Kerala cannot escape this trend. Amidst expanding companies seeking talented data experts, professionals with keen abilities in data science have a significant appeal within the workforce of this location. Offered through the GALTech School of Technology, the Data Science and Machine Learning Course aims to equip aspiring data scientists with essential knowledge and abilities. Insightful instruction from seasoned practitioners lies at the core of this curriculum. By participating in actual projects, you acquire professional skills vital for employment.
Data Science
Python for Data Science
Offering versatility and widespread application, Python proves an adaptable language in the domain of data science. By offering a myriad of libraries and tools, it becomes a premier choice for handling everything from data manipulation to visualization. Libraries including pandas, NumPy, and Matplotlib enable seamless data management and visualization, while scikit-learn, TensorFlow, and PyTorch allow for the creation and training of sophisticated machine learning models. Merging these aspects creates a suitable environment for newcomers and seasoned data experts, alike, to extract meaningful insights and shape informed decisions via Python.
If you are looking for a comprehensive and practical data science course in Kerala, then GALTech School of Technology is the perfect choice for you. The Data Science and Machine Learning Course from GALTech School of Technology is the perfect way to launch your career in data science. With the skills you will learn in this course, you will be well-positioned to land a job in this growing field.
Basic
DATA SCIENCE & MACHINE LEARNING COURSE
Course Details
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
Week 01
Week 01
Python Basics and Data Structures
Week 02
Week 02
Advanced Python Programming
Week 03
Week 03
Advanced Python Programming (Part 1)
Week 04 - 05
Week 04
Advanced Python Programming (Part 2)
Week 05
Advanced Python Programming (Part 3)
Week 06 - 07
Week 06
Exploratory Data Analysis (EDA) with Python
Week 07
Databases, Web Scraping, and Statistics
Week 08 - 09
Week 08
Introduction to Machine Learning
Week 09
Machine Learning Algorithms (Part 1)
Week 10 - 11
Week 10
Machine Learning Algorithms (Part 2)
Week 11
Real-world Projects with Kaggle
Week 12
Week 12
HTML & CSS Basics
Week 13 - 14
Week 13
Introduction to Django Web Framework
Week 14
Django with Machine Learning
Week 14 - 17
Week 14 - 17
Live Project Experience
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
ADVANCED AI ENGINEERING
Course Details
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.
Week 01 - 17
Week 01 - 17
Same as basic data science and machine learning course
Week 18
Week 18
NLP Foundations
Week 19
Week 19
Advanced NLP
Week 20
Week 20
Interactive NLP Apps
Streamlit's intro & setup
NLP integration & visuals
Easy deployment via Streamlit sharing
DRF overview
NLP APIs, Serializers, ViewSets
Streamlit-DRF seamless integration
Week 21
Week 21
API Integration
Week 22
Week 22
Advanced Techniques
Week 23 - 25
Week 23 - 25
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 and Machine Learning Course
Course DetailsDuration: 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
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?
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.
