π Complete Data Science Journey: From Beginner to Pro
π₯ Data Science Courses @ βΉ399 / $9.99 β Available Worldwide π
Want to become job-ready in Data Science & Machine Learning?
This complete course bundle takes you from basics to production deployment β step by step, no confusion.
This is a step-by-step Data Science learning path β from basics to real production deployment.
π 1. Machine Learning & Data Science for Beginners in Python
Start from ZERO and build strong foundations.

You will learn:
- Python for Data Science
- Supervised & Unsupervised Learning
- Linear & Logistic Regression
- KNN, Decision Trees, Random Forest
- KMeans, PCA, XGBoost
- Model evaluation & real projects
π¨βπ Best for: Beginners, students, freshers
π― Goal: Strong ML foundation with hands-on practice
π 2. Python for Linear & Advanced Regression in Machine Learning
Master regression like a professional.

You will learn:
- Linear & Non-Linear Regression
- Lasso & Ridge Regression
- Feature selection & transformation
- Outlier detection & removal
- Model explainability using SHAP & LIME
- Data visualization & interpretation
π¨βπ Best for: Anyone serious about ML fundamentals
π― Goal: Build interpretable & high-quality regression models
π 3. Deep Learning for Beginners with Python
Step into Neural Networks and Deep Learning.

You will learn:
- Neural Networks from scratch
- ANN, CNN, RNN, LSTM
- TensorFlow 2.x
- Image & sequence modeling
- Transfer Learning
- Real-world deep learning use cases
π¨βπ Best for: ML learners moving to Deep Learning
π― Goal: Understand and build deep learning models confidently
π 4. Natural Language Processing (NLP) Mastery in Python
Learn how machines understand text.

You will learn:
- Text cleaning & preprocessing
- Regular Expressions
- NLTK & SpaCy
- Sentiment & emotion analysis
- Spam classification
- word2vec, GloVe, LSTM for NLP
- PDF text extraction & CV parsing
π¨βπ Best for: NLP & AI application builders
π― Goal: Build real-world NLP projects
π 5. Deploy ML Models in Production with FastAPI & Docker
Turn models into real products.

You will learn:
- Build REST APIs using FastAPI
- Deploy ML & NLP models
- Docker for ML applications
- AWS EC2 & S3 deployment
- HuggingFace Transformers (BERT, TinyBERT, ViT)
- Streamlit apps for ML demos
- Monitoring & production best practices
π¨βπ Best for: Job-ready ML engineers
π― Goal: Production-grade ML deployment skills
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