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Data Science and Visualisation with Machine Learning
Overview In the contemporary landscape of technology and business, data science and visualization with machine learning have emerged as indispensable …
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19 Students
Overview
In the contemporary landscape of technology and business, data science and visualization with machine learning have emerged as indispensable skills. This course serves as a comprehensive guide to mastering these disciplines, equipping learners with the tools and knowledge necessary to navigate and thrive in the data-driven world.
The course begins with an introduction to Python, a powerful programming language widely used in data analysis and machine learning. You will delve into fundamental Python libraries such as Pandas and NumPy, learning how to manipulate and preprocess data efficiently. These skills are essential for cleaning, organizing, and transforming raw data into formats suitable for analysis and modeling.
Next, the course transitions into data visualization using Matplotlib and Seaborn. Visualizing data is crucial for understanding trends, patterns, and relationships within datasets, and these libraries provide robust tools for creating insightful charts, plots, and graphs. You will explore various visualization techniques and learn how to communicate your findings effectively through compelling visuals.
The heart of the course focuses on machine learning algorithms. You will gain a deep understanding of supervised and unsupervised learning techniques, including regression, classification, clustering, and dimensionality reduction. Through hands-on exercises and projects, you will implement these algorithms to solve real-world problems, from predicting customer behavior to identifying trends in financial data.
Moreover, the course emphasizes the entire data science workflow, from data acquisition and cleaning to model training, evaluation, and deployment. You will learn best practices for model evaluation and optimization, ensuring that your machine learning models are accurate, robust, and scalable.
By the end of the course, you will have acquired a comprehensive skill set in data science and visualization with machine learning. You will be proficient in Python programming, data manipulation, statistical analysis, data visualization, and machine learning modeling. These skills are highly sought after in industries such as finance, healthcare, e-commerce, and more, where data-driven decision-making is critical for success.
Whether you are looking to advance your career as a data scientist, analyst, or machine learning engineer, or seeking to integrate data-driven strategies into your current role, this course will provide you with the expertise and confidence to excel in the rapidly evolving field of data science.
Learning Outcomes
- Master Python libraries for data manipulation and visualization.
- Implement machine learning algorithms for various data types.
- Create insightful data visualizations using Matplotlib and Seaborn.
- Apply data preprocessing techniques for machine learning models.
- Evaluate model performance and optimize algorithms for accuracy.
What Will Make You Stand Out?
On Completion of this online course, you’ll acquire:
- CPD QS Accredited Course.
- Upon Course Completion, Earn a Complimentary PDF and a hard copy Certificate to Showcase Your Enhanced Skills.
- Lifetime access.
- 24x7 Tutor Support.
- Complete the course at your own pace.
Description
This course begins with an introduction to Python programming essentials, focusing on libraries like Pandas and NumPy for data manipulation and analysis. You’ll then progress to exploring data visualization techniques using Matplotlib and Seaborn, enabling you to present data effectively through plots, charts, and graphs. The core of the course covers machine learning algorithms such as linear regression, decision trees, and clustering methods. You’ll learn how to preprocess data, select appropriate algorithms, and evaluate model performance using techniques like cross-validation and grid search. Practical exercises and hands-on projects throughout the course will deepen your understanding and hone your skills in applying these concepts to real-world datasets. By the end, you’ll be proficient in leveraging Python and machine learning for data-driven decision-making and visual storytelling.
Who is this course for?
This course is ideal for aspiring data scientists, analysts, and anyone looking to enhance their skills in data manipulation, visualization, and machine learning. Whether you’re a beginner or have some experience in programming and statistics, this course will equip you with the tools and knowledge needed to succeed in the field of data science.
Requirements
Access to a computer with internet connectivity and a desire to learn and succeed in your home-based business venture. No prior experience or qualifications are necessary.
Certification
Upon successful completion of the Data Science and Visualisation with Machine Learning course, learners can obtain both a PDF certificate and a Hard copy certificate for completely FREE. The Hard copy certificate is available for a nominal fee of £3.99, which covers the delivery charge within the United Kingdom. Additional delivery charges may apply for orders outside the United Kingdom.
Career Path
- Data Scientist
- Machine Learning Engineer
- Business Analyst
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 | ||
Frequently Asked Questions
In the UK, the social care system is mainly managed by the local councils. People are directly employed by the councils. They often work together with the health commissioners under joint funding arrangements. Some people work for private companies or voluntary organizations hired by local councils. They help the local councils with their personal social services.
In the UK, the social care system is mainly managed by the local councils. People are directly employed by the councils. They often work together with the health commissioners under joint funding arrangements. Some people work for private companies or voluntary organizations hired by local councils. They help the local councils with their personal social services.
In the UK, the social care system is mainly managed by the local councils. People are directly employed by the councils. They often work together with the health commissioners under joint funding arrangements. Some people work for private companies or voluntary organizations hired by local councils. They help the local councils with their personal social services.
In the UK, the social care system is mainly managed by the local councils. People are directly employed by the councils. They often work together with the health commissioners under joint funding arrangements. Some people work for private companies or voluntary organizations hired by local councils. They help the local councils with their personal social services.
In the UK, the social care system is mainly managed by the local councils. People are directly employed by the councils. They often work together with the health commissioners under joint funding arrangements. Some people work for private companies or voluntary organizations hired by local councils. They help the local councils with their personal social services.
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