Applied Data Science and Machine Learning
From ML fundamentals to LLMs and vector databases
No job-placement promises — we make you interview-ready with real mock interviews and honest feedback.
Applied Data Science and Machine Learning
Course overview
Comprehensive data science course covering machine learning, deep learning, neural networks, transformers, and modern LLM applications. Includes hands-on projects with real-world datasets.
Duration
12 weeks
Format
Live + Offline
Problems
26+
Difficulty
Intermediate
What You'll Learn
Curriculum at a glance
| Week | Focus | Key topics | Problems |
|---|---|---|---|
| 1 | Python for Data Science | NumPy & Array Operations, Pandas DataFrames & Series, Data Cleaning & Feature Engineering | 3 |
| 2 | Statistics & Probability | Descriptive Statistics & Distributions, Hypothesis Testing & A/B Tests, Bayesian Statistics | 3 |
| 3 | Machine Learning Fundamentals | Linear & Logistic Regression, Decision Trees & Random Forests, SVMs & Ensemble Methods, Clustering & Dimensionality Reduction | 4 |
| 4 | Deep Learning & Neural Networks | Neural Network Foundations, CNNs for Computer Vision, RNNs & LSTMs for Sequences, Training Techniques & Regularization | 4 |
| 5 | Natural Language Processing | Text Preprocessing & Tokenization, Word Embeddings (Word2Vec, GloVe), Sequence-to-Sequence Models | 3 |
| 6 | Transformers & LLMs | Attention Mechanism Deep Dive, GPT, BERT & Model Architectures, Fine-tuning & Prompt Engineering | 3 |
| 7 | Vector Databases & RAG | Vector Embeddings & Similarity Search, Pinecone, Weaviate & ChromaDB, Building RAG Applications | 3 |
| 8 | ML System Design & Deployment | ML System Design Patterns, Model Serving & Monitoring, MLOps & CI/CD for ML | 3 |
Course Curriculum
Total: 26+ problems
Note: Curriculum may vary based on cohort progress and student needs. All enrolled students get lifetime access to course materials and recordings.
Meet Your Instructor
AlgoEngineer Instructor
Course Instructor
Our instructors are experienced software engineers passionate about teaching in a practical, interview-focused manner.
Expertise:
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