Course Introduction
This bootcamp is a complete, practical introduction to using Python for data science and machine learning. Taught by Jose Portilla, the course combines numerical computing, data manipulation, visualization, and hands-on machine learning workflows.
With clear explanations and real datasets, it guides learners through the essential tools of the data science ecosystem—NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, TensorFlow, and even Spark for big data analysis. Suitable for beginners with some programming background, the training provides an end-to-end foundation for real analytical and ML tasks.
Instructor: Jose Portilla
Rating: 4.6 / 5 (155,505 ratings)
Learners: 793,981 students
Languages: English, Arabic (Auto)
Last Updated: —
Duration: 25 hours on-demand video
Certificate: Included
Access: Lifetime on mobile & TV
Extras: 13 articles • 5 downloadable resources
Key Highlights
- Covers the full Python data science stack: NumPy, Pandas, Matplotlib, Seaborn, Plotly
- Practical ML projects using Scikit-learn and TensorFlow
- Introduction to Spark for big data workflows
- Real datasets used for clustering, regression, classification, and NLP
- Step-by-step implementations of common machine learning algorithms
- Clear and accessible explanations suitable for beginners and upskillers
- Strong focus on applied examples rather than just theory
What You Will Learn
Python for Data Science
- Numerical computing with NumPy
- Data wrangling, merging, cleaning, and preprocessing with Pandas
- Summary statistics, exploratory analysis, and dataset transformations
Data Visualization
- Static plotting with Matplotlib
- Statistical visualizations using Seaborn
- Interactive dashboards using Plotly
- Visual analysis for insight discovery
Machine Learning Essentials
Using Scikit-learn, you will build models such as:
- Linear & Logistic Regression
- Random Forests & Decision Trees
- Support Vector Machines (SVM)
- K-Means Clustering
- Spam filters & NLP pipelines
- Basic Neural Networks
Deep Learning Foundations
- Introduction to TensorFlow
- Building simple neural network models
- Understanding tensors, layers, and model training
Big Data Tools
- Using Spark for distributed data processing
- Handling large datasets and parallel workflows
Hands-On Workflows
- Practical exercises built into every module
- Real-world examples of ML model building and tuning
- End-to-end data projects that mirror industry practices
Frequently Asked Questions (FAQ)
Q — Do I need advanced programming skills?
A — No. Basic programming experience is sufficient; the course covers all required Python libraries.
Q — Does the course include machine learning projects?
A — Yes. Nearly every ML algorithm is paired with a hands-on example using real datasets.
Q — Is this course suitable for absolute beginners in data science?
A — Yes. It’s designed to introduce the entire ecosystem step-by-step.
Q — Does the course cover deep learning?
A — It includes an introductory TensorFlow module with basic neural network implementation.
Q — Can this help with starting a data science career?
A — The course provides strong foundational skills used in modern data science workflows.
Q — Is Spark included?
A — Yes. A module on Spark introduces big data concepts and distributed analysis.
Why This Course Stands Out
Jose Portilla’s teaching style is clear and structured, making complex subjects accessible. The curriculum balances theory with practice and exposes learners to nearly every major tool used by data scientists today. The inclusion of Spark, TensorFlow, and Scikit-learn makes this a broad, versatile starting point.
Final Verdict
If you’re looking to enter data science or upskill into machine learning, this bootcamp offers one of the most approachable and practical paths. Its focus on real datasets, core libraries, and ML algorithms makes it a reliable foundation for further specialization.
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