Advanced Machine Learning Models with Python
Build the ML models and algorithms that define a professional Data Scientist — hands-on model building, evaluation, and accuracy enhancement, across three focused modules.
Build the popular models. Learn to evaluate and improve them.
Build important and popular Machine Learning models and algorithms for working as a professional Data Scientist — hands-on model building, plus evaluation and accuracy enhancement techniques.
Those working toward, or already in, a Data Scientist role.
For those working or aspiring to work as Data Scientists. Requires a strong statistics foundation, R or Python proficiency, and business experience.
Machine Learning basics, applied across industries.
APPLICATIONS: Finance (credit scoring) · Retail (segmentation, forecasting) · Healthcare (disease prediction) · Real Estate (price prediction)
- Introduction to Machine Learning
- Linear & Logistic Regression
- Classification, Decision Tree, Random Forest
- KNN, K-Means, Naive Bayes
- Model Evaluation Metrics
Where model accuracy actually gets won.
APPLICATIONS: Finance (fraud detection, stock forecasting) · Retail (recommendations, demand forecasting) · Telecom (churn) · Manufacturing (predictive maintenance)
- Time Series (ETS/ARIMA)
- Recommender Systems
- SVM, PCA & Dimensionality Reduction
- Gradient Boosting (XGBoost, LightGBM)
- Regularization (Ridge, Lasso, Elastic Net)
- Model Tuning & Cross-Validation
The techniques behind chatbots, sentiment tools, and modern LLMs.
APPLICATIONS: Banking (document processing, compliance) · Retail (sentiment) · Real Estate (property descriptions) · Healthcare (clinical notes) · Government/Legal (policy analysis, contract review)
- Introduction to NLP & Text Preprocessing
- Bag of Words / TF-IDF
- Word Embeddings (Word2Vec, GloVe, FastText)
- Sentiment Analysis & Topic Modeling (LDA)
- Sequence Models (RNN/LSTM/GRU)
- Transformers (BERT/GPT), Chatbots, Entity Recognition
The five-step journey this course sits within.
- Step 1 — Statistics
- Step 2 — Data Interpretation
- Step 3 — Big Data Analytics
- Step 4 — Machine Learning
- Step 5 — Artificial Intelligence
Approved since 2014
30+ years practitioner-led
100% in-person
Max 8 learners
Common questions about this course.
Do I need to take all three modules?
No — each module can be taken individually or together, depending on which applications are most relevant to your role.
What if I haven't completed the prerequisite levels at Ambeone?
You can qualify by passing an examination demonstrating equivalent understanding. Discount packages are also available if you'd rather complete the prerequisite courses first.
Which module should I start with?
Module 1 (AI Foundation) if you're newer to Machine Learning; Modules 2 or 3 if you already have foundational ML experience and want to specialize in ensemble methods or NLP specifically.