Master Data Science, AI ML, DL & NLP Beginner to Advanced
Published 10/2026
Created by Voryaan Choudhary
MP4 | Video: h264, 2560×1440 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 326 Lectures ( 52h 16m ) | Size: 30.8 GB
Master in Data Sc, AI/ML, DL & NLP | Beginner to Advanced | Theory, Mathematics, Hands-on Practice & Real-World Projects
What you’ll learn
– Python Programming from Beginner to Advanced
– Statistics, Probability, and Linear Algebra for Machine Learning
– Data Cleaning, EDA, and Feature Engineering
– Supervised and Unsupervised Machine Learning
– Ensemble Learning Techniques
– Hyperparameter Tuning and Cross Validation
– Natural Language Processing (NLP)
– Deep Learning using TensorFlow and Keras
– ANN, CNN, RNN, LSTM, and GRU
– Multiple End-to-End Machine Learning Projects
– Model Deployment using Flask and Streamlit
– Industry Best Practices and Interview Preparation
Requirements
-Computer: A PC or Mac (Windows, macOS, or Linux) with at least 4GB of RAM (8GB+ recommended for Deep Learning and project work).
-Internet Connection: To download datasets, libraries, and watch course videos.
-Software: No prior software is needed on day one! You will guide students through installing Anaconda and VS Code in Module 1.
Description
Are you looking for a complete course that teachesData Science, Machine Learning, Deep Learning, and Natural Language Processing from the ground up?
This course is designed to provide both the theoretical foundation and practical skills needed to build real-world AI applications.
Starting with Python programming, you’ll gradually progress through Statistics, Probability, Data Science, Feature Engineering, Machine Learning, Deep Learning, and NLP while building multiple end-to-end projects.
You’ll learn the mathematics behind every major algorithm, understand how models make decisions, and implement everything using Python and industry-standard libraries such as NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and Keras.
This course includes
– Complete Python Programming
– Statistics and Probability
– Data Analysis and Visualization
– Feature Engineering and Exploratory Data Analysis
– Supervised Machine Learning
– Unsupervised Machine Learning
– Ensemble Learning Algorithms
– Model Evaluation and Hyperparameter Tuning
– Deep Learning (ANN, CNN, RNN, LSTM, GRU)
– Natural Language Processing
– Text Preprocessing and Word Embeddings
– Real-world End-to-End Machine Learning Projects
– Flask and Streamlit Deployment
– Best Practices for Building Production-Ready Machine Learning Applications
Every section combines theory, mathematical intuition, coding implementation, practical exercises, and real-world case studies to ensure you gain both conceptual understanding and hands-on experience.
By the end of this course, you’ll be able to build, train, evaluate, optimize, and deploy intelligent applications with confidence.
Who this course is for
– Absolute beginners in Data Science and AI.
– College and university students.
– Software Engineers and Developers.
– Data Analysts and Business Analysts.
– Python programmers.
– Professionals looking to switch into AI or Machine Learning.
– Anyone interested in building intelligent applications using modern AI technologies.
– Learners who prefer understanding both the theory and practical implementation of Machine Learning and Deep Learning.
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