This is a Python course for absolute beginners. You will learn to write Python programs, perform text processing, apply simple machine learning concepts, and so much more! By the time you’re finished with this intensive video training, you will have gone from zero experience to a fairly serious, early intermediate level. Supplemental Material included!
Learning with Study 365 has many advantages. The course material is delivered straight to you and can be adapted to fit in with your lifestyle. It is created by experts within the industry, meaning you are receiving accurate information, which is up-to-date and easy to understand.
This course is comprised of professional learning materials, all delivered through a system that you will have access to 24 hours a day, 7 days a week for 365 days (12 months).
Who is it for?
- Folks with zero programming experience looking to learn a new skill
- Machine Learning and Language Processing folks looking to apply concepts in a full-fledged programming language
- Computer Science students or software engineers with no experience in Java, but experience in Python, C++ or even C#. You might need to skip over some bits, but in general the class will still have new learning to offer you.
This course consists of the following modules:
- Module 01: What is coding? – It’s a lot like cooking!
- Module 02: Don’t Jump Through Hoops, Use Dictionaries, Lists and Loops
- Module 03: Our First Serious Program
- Module 04: Doing Stuff with Files
- Module 05: Functions are like Food Processors
- Module 06: Databases – Data in rows and columns
- Module 07: An Object Oriented State of Mind
- Module 08: Natural Language Processing and Python
- Module 09: Machine Learning and Python
From the day you purchase the course, you will have 12 months access to the online study platform. As the course is self-paced you can decide how fast or slow the training goes, and are able to complete the course in stages, revisiting the training at any time.
Method of Assessment:
At the end of each module, you will have one assignment to be submitted (you need a mark of 65% to pass) and you can submit the assignment at any time. You will only need to pay £19 for assessment and certification when you submit the assignment. You will receive the results within 72 hours of submittal, and will be sent a certificate in 7-14 days if you have successfully passed.
Successful candidates will be awarded a certificate for Machine Learning – Python Programming: From Beginner to Intermediate.
Learners must be age 16 or over and should have a basic understanding of the English Language, numeracy, literacy, and ICT.
This course will not train you to do a specific job, but it will provide you with the skills and knowledge to use Python. Whether you’re a computer science student, someone just getting into programming, or if you need to use it for work, this course will teach you something new.
Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.
Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft
Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too
Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum
Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum
PLEASE NOTE: We do not provide any software with this course.
|Machine Learning – Python Programming From Beginner to Intermediate||FREE||00:00:00|
|1: What is coding? – It’s a lot like cooking!|
|2. Coding is like Cooking||00:00:00|
|3. Anaconda and Pip||00:00:00|
|4. Variables are like containers||00:00:00|
|2: Don’t Jump Through Hoops, Use Dictionaries, Lists and Loops|
|1. A List is a list||00:00:00|
|2. Fun with Lists!||00:00:00|
|3. Dictionaries and If-Else||00:00:00|
|4. Don’t Jump Through Hoops, Use Loops||00:00:00|
|5. Doing stuff with loops||00:00:00|
|6. Everything in life is a list – Strings as lists||00:00:00|
|3: Our First Serious Program|
|1. Modules are cool for code-reuse||00:00:00|
|2. Our first serious program : Downloading a webpage||00:00:00|
|3. A few details – Conditionals||00:00:00|
|4. A few details – Exception Handling in Python||00:00:00|
|4: Doing Stuff with Files|
|1. A File is like a barrel||00:00:00|
|2. Auto Generating Spreadsheets with Python||00:00:00|
|3. Auto Generating Spreadsheets – Download and Unzip||00:00:00|
|4. Auto Generating Spreadsheets – Parsing CSV files||00:00:00|
|5. Auto Generating Spreadsheets with XLSXwriter||00:00:00|
|5: Functions are like Food Processors|
|1. Functions are like Food processors||00:00:00|
|2. Argument Passing in Functions||00:00:00|
|3. Writing your first function||00:00:00|
|5. Recursion in Action||00:00:00|
|6: Databases – Data in rows and columns|
|1. How would you implement a Bank ATM?||00:00:00|
|2. Things you can do with Databases – I||00:00:00|
|3. Things you can do with Databases – II||00:00:00|
|4. Interfacing with Databases from Python||00:00:00|
|5. SQLite works right out of the box||00:00:00|
|6. Manually downloading the zip files required||00:00:00|
|7. Build a database of Stock Movements – I||00:00:00|
|8. Build a database of Stock Movements – II||00:00:00|
|9. Build a database of Stock Movements – III||00:00:00|
|7: An Object Oriented State of Mind|
|1. Objects are like puppies!||00:00:00|
|2. A class is a type of variable||00:00:00|
|3. An Interface drives behaviour||00:00:00|
|8: Natural Language Processing and Python|
|1. Natural Language Processing with NLTK||00:00:00|
|2. Natural Language Processing with NLTK – See it in action||00:00:00|
|3. Web Scraping with BeautifulSoup||00:00:00|
|4. A Serious NLP Application : Text Auto Summarization using Python||00:00:00|
|5. Auto Summarize News Articles – I||00:00:00|
|6. Auto Summarize News Articles – II||00:00:00|
|7. Auto Summarize News Articles – III||00:00:00|
|9: Machine Learning and Python|
|1. Machine Learning – Jump on the Bandwagon||00:00:00|
|2. Plunging In – Machine Learning Approaches to Spam Detection||00:00:00|
|3. Spam Detection with Machine Learning Continued||00:00:00|
|4. News Article Classification using K-Nearest Neighbors||00:00:00|
|5. News Article Classification using Naive Bayes||00:00:00|
|6. Code Along – Scraping News Websites||00:00:00|
|7. Code Along – Feature Extraction from News articles||00:00:00|
|8. Code Along – Classification with K-Nearest Neighbours||00:00:00|
|9. Code Along – Classification with Naive Bayes||00:00:00|
|10. Document Distance using TF-IDF||00:00:00|
|11. News Article Clustering with K-Means and TF-IDF||00:00:00|
|12. Code Along – Clustering with K-Means||00:00:00|
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