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Data Science and Machine Learning using Python : A Bootcamp
CPDUK Accredited | 50% OFF Certificate & Transcript
22 Students enrolled on this course 4.7 (7 Reviews)
Last updated May 19, 2023
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Learning Outcomes
After completing this course, learners will be able to:
- Learn Python for data analysis using NumPy and Pandas
- Acquire a clear understanding of data visualisation using Matplotlib, Seaborn and Pandas
- Deepen your knowledge of Python for interactive and geographical potting using Plotly and Cufflinks
- Understand Python for data science and machine learning
- Get acquainted with Recommender Systems with Python
- Enhance your understanding of Python for Natural Language Processing (NLP)
Description
Whether you are from an engineering background or not you still can efficiently work in the field of data science and the machine learning sector, if you have proficient knowledge of Python. Since Python is the easiest and most used programming language, you can start learning this language now to advance your career with the Data Science And Machine Learning Using Python : A Bootcamp course.
This course will give you a thorough understanding of the Python programming language. Moreover, it will show how can you use Python for data analysis and machine learning. Alongside that, from this course, you will get to learn data visualisation, and interactive and geographical plotting by using Python. The course will also provide detailed information on Python for data analysis, Natural Language Processing (NLP) and much more.
Upon successful completion of this course, get a CPD- certificate of achievement which will enhance your resume and career.
Certificate of Achievement
After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.
Method of Assessment
After completing this course, you will be provided with some assessment questions. To pass that assessment, you need to score at least 60%. Our experts will check your assessment and give you feedback accordingly.
Career Path
After completing this course, you can explore various career options such as
- Web Developer
- Software Engineer
- Data Scientist
- Machine Learning Engineer
- Data Analyst
In the UK professionals usually get a salary of £25,000 – £30,000 per annum for these positions.
Course Curriculum
| Welcome, Course Introduction & overview, and Environment set-up | |||
| Welcome & Course Overview | 00:07:00 | ||
| Set-up the Environment for the Course (lecture 1) | 00:09:00 | ||
| Set-up the Environment for the Course (lecture 2) | 00:25:00 | ||
| Two other options to setup environment | 00:04:00 | ||
| Python Essentials | |||
| Python data types Part 1 | 00:21:00 | ||
| Python Data Types Part 2 | 00:15:00 | ||
| Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) | 00:16:00 | ||
| Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) | 00:20:00 | ||
| Python Essentials Exercises Overview | 00:02:00 | ||
| Python Essentials Exercises Solutions | 00:22:00 | ||
| Python for Data Analysis using NumPy | |||
| What is Numpy? A brief introduction and installation instructions. | 00:03:00 | ||
| NumPy Essentials – NumPy arrays, built-in methods, array methods and attributes. | 00:28:00 | ||
| NumPy Essentials – Indexing, slicing, broadcasting & boolean masking | 00:26:00 | ||
| NumPy Essentials – Arithmetic Operations & Universal Functions | 00:07:00 | ||
| NumPy Essentials Exercises Overview | 00:02:00 | ||
| NumPy Essentials Exercises Solutions | 00:25:00 | ||
| Python for Data Analysis using Pandas | |||
| What is pandas? A brief introduction and installation instructions. | 00:02:00 | ||
| Pandas Introduction | 00:02:00 | ||
| Pandas Essentials – Pandas Data Structures – Series | 00:20:00 | ||
| Pandas Essentials – Pandas Data Structures – DataFrame | 00:30:00 | ||
| Pandas Essentials – Handling Missing Data | 00:12:00 | ||
| Pandas Essentials – Data Wrangling – Combining, merging, joining | 00:20:00 | ||
| Pandas Essentials – Groupby | 00:10:00 | ||
| Pandas Essentials – Useful Methods and Operations | 00:26:00 | ||
| Pandas Essentials – Project 1 (Overview) Customer Purchases Data | 00:08:00 | ||
| Pandas Essentials – Project 1 (Solutions) Customer Purchases Data | 00:31:00 | ||
| Pandas Essentials – Project 2 (Overview) Chicago Payroll Data | 00:04:00 | ||
| Pandas Essentials – Project 2 (Solutions Part 1) Chicago Payroll Data | 00:18:00 | ||
| Python for Data Visualization using matplotlib | |||
| Matplotlib Essentials (Part 1) – Basic Plotting & Object Oriented Approach | 00:13:00 | ||
| Matplotlib Essentials (Part 2) – Basic Plotting & Object Oriented Approach | 00:22:00 | ||
| Matplotlib Essentials (Part 3) – Basic Plotting & Object Oriented Approach | 00:22:00 | ||
| Matplotlib Essentials – Exercises Overview | 00:06:00 | ||
| Matplotlib Essentials – Exercises Solutions | 00:21:00 | ||
| Python for Data Visualization using Seaborn | |||
| Seaborn – Introduction & Installation | 00:04:00 | ||
| Seaborn – Distribution Plots | 00:25:00 | ||
| Seaborn – Categorical Plots (Part 1) | 00:21:00 | ||
| Seaborn – Categorical Plots (Part 2) | 00:16:00 | ||
| Seborn-Axis Grids | 00:25:00 | ||
| Seaborn – Matrix Plots | 00:13:00 | ||
| Seaborn – Regression Plots | 00:11:00 | ||
| Seaborn – Controlling Figure Aesthetics | 00:10:00 | ||
| Seaborn – Exercises Overview | 00:04:00 | ||
| Seaborn – Exercise Solutions | 00:19:00 | ||
| Python for Data Visualization using pandas | |||
| Pandas Built-in Data Visualization | 00:34:00 | ||
| Pandas Data Visualization Exercises Overview | 00:03:00 | ||
| Panda Data Visualization Exercises Solutions | 00:13:00 | ||
| Python for interactive & geographical plotting using Plotly and Cufflinks | |||
| Plotly & Cufflinks – Interactive & Geographical Plotting (Part 1) | 00:19:00 | ||
| Plotly & Cufflinks – Interactive & Geographical Plotting (Part 2) | 00:14:00 | ||
| Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Overview) | 00:11:00 | ||
| Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Solutions) | 00:37:00 | ||
| Capstone Project - Python for Data Analysis & Visualization | |||
| Project 1 – Oil vs Banks Stock Price during recession (Overview) | 00:15:00 | ||
| Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 1) | 00:18:00 | ||
| Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 2) | 00:18:00 | ||
| Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 3) | 00:17:00 | ||
| Project 2 (Optional) – Emergency Calls from Montgomery County, PA (Overview) | 00:03:00 | ||
| Python for Machine Learning (ML) - scikit-learn - Linear Regression Model | |||
| Introduction to ML – What, Why and Types….. | 00:15:00 | ||
| Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff | 00:15:00 | ||
| scikit-learn – Linear Regression Model – Hands-on (Part 1) | 00:17:00 | ||
| scikit-learn – Linear Regression Model Hands-on (Part 2) | 00:19:00 | ||
| Good to know! How to save and load your trained Machine Learning Model! | 00:01:00 | ||
| scikit-learn – Linear Regression Model (Insurance Data Project Overview) | 00:08:00 | ||
| scikit-learn – Linear Regression Model (Insurance Data Project Solutions) | 00:30:00 | ||
| Python for Machine Learning - scikit-learn - Logistic Regression Model | |||
| Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc. | 00:10:00 | ||
| scikit-learn – Logistic Regression Model – Hands-on (Part 1) | 00:17:00 | ||
| scikit-learn – Logistic Regression Model – Hands-on (Part 2) | 00:20:00 | ||
| scikit-learn – Logistic Regression Model – Hands-on (Part 3) | 00:11:00 | ||
| scikit-learn – Logistic Regression Model – Hands-on (Project Overview) | 00:05:00 | ||
| scikit-learn – Logistic Regression Model – Hands-on (Project Solutions) | 00:15:00 | ||
| Python for Machine Learning - scikit-learn - K Nearest Neighbors | |||
| Theory: K Nearest Neighbors, Curse of dimensionality …. | 00:08:00 | ||
| scikit-learn – K Nearest Neighbors – Hands-on | 00:25:00 | ||
| scikt-learn – K Nearest Neighbors (Project Overview) | 00:04:00 | ||
| scikit-learn – K Nearest Neighbors (Project Solutions) | 00:14:00 | ||
| Python for Machine Learning - scikit-learn - Decision Tree and Random Forests | |||
| Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging…. | 00:18:00 | ||
| scikit-learn – Decision Tree and Random Forests – Hands-on (Part 1) | 00:19:00 | ||
| scikit-learn – Decision Tree and Random Forests (Project Overview) | 00:05:00 | ||
| scikit-learn – Decision Tree and Random Forests (Project Solutions) | 00:15:00 | ||
| Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) | |||
| Support Vector Machines (SVMs) – (Theory Lecture) | 00:07:00 | ||
| scikit-learn – Support Vector Machines – Hands-on (SVMs) | 00:30:00 | ||
| scikit-learn – Support Vector Machines (Project 1 Overview) | 00:07:00 | ||
| scikit-learn – Support Vector Machines (Project 1 Solutions) | 00:20:00 | ||
| scikit-learn – Support Vector Machines (Optional Project 2 – Overview) | 00:02:00 | ||
| Python for Machine Learning - scikit-learn - K Means Clustering | |||
| Theory: K Means Clustering, Elbow method ….. | 00:11:00 | ||
| scikit-learn – K Means Clustering – Hands-on | 00:23:00 | ||
| scikit-learn – K Means Clustering (Project Overview) | 00:07:00 | ||
| scikit-learn – K Means Clustering (Project Solutions) | 00:22:00 | ||
| Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) | |||
| Theory: Principal Component Analysis (PCA) | 00:09:00 | ||
| scikit-learn – Principal Component Analysis (PCA) – Hands-on | 00:22:00 | ||
| scikit-learn – Principal Component Analysis (PCA) – (Project Overview) | 00:02:00 | ||
| scikit-learn – Principal Component Analysis (PCA) – (Project Solutions) | 00:17:00 | ||
| Recommender Systems with Python - (Additional Topic) | |||
| Theory: Recommender Systems their Types and Importance | 00:06:00 | ||
| Python for Recommender Systems – Hands-on (Part 1) | 00:18:00 | ||
| Python for Recommender Systems – – Hands-on (Part 2) | 00:19:00 | ||
| Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) | |||
| Natural Language Processing (NLP) – (Theory Lecture) | 00:13:00 | ||
| NLTK – NLP-Challenges, Data Sources, Data Processing ….. | 00:13:00 | ||
| NLTK – Feature Engineering and Text Preprocessing in Natural Language Processing | 00:19:00 | ||
| NLTK – NLP – Tokenization, Text Normalization, Vectorization, BoW…. | 00:19:00 | ||
| NLTK – BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes … | 00:13:00 | ||
| NLTK – NLP – Pipeline feature to assemble several steps for cross-validation… | 00:09:00 | ||
| Resources | |||
| Resources – Data Science and Machine Learning using Python : A Bootcamp | 00:00:00 | ||
| Order your Certificates & Transcripts | |||
| Order your Certificates & Transcripts | 00:00:00 | ||
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Frequently asked questions
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There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course.
You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience.
For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime.
Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks.
We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase.
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If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.