Back to Courses
Data Science and Machine Learning using Python - A Bootcamp
Learn Data Science and Machine Learning using Python with projects covering Python essentials, data analysis, visualization, and machine learning models.
Course Highlights
Understand the exciting field of data science and machine learning with our all-inclusive bootcamp, “Data Science and Machine Learning using Python.” This comprehensive course is designed for experts and aspiring data enthusiasts who want to use Python for machine learning, data analysis, and visualisation. Starting with a strong foundation in Python fundamentals, the journey guarantees that you have the coding skills needed for the fascinating modules that come next.
The course unfolds with an exploration of Python libraries such as NumPy and Pandas, enabling you to perform robust data analysis. You’ll learn to extract valuable insights from datasets, manipulate information efficiently, and handle complex data structures. You will become proficient with Matplotlib, Seaborn, and Plotly as you move smoothly into the field of data visualisation and produce eye-catching visual representations of data.
As you progress, your concentration shifts to understanding machine learning and the intricate algorithms that support intelligent systems. Models like as PCA, K Means Clustering, Decision Trees, Random Forests, SVMs, Linear Regression, and Logistic Regression may all be implemented with Scikit-Learn as a toolkit. Beyond the fundamentals, the course explores further subjects such as Recommender Systems and Natural Language Processing with NLTK. Upon completion, you will possess a diverse skill set that encompasses data manipulation and predictive model creation, equipping you for positions like data scientist, machine learning engineer, or data analyst in the rapidly changing data field.
This program is more than just education; it’s about changing how you see data. Whether you want to progress in your career or start a new one, this training offers opportunities in data-driven decision-making. Come join us and explore the thrilling connections between Data Science and Machine Learning.
Learning outcome
- Master Python programming for data analysis and visualization.
- Understand essential machine learning algorithms and their applications.
- Develop skills in implementing machine learning models using Scikit-Learn.
- Apply Python libraries for data manipulation, exploration, and visualization.
- Gain skills through a Capstone Project and additional topics like NLP and Recommender Systems.
Course media
Why should I take this course?
- Acquire essential skills in Data Science and Machine Learning.
- Python programming and its libraries with projects.
- Enhance your career prospects in data-driven industries.
- Build a strong foundation in machine learning algorithms.
- Develop proficiency in data analysis, visualization, and model implementation.
Career Path
- Data Scientist
- Machine Learning Engineer
- Data Analyst
- Business Intelligence Analyst
- AI Engineer
Requirements
- Basic understanding of programming concepts.
- Familiarity with Python is beneficial but not mandatory.
- Passion for learning and applying new technologies.
Course Curriculum
Course Curriculum
| Module 01: 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 | ||
| Module 02: 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 | ||
| Module 03: 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 | ||
| Module 04: 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 | ||
| Module 05: 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 | ||
| Module 06: 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 | ||
| Module 07: 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 | ||
| Module 08: 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 | ||
| Module 09: 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 | ||
| Module 10: 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 | ||
| Module 11: 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 | ||
| Module 12: 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 | ||
| Module 13: 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 | ||
| Module 14: 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 | ||
| Module 15: 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 | ||
| Module 16: 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 | ||
| Module 17: 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 | ||
| Module 18: 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 | ||
| Resource | |||
| Resources – Data Science and Machine Learning using Python – A Bootcamp | 00:00:00 | ||
Don't just take our word for it
Amazing workshop.
Very informative for the freshers like me. Learned a lot of things about digital marketing. I'm really thankful.
Aarini Sinha 
It was a great experience. The course was jampacked with excellent information. And when I needed help, Skill-up helped me with technical problems instantly. Fast feedback.
Varsha Sivamohan 
I am currently signed on to several courses with skill up. I have just finished my second one. The courses are informative and good for all types of learners. I recommend them.
Judith Taylor 
The support team was FANTASTIC!!! Very quick to respond and even though I was nervous as I am not good at tests, they made me feel comfortable.
I am very grateful for the support
Charlie Anderson 
Completed the Level 3 Mental Health Support Worker course, very good and highly recommend it.
Very good course, enjoyed it thoroughly and important in my line of work as a Support worker.
Parmjit Dosanjh 
I am pleased to have completed my online course in Mental Health First Aid with SkillUp. All the modules were detailed & comprehensive with review questions, which really helped me to grasp the information & prepared me well for the mock test & final exam.
Shan 
I was a bit nervous about the course but I gave a try,it's perfect and I still can't believe I paid a peanut to acquire such a course,the sliding show and introduction was well understood.i will recommend skill up to anyone interested to persuade a meaningful career,and also to the support team keep doing the good work.💪
Sara Nana 
Original price was: $457.31.$34.75Current price is: $34.75.
1 Year Access 
1 Students 
1 day