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Data Science and Machine Learning using Python - A Bootcamp

4.5 (15 ratings) 5 enrolled Updated Nov 2026
CertificateOn completion
Self-pacedLearn at your own speed
80 lessonsOn the UISC learning portal
All LevelsNo prior training needed

Course curriculum

1 modules 80 lessons 0 min total
01 Data Science and Machine Learning using Python - A Bootcamp 80 lessons
  1. Welcome & Course Overview
  2. Set-up the Environment for the Course (lecture 1)
  3. Protected: Set-up the Environment for the Course (lecture 2)
  4. Two other options to setup environment
  5. Protected: Python data types Part 1
  6. Protected: Python Data Types Part 2
  7. Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1)
  8. Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2)
  9. Python Essentials Exercises Overview
  10. Python Essentials Exercises Solutions
  11. Protected: What is Numpy? A brief introduction and installation instructions.
  12. NumPy Essentials – NumPy arrays, built-in methods, array methods and attributes.
  13. NumPy Essentials – Indexing, slicing, broadcasting & boolean masking
  14. NumPy Essentials – Arithmetic Operations & Universal Functions
  15. NumPy Essentials Exercises Overview
  16. Protected: NumPy Essentials Exercises Solutions
  17. What is pandas? A brief introduction and installation instructions.
  18. Pandas Introduction
  19. Protected: Pandas Essentials – Pandas Data Structures – Series
  20. Pandas Essentials – Pandas Data Structures – DataFrame
  21. Pandas Essentials – Handling Missing Data
  22. Protected: Pandas Essentials – Data Wrangling – Combining, merging, joining
  23. Pandas Essentials – Groupby
  24. Protected: Pandas Essentials – Useful Methods and Operations
  25. Protected: Pandas Essentials – Project 1 (Overview) Customer Purchases Data
  26. Pandas Essentials – Project 1 (Solutions) Customer Purchases Data
  27. Pandas Essentials – Project 2 (Overview) Chicago Payroll Data
  28. Protected: Pandas Essentials – Project 2 (Solutions Part 1) Chicago Payroll Data
  29. Protected: Matplotlib Essentials (Part 1) – Basic Plotting & Object Oriented Approach
  30. Matplotlib Essentials (Part 2) – Basic Plotting & Object Oriented Approach
  31. Matplotlib Essentials (Part 3) – Basic Plotting & Object Oriented Approach
  32. Matplotlib Essentials – Exercises Overview
  33. Matplotlib Essentials – Exercises Solutions
  34. Seaborn – Introduction & Installation
  35. Seaborn – Distribution Plots
  36. Seaborn – Categorical Plots (Part 1)
  37. Seaborn – Categorical Plots (Part 2)
  38. Seborn-Axis Grids
  39. Seaborn – Matrix Plots
  40. Seaborn – Regression Plots
  41. Protected: Seaborn – Controlling Figure Aesthetics
  42. Seaborn – Exercises Overview
  43. Seaborn – Exercise Solutions
  44. Pandas Built-in Data Visualization
  45. Pandas Data Visualization Exercises Overview
  46. Panda Data Visualization Exercises Solutions
  47. Plotly & Cufflinks – Interactive & Geographical Plotting (Part 1)
  48. Plotly & Cufflinks – Interactive & Geographical Plotting (Part 2)
  49. Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Overview)
  50. Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Solutions)
  51. Protected: Project 1 – Oil vs Banks Stock Price during recession (Overview)
  52. Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 1)
  53. Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 2)
  54. Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 3)
  55. Project 2 (Optional) – Emergency Calls from Montgomery County, PA (Overview)
  56. Protected: Introduction to ML – What, Why and Types…..
  57. Protected: Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff
  58. scikit-learn – Linear Regression Model – Hands-on (Part 1)
  59. scikit-learn – Linear Regression Model Hands-on (Part 2)
  60. Good to know! How to save and load your trained Machine Learning Model!
  61. Protected: scikit-learn – Linear Regression Model (Insurance Data Project Overview)
  62. scikit-learn – Linear Regression Model (Insurance Data Project Solutions)
  63. Protected: Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc.
  64. Protected: scikit-learn – Logistic Regression Model – Hands-on (Part 1)
  65. scikit-learn – Logistic Regression Model – Hands-on (Part 2)
  66. Protected: scikit-learn – Logistic Regression Model – Hands-on (Part 3)
  67. scikit-learn – Logistic Regression Model – Hands-on (Project Overview)
  68. Protected: scikit-learn – Logistic Regression Model – Hands-on (Project Solutions)
  69. Theory: K Nearest Neighbors, Curse of dimensionality ….
  70. Protected: scikit-learn – K Nearest Neighbors – Hands-on
  71. scikt-learn – K Nearest Neighbors (Project Overview)
  72. scikit-learn – K Nearest Neighbors (Project Solutions)
  73. Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging….
  74. scikit-learn – Decision Tree and Random Forests – Hands-on (Part 1)
  75. scikit-learn – Decision Tree and Random Forests (Project Overview)
  76. Protected: scikit-learn – Decision Tree and Random Forests (Project Solutions)
  77. Support Vector Machines (SVMs) – (Theory Lecture)
  78. scikit-learn – Support Vector Machines – Hands-on (SVMs)
  79. scikit-learn – Support Vector Machines (Project 1 Overview)
  80. scikit-learn – Support Vector Machines (Project 1 Solutions)

About this course

Overview:

Welcome to the Data Science and Machine Learning using Python Bootcamp! This comprehensive course is designed to equip participants with the essential knowledge and skills to excel in the fields of data science and machine learning using the Python programming language. Through hands-on exercises, real-world projects, and expert guidance, participants will learn the fundamental concepts, tools, and techniques required to analyze data, build predictive models, and extract valuable insights.

  • Interactive video lectures by industry experts
  • Instant e-certificate
  • Fully online, interactive course with Professional voice-over
  • Developed by qualified first aid professionals
  • Self paced learning and laptop, tablet, smartphone friendly
  • 24/7 Learning Assistance
  • Discounts on bulk purchases

Main Course Features:

  • Comprehensive coverage of Python programming fundamentals and libraries for data science and machine learning, including NumPy, Pandas, Matplotlib, and Scikit-learn.
  • Hands-on coding exercises and projects to reinforce learning and practical application of concepts.
  • In-depth exploration of data preprocessing, feature engineering, model selection, and evaluation techniques.
  • Guidance on building and deploying machine learning models for various applications, including classification, regression, clustering, and recommendation systems.
  • Introduction to deep learning and neural networks using TensorFlow and Keras.
  • Interactive lectures, demonstrations, and code-along sessions led by industry experts and experienced instructors.
  • Access to a curated selection of datasets and resources for further study and practice.
  • Ongoing support and mentorship to help participants succeed in their data science and machine learning journey.

Who Should Take This Course:

  • Aspiring data scientists, machine learning engineers, and AI enthusiasts seeking to develop practical skills in Python-based data science and machine learning.
  • Professionals looking to transition into data science roles or enhance their existing skill set with hands-on experience in Python.
  • Students and researchers interested in exploring data science and machine learning concepts for academic or professional purposes.

Learning Outcomes:

  • Master Python programming fundamentals and libraries essential for data science and machine learning.
  • Acquire practical skills in data preprocessing, visualization, and analysis using Python.
  • Build and evaluate predictive models for classification, regression, and clustering tasks.
  • Gain proficiency in deep learning techniques for image classification and natural language processing.
  • Develop the ability to interpret and communicate insights derived from data effectively.
  • Apply best practices and methodologies for developing end-to-end machine learning solutions.
  • Complete real-world projects demonstrating proficiency in data science and machine learning concepts.
  • Prepare for career opportunities in data science, machine learning, and AI with a strong foundation and portfolio of projects.

Certification

Once you’ve successfully completed your course, you will immediately be sent a digital certificate. All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.

Assessment

At the end of the Course, there will be an online assessment, which you will need to pass to complete the course. Answers are marked instantly and automatically, allowing you to know straight away whether you have passed. If you haven’t, there’s no limit on the number of times you can take the final exam. All this is included in the one-time fee you paid for the course itself.

Your certificate

CPD certificate — download it when you finish

Issued the moment you complete the final module. Every certificate lists the course aims and objectives, anticipated learning outcomes, the quality-assurance process — the elements required for verifiable CPD. Non-registrants can use it as CQC training evidence and for appraisals.

Aims & objectives Learning outcomes Unique certificate number

Learner ratings

4.5

Rated by 15 learners on the UISC learning portal · 5 enrolled

Common questions

Where do I take the course?

On the UISC learning portal. "Enrol now" takes you to this course there, where you pay securely, get your login and study at your own pace. Your certificate is issued there when you finish.

When can I start?

Immediately. Access opens the moment your order is confirmed and you can learn from any device, in any order, as many times as you like.

How is the course assessed?

A short multiple-choice or scenario-based assessment at the end of the course with instant results. You can retake it as many times as you need at no extra cost.

Can I enrol my whole team?

Yes. Organisations can buy multiple places on one invoice and receive a training record for every learner. Contact us for team pricing.

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