Introduction to Machine Learning for Data Science is one of the highest-rated courses. Students can understand Computer Science, AI, Data Science, and Big Data through this course. They will also understand how these different domains fit together and also how they are different from each other. The “Backyard Data Scientist” will lead the students through the wilderness of Machine Learning for Data Science in this introductory course.
This introductory course, which is open to all, not only explains Machine Learning, but also where it fits in the “techno sphere around us,” why it’s important now, and how it will dramatically change our world today and in the future. Currently, Udemy is offering Introduction to Machine Learning for Data Science course for up to 87 % off i.e. INR 449 (INR 3,499).
Who all can opt for this course?
- Students will learn the fundamentals in this basic course so you can start getting hands-on
- Anyone who is curious about how machine learning is applied in data science
- Company executives, managers, app developers, and the customer
- People with a sense of adventure who are prepared to enter the mysterious world of data science and machine learning
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 05 Hours |
| Rating | 4.4/5 |
| Student Enrollment | 57,542 students |
| Instructor | David Valentine https://www.linkedin.com/in/davidvalentine |
| Topics Covered | A special message for hard-of-hearing and ESL students, Computer Science – The `Train Wreck’ Definition, What is Machine Learning? – Part 1 – The ideas, Impacts, Importance, and examples – Overview |
| Course Level | Intermediate |
| Total Student Reviews | 12,344 |
Learning Outcomes
- Genuinely comprehend the meaning of the terms “computer science,” “algorithms,” “programming,” “data,” “big data,” “artificial intelligence,” “machine learning,” and “data science
- The effects that data science and machine learning are having on society
- Realizing the extent to which computer technology has altered the world requires a comprehension of its scope
- Should be aware of the issues that machine learning can resolve and the operation of the machine learning process
- How to properly apply machine learning without losing your head! How to prevent complications with it
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Introduction (21 minutes) | Course Promotion Video |
| A special message for hard-of-hearing and ESL students | ||
| Thank you for investing in this Course! | ||
| Course Overview | ||
| The secret sauce inside!: How to get the most out of this course. | ||
| Course Links Reference Guide and Lecture Resources | ||
| Course Survey | ||
| 2. | Core Concepts (01 hour 06 minutes) | Core Concepts Overview |
| Computer Science – The `Train Wreck’ Definition | ||
| What’s Data / “I can see data everywhere!” | ||
| Structured vs Unstructured Data | ||
| Structured and Unstructured Data | ||
| Computer Science – Definition Revisited & The Greatest “lie” ever SOLD | ||
| What’s big data? | ||
| Big Data – Quiz | ||
| What is Artificial Intelligence (AI) | ||
| What is Machine Learning? – Part 1 – The ideas | ||
| What is Machine Learning? – Part 2 – An Example | ||
| What is data science? | ||
| Recap & How do these relate to each other? | ||
| 3. | Impacts, Importance, and examples (35 minutes) | Impacts, Importance, and examples – Overview |
| Why is this important now? | ||
| Computers exploding! – The explosive growth of computer power explained. | ||
| What problems does Machine Learning Solve? | ||
| Where it’s transforming our lives | ||
| 4. | The Machine Learning Process (34 minutes) | The Machine Learning Process – Overview |
| 5-Step Machine Learning Process Overview | ||
| 1 – Asking the right question | ||
| 2 – Identifying, obtaining, and preparing the right data | ||
| 3 – Identifying and applying an ML Algorithm | ||
| 4 – Evaluating the performance of the model and adjusting | ||
| 5 – Using and presenting the model | ||
| Machine Learning – Process | ||
| 5. | How to apply Machine Learning for Data Science (14 minutes) | How to apply Machine Learning for Data Science – Overview |
| Where to begin your journey | ||
| Common platforms and tools for Data Science | ||
| Data Science using – R | ||
| Data Science using – Python | ||
| Data Science using SQL | ||
| Data Science using Excel | ||
| Data Science using RapidMiner | ||
| Cautionary Tales | ||
| 6. | Conclusion (40 seconds) | All done! What’s next? |
| 7. | Section 1 -Bonus course – Machine Learning in Python and Jupyter for Beginners (19 minutes) | Introduction and Anaconda Installation |
| What will we cover? | ||
| Introduction and Setup | ||
| 8. | Section 2 -Bonus course – Machine Learning in Python and Jupyter for Beginners (24 minutes) | Crash course in Python – Beginning concepts |
| Crash course in Python – Strings, Slices, and Lists! | ||
| Crash course in Python – Expressions, Operators, Conditions, and Loops | ||
| Crash course in Python – Functions, Scope, Dictionaries, and more! | ||
| 9. | Section 3 – Bonus course – Machine Learning in Python and Jupyter for Beginners (10 minutes) | Hands-on Running Python |
| 10. | Section 4 – Bonus course – Machine Learning in Python and Jupyter for Beginners (17 minutes) | Foundations of Machine Learning and Data Science – Definitions and concepts. |
| Foundations of Machine Learning and Data Science – Machine Learning Workflow | ||
| Foundations of Machine Learning and Data Science – Algorithms, concepts, and more | ||
| 11. | Section 5 -Bonus course – Machine Learning in Python and Jupyter for Beginners (22 minutes) | Introducing the essential modules for Machine Learning, and NumPy Basics |
| Pandas and Matplotlib | ||
| Analysis using Pandas, plotting in Matplotlib, intro to SciPy and Scikit-learn | ||
| 12. | Section 6 – Bonus course – Machine Learning in Python and Jupyter for Beginners (55 minutes) | A Titanic Example – Getting our start. |
| A Titanic Example – Understanding the data set. | ||
| A Titanic Example – Understanding the data set in regard to survival | ||
| A Titanic Example – Preparing the right data and applying a basic algorithm | ||
| A Titanic Example – Applying regression algorithms. | ||
| A Titanic Example – Applying Decision Trees (example of overfit and underfit) | ||
| 13. | Section 7 -Bonus course – Machine Learning in Python and Jupyter for Beginners (06 minutes) | Conclusions – for our Titanic Example, important concepts and where to go next! |
| 14. | Bonus Content (03 minutes) | Bonus Article – The startling breakthrough in Machine Learning from 2016. |
Resources Required
- You need this course before you install Python or launch R
- Although it would be helpful for students to have some background in statistics, this course can be understood without it.
Featured Review
Ayush Jain (5/5): It is wonderful training that helps us to understand the idea of what Machine Learning is, what Data Science is, and how it is used. The scenarios and case study provides a great idea of the concepts and information shared during the session.
Pros
- Darina Horvathova (5/5): A wonderful course for beginners with lots of additional resources and inspiration.
- YU Jiang (5/5): This is an excellent course that helps me understand the concept of machine learning.
- Bhogireddy Raghu Ram (5/5): The course is interesting, and I had to go through this !!!!.
- Harish Chawla (4/5): Very good overview and hands-on training on Machine Learning and Data Science.
Cons
- Patrick Maxwell (2/5): Steel’s firewall blocked it, but inexplicably the link in the second one worked.
- Jochem Stoel (1/5): I was under the impression by now at least we had gotten into tensors or word-to-vector implementations or whatever.
- Patrick Maxwell (2/5): This was OK for me but might be confusing or annoying (to the point of abandonment!) to others.
- Jaya Bharath Bhavanam (2/5): For the time we are spending, very less information and experience to take away.
About the Author
The instructor of this course is David Valentine who is a Backyard Data Scientist with a 4.4 Instructor Rating and 12,470 Reviews on Udemy. He offers 2 Courses and has taught 85,061 Students so far.
- With more than 17 years of experience in business computing systems, Mr. David Valentine is a distinguished enterprise architect
- He is currently employed with the Province of Manitoba in Canada, where he is in charge of the server and mainframe computing environment’s architecture
- Data Science, Computer Science, Machine Learning, and Data Science are all areas of interest for Mr. Valentine
- As the “Backyard Data Scientist,” he brings to data science his expertise and talent for demystifying complex technological concepts
- He’s excited to share “Machine Learning for Data Science,” his first course, with the world on the Udemy platform
Comparison Table
| Parameters | Introduction to Machine Learning for Data Science | R Programming Hands-on Specialization for Data Science (Lv1) | Complete iOS Machine Learning Masterclass |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 5.5 hours | 11 hours | 7.5 hours |
| Rating | 4.4 /5 | 4.4 /5 | 4.5 /5 |
| Student Enrollments | 57,542 | 21,444 | 16,727 |
| Instructors | David Valentine | Irfan Elahi | Yohann Taieb |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
Introduction to Machine Learning for Data Science: FAQs
Ques. What is machine learning in data science?
Ans. A data analysis technique called machine learning automates the creation of analytical models. It is a subfield of artificial intelligence founded on the notion that machines are capable of learning from data, spotting patterns, and making judgments with little assistance from humans.
Ques. What are the basics of machine learning?
Ans. Artificial intelligence is used in machine learning, where a computer or machine learns from its prior experiences (input data) and predicts the future. Such a system’s performance ought to be at least human-level. The system learns from the given data set in order to complete task T.
Ques. What are the 7 steps of machine learning?
Ans. The quantity and quality of your data determine how accurate our model is. – Data collection. – The preparation of data. Gather data and get it ready for training. selecting a model, training it, evaluating it, fine-tuning its parameters, and making predictions.
Ques. What is a good definition of machine learning?
Ans. A subfield of artificial intelligence (AI) and computer science called machine learning focuses on using data and algorithms to simulate how humans learn, gradually increasing the accuracy of the system.
Ques. What are machine learning examples?
Ans. A well-known and common application of machine learning in the real world is image recognition. Based on the intensity of the pixels in black-and-white or color photos, it can recognize an object as a digital image. Examples of image recognition in the real world Indicate if an x-ray is malignant or not.
Ques. What are the 4 basics of machine learning?
Ans. – Supervised Education. When a machine has sample data, such as input and output data with accurate labels, supervised learning is appropriate. – Learning without supervision. Reward-Based Learning – Learning under semi-supervision.
Ques. What is machine learning with real-life examples?
Ans. A well-known and common application of machine learning in the real world is image recognition. Based on the intensity of the pixels in black-and-white or color photos, it can recognize an object as a digital image. Examples of image recognition in the real world Indicate if an x-ray is malignant or not.
Ques. What are the basics needed for machine learning?
Ans. Variables, linear equations, function graphs, histograms, and statistical means must all be concepts you are familiar with. You ought to be a competent coder. You should ideally have some prior Python programming knowledge since the exercises are in Python.
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