Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Language
English
Taught by
Akhil Vydyula
Rating
Course Overview
Learn Python, Excel,Deep Learning, Power BI, SQL, Artificial Intelligence,Business Statistics, Capstone Projects
Welcome to the Full Stack Data Science & Machine Learning BootCamp Course, the only course you need to learn Foundation skills and get into data science.
At over 40+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery. Here's why:
The course is taught by the lead instructor at the PwC, India's leading in-person programming bootcamp.
In the course, you'll be learning the latest tools and technologies that are used by data scientists at Google, Amazon, or Netflix.
This course doesn't cut any corners, there are beautiful animated explanation videos and real-world projects to build.
The curriculum was developed over a period of three years together with industry professionals, researchers and student testing and feedback.
To date, I’ve taught over 10000+ students how to code and many have gone on to change their lives by getting jobs in the industry or starting their own tech startup.
You'll save yourself over $12,000 by enrolling, but get access to the same teaching materials and learn from the same instructor and curriculum as our in-person programming bootcamp.
We'll take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional.
The course includes over 40+ hours of HD video tutorials and builds your programming knowledge while solving real-world problems.
In the curriculum, we cover a large number of important data science and machine learning topics, such as:
MACHINE LEARNING -
Regression: Simple Linear Regression, , SVR, Decision Tree , Random Forest,
Clustering: K-Means, Hierarchical Clustering Algorithms
Classification: Logistic Regression, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
Natural Language Processing: Bag-of-words model and algorithms for NLP
DEEP LEARNING -
Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long short term Memory, Vgg16 , Transfer learning, Web Based Flask Application.
Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.
By the end of this course, you will be fluently programming in Python and be ready to tackle any data science project. We’ll be covering all of these Python programming concepts:
PYTHON -
Data Types and Variables
String Manipulation
Functions
Objects
Lists, Tuples and Dictionaries
Loops and Iterators
Conditionals and Control Flow
Generator Functions
Context Managers and Name Scoping
Error Handling
Power BI -
What is Power BI and why you should be using it.
To import CSV and Excel files into Power BI Desktop.
How to use Merge Queries to fetch data from other queries.
How to create relationships between the different tables of the data model.
All about DAX including using the COUTROWS, CALCULATE, and SAMEPERIODLASTYEAR functions.
All about using the card visual to create summary information.
How to use other visuals such as clustered column charts, maps, and trend graphs.
How to use Slicers to filter your reports.
How to use themes to format your reports quickly and consistently.
How to edit the interactions between your visualizations and filter at visualization, page, and report level.
By working through real-world projects you get to understand the entire workflow of a data scientist which is incredibly valuable to a potential employer.
Sign up today, and look forward to:
178+ HD Video Lectures
30+ Code Challenges and Exercises
Fully Fledged Data Science and Machine Learning Projects
Programming Resources and Cheatsheets
Our best selling 12 Rules to Learn to Code eBook
$12,000+ data science & machine learning bootcamp course materials and curriculum
Course Content
- 10 section(s)
- 69 lecture(s)
- Section 1 Introduction to the Full Stack Data Science Course
- Section 2 Python Fundamentals: Introduction to Basics for Beginners
- Section 3 Data Analysis with Business Statistics: Techniques and Applications
- Section 4 Machine Learning Fundamentals: Concepts, Algorithms, and Applications
- Section 5 Flight Fare Prediction: Machine Learning Capstone Project
- Section 6 Mushroom Classification: Machine Learning Capstone Project
- Section 7 Nursery School Application Classification: Machine Learning Capstone Project
- Section 8 ML Capstone Project 4 : Toxic_Comments_Classification
- Section 9 ML Capstone Project 5 : UK_Road_Accident_Timeseries_Forecasting
- Section 10 Structured Query Language (SQL)
What You’ll Learn
- Build a real-world portfolio: Create multiple data science projects to demonstrate your skills to potential employers.
- Master data visualization: Design clear, insightful charts (bar, line, scatter, histogram, etc.) and use visualization techniques to explore and present large d
- Develop deep learning skills: Construct, train, and deploy neural networks for tasks like image recognition and data classification.
- Apply core algorithms: Use common data science and machine learning algorithms on real projects (for example, classifying mushrooms or analyzing images).
- Use modern tools: Learn to work with essential data science tools and libraries (TensorFlow, NumPy, Matplotlib, Pandas, etc.) to process data and build models.
- Understand how to use the latest tools in data science, including Tensorflow, Matplotlib, Numpy and many more
- Computer with internet: Just have a Windows or Mac computer and internet access
- that’s all you need to get started.
Skills covered in this course
Reviews
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AArum Chika Geraldine
Each section is well organized and illustrated but would like to practice them for easy implementation.
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SSrivathsava
This course turned out to be far better than I expected. It provides a complete, end-to-end learning path for data science, starting from the absolute basics and gradually building up to advanced machine learning and deep learning concepts. The instructor explains topics very clearly, with practical examples and real-world projects that make the learning process engaging and hands-on. I especially liked the structure of the course—Python fundamentals, machine learning algorithms, deep learning models, SQL, and Power BI are all covered in depth. The capstone projects are very helpful for building a real portfolio, and the demonstration of deploying a model using Flask is a great addition for anyone aiming for real-world ML engineering skills. The SQL and Power BI sections were also surprisingly comprehensive. They provided practical exercises and clear explanations that helped me understand how data science works beyond just Python and ML. Overall, the course is a great match for beginners as well as intermediate learners. The instructor’s industry experience shows through the explanations, and the entire curriculum feels well thought-out and up-to-date. Definitely worth the time—highly recommended!
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CChukwuwbuka Shedrack Eze
Its fine
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JJasvanth S
It was very good easy to understand by beginners like me, it's must be preferable
