Machine Learning Scientist in Python

About Course

Your Journey to Machine Learning Mastery: From Beginner to Kaggle Competitor

Course Overview

Duration: 75 hours — 12 to 15 weeks at 5–6 hours/week
Level: Intermediate to Advanced
Prerequisites: Python for Data Analysts (datasciencehub.cloud) or solid working knowledge of Python, Pandas, and NumPy
Recommended Prior Courses: Python for Data Analysts, SQL for Data Analysts (datasciencehub.cloud)
Target Audience: Data analysts, business analysts, and Python developers looking to move into machine learning and predictive analytics
Tools: Python 3.11+, Scikit-learn, XGBoost, LightGBM, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Statsmodels, spaCy, Prophet, MLflow, FastAPI, Streamlit, Docker

Course Objectives

By the end of this course, students will be able to:

Build, train, and evaluate regression and classification models using Scikit-learn
Prepare raw data for machine learning — scaling, encoding, imputation, and feature engineering
Apply unsupervised learning techniques including clustering, PCA, and anomaly detection
Perform natural language processing — sentiment analysis, text classification, and topic modeling
Forecast time series data using ARIMA, Prophet, and ML-based approaches
Select, tune, and compare models using cross-validation and hyperparameter optimization
Explain model predictions using SHAP values, LIME, and feature importance techniques
Build end-to-end ML pipelines and track experiments with MLflow
Deploy ML models as live web apps using FastAPI and Streamlit
Deliver a complete, job-ready ML portfolio project from problem scoping to deployment

Next Steps After This Course

Deep Learning & Neural Networks (datasciencehub.cloud) — move into TensorFlow and PyTorch for image, text, and sequence modeling
MLOps & Production ML — CI/CD pipelines, model monitoring, Kubernetes, and cloud deployment on AWS or GCP
Advanced NLP with Transformers — BERT, GPT, and Hugging Face for state-of-the-art language models
Analytics Engineering — dbt, data pipelines, and dimensional modeling for production-grade data workflows
Specialization Tracks — Marketing Analytics, Financial Forecasting, or HR People Analytics

Build and train regression models — Linear, Ridge, Lasso, and Polynomial — and evaluate them with business-relevant metrics
Apply classification algorithms — Logistic Regression, Random Forest, XGBoost, and SVM — to real prediction problems
Prepare raw data for ML — scaling, encoding, imputation, feature engineering, and handling imbalanced datasets
Build end-to-end preprocessing and modeling pipelines using Scikit-learn's Pipeline and ColumnTransformer
Evaluate and compare models using cross-validation, learning curves, and the right metric for every business problem
Apply unsupervised learning — K-Means, hierarchical clustering, DBSCAN, and PCA for segmentation and dimensionality reduction
Detect anomalies in data using Isolation Forest, Local Outlier Factor, and One-Class SVM
Process and classify text data using TF-IDF, sentiment analysis, and NLP pipelines with spaCy
Forecast time series data using ARIMA, SARIMA, Facebook Prophet, and ML-based approaches
Engineer powerful features from raw data — date decomposition, lag features, text features, and interaction terms
Select the most impactful features using filter methods, RFE, and tree-based feature importance
Explain any model's predictions using SHAP values, LIME, and partial dependence plots
Track experiments, compare runs, and manage models professionally using MLflow
Deploy ML models as live prediction APIs using FastAPI and interactive web apps using Streamlit
Containerize ML applications with Docker for consistent and shareable deployment
Build a complete end-to-end ML project and publish a polished GitHub portfolio ready for job interviews

Course Content

Module 01 — ML Foundations & Python Refresher (5 Hours)

1.1 What is Machine Learning — supervised, unsupervised, reinforcement learning explained
1.2 The ML Workflow — problem definition, data collection, modeling, evaluation, deployment
1.3 Python Refresher for ML — NumPy, Pandas, and visualization quick review
1.4 Scikit-learn Overview — the ML library ecosystem, API design, fit/predict/transform pattern
1.5 Setting Up Your ML Environment — Anaconda, Jupyter, key libraries installation
1.6 Your First ML Model — end-to-end walkthrough from raw data to prediction in 30 minutes
1.7 Module Project: Predict House Prices (Baseline)

Module 02 — Data Preparation for ML (6 Hours)

2.1 Train/Test/Validation Split — why it matters, stratified splits, data leakage explained
2.2 Feature Scaling — StandardScaler, MinMaxScaler, RobustScaler — when to use each
2.3 Encoding Categorical Variables — LabelEncoder, OrdinalEncoder, OneHotEncoder, target encoding
2.4 Handling Missing Data for ML — imputation strategies, SimpleImputer, KNNImputer
2.5 Feature Engineering — creating new features, date decomposition, interaction terms
2.6 Handling Imbalanced Datasets — SMOTE, oversampling, undersampling, class weights
2.7 Pipelines — building end-to-end preprocessing + modeling pipelines with sklearn
2.8 Module Project: Customer Churn Data Preparation

Module 03 — Regression Algorithms (7 Hours)

3.1 Linear Regression Deep Dive — OLS assumptions, coefficients, R², residual analysis
3.2 Polynomial Regression — capturing non-linear relationships, degree selection, overfitting risk
3.3 Ridge & Lasso Regression — regularization explained, alpha tuning, feature selection with Lasso
3.4 ElasticNet — combining Ridge and Lasso, when to use it
3.5 Regression Evaluation Metrics — MAE, MSE, RMSE, MAPE, R² — interpreting each for business
3.6 Cross-Validation for Regression — KFold, cross_val_score, avoiding data leakage
3.7 Hyperparameter Tuning — GridSearchCV, RandomizedSearchCV for regression models
3.8 Module Project: Retail Sales Forecasting

Module 04 — Classification Algorithms (8 Hours)

4.1 Logistic Regression — sigmoid function, decision boundary, probability outputs, coefficients
4.2 K-Nearest Neighbors — distance metrics, choosing K, curse of dimensionality
4.3 Decision Trees — splitting criteria, depth, pruning, visualizing trees
4.4 Random Forest — bagging, feature importance, out-of-bag error, tuning n_estimators
4.5 Gradient Boosting — XGBoost, LightGBM, CatBoost — the industry workhorses
4.6 Support Vector Machines — kernels, C and gamma parameters, when SVMs shine
4.7 Naive Bayes — Gaussian, Multinomial, Bernoulli — text and probability use cases
4.8 Classification Metrics — accuracy, precision, recall, F1, ROC-AUC, confusion matrix
4.9 Module Project: Credit Card Fraud Detection

Module 05 — Model Evaluation & Selection (6 Hours)

Module 06 — Unsupervised Learning (7 Hours)

6.1 K-Means Clustering — algorithm intuition, elbow method, silhouette score, limitations
6.2 Hierarchical Clustering — dendrograms, linkage methods, when to use over K-Means
6.3 DBSCAN — density-based clustering, handling noise, no need to specify K
6.4 Principal Component Analysis (PCA) — dimensionality reduction, explained variance, visualization
6.5 t-SNE & UMAP — visualizing high-dimensional data in 2D, use cases and limitations
6.6 Anomaly Detection — Isolation Forest, Local Outlier Factor, One-Class SVM
6.7 Association Rule Mining — Apriori algorithm, support/confidence/lift, market basket analysis
6.8 Module Project: Customer Segmentation for Retail

Module 07 — Natural Language Processing (NLP) (7 Hours)

7.1 Text Preprocessing — tokenization, stopwords, stemming, lemmatization, cleaning pipelines
7.2 Bag of Words & TF-IDF — converting text to numbers for ML models
7.3 Sentiment Analysis — rule-based (VADER) and ML-based approaches
7.4 Text Classification — spam detection, topic classification with Naive Bayes and Logistic Regression
7.5 Word Embeddings — Word2Vec, GloVe, FastText — understanding semantic similarity
7.6 Named Entity Recognition — spaCy for extracting people, places, and organizations from text
7.7 Topic Modeling — LDA for discovering hidden themes in large document collections
7.8 Module Project: Product Review Sentiment Analyzer

Module 08 — Time Series Analysis & Forecasting (7 Hours)

8.1 Time Series Fundamentals — trend, seasonality, cyclicality, stationarity, autocorrelation
8.2 Classical Forecasting — moving averages, exponential smoothing, Holt-Winters
8.3 ARIMA & SARIMA — identifying p/d/q parameters, ACF/PACF plots, seasonal models
8.4 Feature Engineering for Time Series — lag features, rolling stats, calendar features
8.5 ML Models for Time Series — using XGBoost and Random Forest for forecasting
8.6 Facebook Prophet — intuitive forecasting with trend, seasonality, and holidays
8.7 Forecast Evaluation — MAE, RMSE, MAPE, SMAPE — walk-forward validation
8.8 Module Project: Retail Demand Forecasting

Module 09 — Feature Engineering & Selection (6 Hours)

9.1 Feature Engineering Techniques — binning, log transforms, ratios, polynomial features
9.2 Date & Time Feature Extraction — hour, day, month, quarter, is_weekend, days_since
9.3 Text Feature Engineering — character counts, word counts, readability scores
9.4 Feature Selection Methods — filter methods (correlation, chi-square, mutual information)
9.5 Wrapper Methods — RFE, RFECV — recursive feature elimination with cross-validation
9.6 Embedded Methods — feature importance from trees, L1 regularization for selection
9.7 Dimensionality Reduction for Features — PCA, truncated SVD in feature pipelines
9.8 Module Project: Feature Engineering Championship

Module 10 — Model Interpretability & Explainability (5 Hours)

Module 11 — ML Pipelines & Production Readiness (6 Hours)

11.1 Sklearn Pipelines Advanced — ColumnTransformer, custom transformers, pipeline serialization
11.2 Model Serialization — saving and loading models with pickle and joblib
11.3 Model Versioning — MLflow for experiment tracking, model registry, run comparison
11.4 REST API with FastAPI — serving your ML model as a live prediction endpoint
11.5 Streamlit — building interactive ML apps in pure Python, no web dev required
11.6 Docker Basics for ML — containerizing your model for consistent deployment
11.7 Monitoring & Drift Detection — detecting when your model degrades in production
11.8 Module Project: ML Model Deployment

Module 12 — Capstone: End-to-End ML Project (10 Hours)

12.1 Problem Scoping & Dataset Selection — defining business objectives, success metrics, constraints
12.2 Exploratory Data Analysis — deep EDA with statistical analysis and visualization
12.3 Data Preparation Pipeline — full cleaning, encoding, scaling, feature engineering
12.4 Model Development — training, tuning, and comparing multiple algorithms
12.5 Model Evaluation & Selection — rigorous cross-validation and business metric alignment
12.6 Explainability Report — SHAP analysis, feature importance, stakeholder-ready findings
12.7 Deployment — Streamlit app + FastAPI endpoint + MLflow experiment log
12.8 Final Presentation — project documentation, GitHub portfolio, interview-ready narrative
2.9 Capstone Project: Full ML Product

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About Course

Your Journey to Machine Learning Mastery: From Beginner to Kaggle Competitor

What Will You Learn?

Course Content

Module 01 — ML Foundations & Python Refresher (5 Hours)

1.1 What is Machine Learning — supervised, unsupervised, reinforcement learning explained

1.2 The ML Workflow — problem definition, data collection, modeling, evaluation, deployment

1.3 Python Refresher for ML — NumPy, Pandas, and visualization quick review

1.4 Scikit-learn Overview — the ML library ecosystem, API design, fit/predict/transform pattern

1.5 Setting Up Your ML Environment — Anaconda, Jupyter, key libraries installation

1.6 Your First ML Model — end-to-end walkthrough from raw data to prediction in 30 minutes

1.7 Module Project: Predict House Prices (Baseline)

Module 02 — Data Preparation for ML (6 Hours)

2.1 Train/Test/Validation Split — why it matters, stratified splits, data leakage explained

2.2 Feature Scaling — StandardScaler, MinMaxScaler, RobustScaler — when to use each

2.3 Encoding Categorical Variables — LabelEncoder, OrdinalEncoder, OneHotEncoder, target encoding

2.4 Handling Missing Data for ML — imputation strategies, SimpleImputer, KNNImputer

2.5 Feature Engineering — creating new features, date decomposition, interaction terms

2.6 Handling Imbalanced Datasets — SMOTE, oversampling, undersampling, class weights

2.7 Pipelines — building end-to-end preprocessing + modeling pipelines with sklearn

2.8 Module Project: Customer Churn Data Preparation

Module 03 — Regression Algorithms (7 Hours)

3.1 Linear Regression Deep Dive — OLS assumptions, coefficients, R², residual analysis

3.2 Polynomial Regression — capturing non-linear relationships, degree selection, overfitting risk

3.3 Ridge & Lasso Regression — regularization explained, alpha tuning, feature selection with Lasso

3.4 ElasticNet — combining Ridge and Lasso, when to use it

3.5 Regression Evaluation Metrics — MAE, MSE, RMSE, MAPE, R² — interpreting each for business

3.6 Cross-Validation for Regression — KFold, cross_val_score, avoiding data leakage

3.7 Hyperparameter Tuning — GridSearchCV, RandomizedSearchCV for regression models

3.8 Module Project: Retail Sales Forecasting

Module 04 — Classification Algorithms (8 Hours)

4.1 Logistic Regression — sigmoid function, decision boundary, probability outputs, coefficients

4.2 K-Nearest Neighbors — distance metrics, choosing K, curse of dimensionality

4.3 Decision Trees — splitting criteria, depth, pruning, visualizing trees

4.4 Random Forest — bagging, feature importance, out-of-bag error, tuning n_estimators

4.5 Gradient Boosting — XGBoost, LightGBM, CatBoost — the industry workhorses

4.6 Support Vector Machines — kernels, C and gamma parameters, when SVMs shine

4.7 Naive Bayes — Gaussian, Multinomial, Bernoulli — text and probability use cases

4.8 Classification Metrics — accuracy, precision, recall, F1, ROC-AUC, confusion matrix

4.9 Module Project: Credit Card Fraud Detection

Module 05 — Model Evaluation & Selection (6 Hours)

5.1 Bias-Variance Trade-off — underfitting vs overfitting, the sweet spot

5.2 Cross-Validation Strategies — KFold, StratifiedKFold, TimeSeriesSplit, LeaveOneOut

5.3 Evaluation Metrics Deep Dive — choosing the right metric for every business problem

5.4 Learning Curves & Validation Curves — diagnosing model performance visually

5.5 Hyperparameter Tuning Mastery — GridSearchCV, RandomizedSearchCV, Optuna intro

5.6 Comparing Multiple Models — statistical significance testing for model comparison

5.7 Module Project: Model Selection Tournament

Module 06 — Unsupervised Learning (7 Hours)

6.1 K-Means Clustering — algorithm intuition, elbow method, silhouette score, limitations

6.2 Hierarchical Clustering — dendrograms, linkage methods, when to use over K-Means

6.3 DBSCAN — density-based clustering, handling noise, no need to specify K

6.4 Principal Component Analysis (PCA) — dimensionality reduction, explained variance, visualization

6.5 t-SNE & UMAP — visualizing high-dimensional data in 2D, use cases and limitations

6.6 Anomaly Detection — Isolation Forest, Local Outlier Factor, One-Class SVM

6.7 Association Rule Mining — Apriori algorithm, support/confidence/lift, market basket analysis

6.8 Module Project: Customer Segmentation for Retail

Module 07 — Natural Language Processing (NLP) (7 Hours)

7.1 Text Preprocessing — tokenization, stopwords, stemming, lemmatization, cleaning pipelines

7.2 Bag of Words & TF-IDF — converting text to numbers for ML models

7.3 Sentiment Analysis — rule-based (VADER) and ML-based approaches

7.4 Text Classification — spam detection, topic classification with Naive Bayes and Logistic Regression

7.5 Word Embeddings — Word2Vec, GloVe, FastText — understanding semantic similarity

7.6 Named Entity Recognition — spaCy for extracting people, places, and organizations from text

7.7 Topic Modeling — LDA for discovering hidden themes in large document collections

7.8 Module Project: Product Review Sentiment Analyzer

Module 08 — Time Series Analysis & Forecasting (7 Hours)

8.1 Time Series Fundamentals — trend, seasonality, cyclicality, stationarity, autocorrelation

8.2 Classical Forecasting — moving averages, exponential smoothing, Holt-Winters

8.3 ARIMA & SARIMA — identifying p/d/q parameters, ACF/PACF plots, seasonal models

8.4 Feature Engineering for Time Series — lag features, rolling stats, calendar features

8.5 ML Models for Time Series — using XGBoost and Random Forest for forecasting

8.6 Facebook Prophet — intuitive forecasting with trend, seasonality, and holidays

8.7 Forecast Evaluation — MAE, RMSE, MAPE, SMAPE — walk-forward validation

8.8 Module Project: Retail Demand Forecasting

Module 09 — Feature Engineering & Selection (6 Hours)

9.1 Feature Engineering Techniques — binning, log transforms, ratios, polynomial features

9.2 Date & Time Feature Extraction — hour, day, month, quarter, is_weekend, days_since

9.3 Text Feature Engineering — character counts, word counts, readability scores

9.4 Feature Selection Methods — filter methods (correlation, chi-square, mutual information)