Data Science & Machine Learning
PythonNumpyMatplotlibJupyter Notebook

Data Science & Machine Learning

Master Python, statistics, ML & deep learning via hands-on projects. Go from beginner to job-ready data scientist in 3 months.

⏱ 3 Months 🎯 ML, Data Science 💻 Online
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What You'll Learn

1
Python for Data Science Foundations
6 lessons
▶ Setting up your environment: Anaconda, Jupyter & VS Code
▶ Python essentials: variables, data types & control flow
▶ Functions, modules & writing clean code
▶ Working with files, JSON & APls
📄 Python style guide and best practices
📄 Build a data collection script
2
Data Wrangling with NumPy & Pandas
6 lessons
▶ NumPy arrays and vectorized operations
▶ Pandas Series and DataFrames deep dive
▶ Indexing, filtering & selecting data
▶ Cleaning messy data: missing values & duplicates
▶ Merging, joining & grouping datasets
📄 Clean and prepare a real-world dataset
3
Data Visualization & Storytelling
6 lessons
▶ Plotting fundamentals with Matplotlib
▶ Statistical visualization with Seaborn
▶ Interactive charts with Plotly
📄 Design principles for effective charts
▶ Telling stories with data
📄 Build a visual EDA report
4
Statistics & Probability for ML
6 lessons
▶ Descriptive statistics and distributions
▶ Probability theory and Bayes' theorem
▶ Sampling, confidence intervals & the CLT
▶ Hypothesis testing and p-values
📄 Common statistical pitfalls
📄 Statistical analysis case study
5
Exploratory Data Analysis & Feature Engineering
6 lessons
▶ The EDA workflow: questions to ask your data
▶ Detecting outliers and anomalies
▶ Feature scaling, encoding & transformation
▶ Creating new features that boost models
📄 Handling imbalanced & high-cardinality data
📄 Full EDA + feature engineering pipeline
6
Supervised Learning: Regression
6 lessons
▶ Intro to machine learning and scikit-learn
▶ Linear and polynomial regression
▶ Regularization: Ridge, Lasso & ElasticNet
▶ Model evaluation: RMSE, MAE & R²
📄 Bias-variance tradeoff explained
📄 Predict house prices
7
Supervised Learning: Classification
6 lessons
▶ Logistic regression and decision boundaries
▶ Decision trees and random forests
▶ K-Nearest Neighbors and Naive Bayes
▶ Evaluating classifiers: precision, recall & ROC-AU
▶ Cross-validation and hyperparameter tuning
📄 Customer churn prediction
8
Ensemble Methods & Model Optimization
6 lessons
▶ Bagging, boosting & stacking explained
▶ Gradient boosting with XGBoost
▶ LightGBM and CatBoost in practice
▶ Pipelines and automated workflows
📄 Model selection & deployment readiness
📄 Win a Kaggle-style competition
9
Unsupervised Learning & NLP Basics
6 lessons
▶ K-Means and hierarchical clustering
▶ Dimensionality reduction with PCA and t-SNE
▶ Text preprocessing and TF-IDF
▶ Sentiment analysis fundamentals
📄 Recommender systems overview
📄 Customer segmentation + text classifier
10
Deep Learning & Capstone Deployment
6 lessons
▶ Neural networks with TensorFlow/Keras basics
▶ Building and training your first deep network
▶ Intro to CNNs for image classification
▶ Model deployment with Streamlit and Flask
📄 ML career roadmap & portfolio tips
📄 End-to-end ML project + deployment

Technologies Covered

PythonNumpyMatplotlibJupyter Notebook

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