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Data Science & ML

Transform raw data into insights and predictions. Master Python data tools, machine learning algorithms, deep learning, and deploy real ML models — all with live 1-on-1 guidance.

⏱ 12 Weeks Duration
🎯 Intermediate Level
👥 1-on-1 Coaching
🌍 Online Live Format
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76Students enrolled
97%Satisfaction
12Weeks
Starting from
€99/month
Book Free Intro Call
✅ 1-on-1 live sessions
✅ Session recordings
✅ Datasets & notebooks
✅ ML project portfolio
✅ Certificate on completion
Learning Outcomes

What You'll Be Able to Build

🔍 Exploratory data analysis pipelines using pandas and seaborn
🤖 Machine learning models for classification, regression, and clustering
📈 Time series forecasting systems for business data
🧠 Deep learning models using TensorFlow and Keras
💬 NLP models for text classification and sentiment analysis
🚀 Deployed ML APIs serving live predictions via FastAPI
Full Curriculum

12-Week Course Outline

Every week = 1-on-1 coaching session + Jupyter notebook + hands-on project

Week 1
Python for Data Science
5 lessons · 1 assignment
▼
📖 NumPy arrays, broadcasting, and vectorised operations
📖 Pandas DataFrames — creating, indexing, and slicing
📖 Reading CSV, Excel, and JSON data
📖 Jupyter notebooks for data science workflow
📖 Introduction to Google Colab
🎯 Assignment: Analyse a real dataset — answer 10 business questions using pandas
Week 2
Exploratory Data Analysis (EDA)
5 lessons · 1 project
▼
📖 Descriptive statistics — mean, median, std, quartiles
📖 Missing data: detection and imputation strategies
📖 Outlier detection with IQR and z-scores
📖 Correlation analysis and heatmaps
📖 Feature engineering basics
🎯 Project: EDA report on the Titanic or UK housing dataset
Week 3
Data Visualisation
4 lessons · 1 project
▼
📖 Matplotlib — line, bar, scatter, histogram
📖 Seaborn for statistical visualisation
📖 Plotly for interactive charts
📖 Building dashboards with Streamlit
🎯 Project: Interactive COVID-19 trends dashboard deployed on Streamlit
Week 4
ML Fundamentals & Scikit-learn
5 lessons · 1 project
▼
📖 Supervised vs unsupervised learning
📖 Train/validation/test split
📖 Cross-validation strategies
📖 Linear regression — theory and implementation
📖 Scikit-learn pipelines
🎯 Project: Predict house prices using Linear and Ridge Regression
Week 5
Classification Algorithms
5 lessons · 1 project
▼
📖 Logistic Regression
📖 Decision Trees and visualisation
📖 Random Forest
📖 SVM and K-Nearest Neighbours
📖 Evaluation: accuracy, precision, recall, F1, ROC-AUC
🎯 Project: Email spam classifier with evaluation report
Week 6
Ensemble Methods & Tuning
4 lessons · 1 project
▼
📖 Bagging and Boosting concepts
📖 XGBoost and LightGBM
📖 Hyperparameter tuning with GridSearchCV and Optuna
📖 Feature importance and SHAP values
🎯 Project: Compete on a Kaggle tabular dataset — aim for top 20%
Week 7
Unsupervised Learning
4 lessons · 1 project
▼
📖 K-Means clustering and the elbow method
📖 PCA for dimensionality reduction
📖 DBSCAN for density-based clustering
📖 Anomaly detection techniques
🎯 Project: Customer segmentation for a retail dataset
Week 8
Time Series Analysis
4 lessons · 1 project
▼
📖 Time series components: trend, seasonality, noise
📖 ARIMA models
📖 Facebook Prophet
📖 Evaluating forecast accuracy (MAE, MAPE)
🎯 Project: Sales forecasting model for a retail chain
Week 9
Deep Learning Foundations
4 lessons · 1 project
▼
📖 Neural network architecture
📖 TensorFlow and Keras basics
📖 Backpropagation and optimisers (Adam, SGD)
📖 Regularisation and dropout
🎯 Project: MNIST handwritten digit recogniser with 99%+ accuracy
Week 10
CNNs & Image Classification
4 lessons · 1 project
▼
📖 Convolutional Neural Networks (CNNs)
📖 Transfer learning with ResNet and EfficientNet
📖 Data augmentation techniques
📖 Real image datasets (CIFAR, custom)
🎯 Project: Dog vs cat image classifier with 95%+ accuracy
Week 11
NLP & Text Analysis
4 lessons · 1 project
▼
📖 Text preprocessing (tokenisation, stemming, stop words)
📖 TF-IDF and Bag of Words
📖 BERT fine-tuning with Hugging Face
📖 Sentiment analysis and text classification
🎯 Project: Product review sentiment analyser
Week 12
Deploying ML Models
4 lessons · 1 project
▼
📖 Saving and loading models (pickle, joblib, ONNX)
📖 FastAPI model serving
📖 Docker for ML models
📖 MLflow for experiment tracking
🎯 Project: Deploy your ML model as a live REST API
Capstone
🏆 Capstone Project
End-to-end data science project from raw data to deployment
▼
📖 Problem definition and data collection
📖 EDA, feature engineering, and model selection
📖 Training, tuning, and evaluation
📖 Deployment and presentation
🏆 Deliverable: A complete data science project from raw data to a live deployed model
Tools You'll Learn

Technologies & Libraries

🐍 Python
🔢 NumPy
🐼 Pandas
📊 Matplotlib & Seaborn
🔷 Plotly
🤖 Scikit-learn
⚡ XGBoost
🧠 TensorFlow / Keras
🤗 Hugging Face
⚡ FastAPI
🌊 Streamlit
📊 MLflow

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