The course will teach you the various stages of Python programming. You can make Python your focus and jump straight into machine learning and data science. This is an intuitive language and will let you achieve your results within reduced time. The course features key topics such as what is coding, databases and natural language processing and python. Getting certified in this course will give you a competitive edge in the industry, and open more doors to having an exciting career. This is a Python course for absolute beginners. You will learn to write Python programmes, 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).
This course consists of the following modules:
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.
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 the course.
Successful candidates will be awarded a certificate in 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.
Once you successfully complete the course in Machine Learning – Python Programming: From Beginner to Intermediate, you will gain an accredited qualification that will prove your expertise in Python programming. You can use this certification to get a high-paying job, incentives or even a promotion. At the end of this course you will gain a high level of knowledge about Python and the ability to use it in your place of work. A career with bright prospects await you with the completion of this course. This course is not targeted at any specific trade, hence it will enable you to enter into related job positions available in the industry and will provide you with the skills and knowledge which are necessary to use Python in your place of work.
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|
|3. Anaconda and Pip|
|4. Variables are like containers|
|2: Don’t Jump Through Hoops, Use Dictionaries, Lists and Loops|
|1. A List is a list|
|2. Fun with Lists!|
|3. Dictionaries and If-Else|
|4. Don’t Jump Through Hoops, Use Loops|
|5. Doing stuff with loops|
|6. Everything in life is a list – Strings as lists|
|3: Our First Serious Program|
|1. Modules are cool for code-reuse|
|2. Our first serious program : Downloading a webpage|
|3. A few details – Conditionals|
|4. A few details – Exception Handling in Python|
|4: Doing Stuff with Files|
|1. A File is like a barrel|
|2. Auto Generating Spreadsheets with Python|
|3. Auto Generating Spreadsheets – Download and Unzip|
|4. Auto Generating Spreadsheets – Parsing CSV files|
|5. Auto Generating Spreadsheets with XLSXwriter|
|5: Functions are like Food Processors|
|1. Functions are like Food processors|
|2. Argument Passing in Functions|
|3. Writing your first function|
|5. Recursion in Action|
|6: Databases – Data in rows and columns|
|1. How would you implement a Bank ATM?|
|2. Things you can do with Databases – I|
|3. Things you can do with Databases – II|
|4. Interfacing with Databases from Python|
|5. SQLite works right out of the box|
|6. Manually downloading the zip files required|
|7. Build a database of Stock Movements – I|
|8. Build a database of Stock Movements – II|
|9. Build a database of Stock Movements – III|
|7: An Object Oriented State of Mind|
|1. Objects are like puppies!|
|2. A class is a type of variable|
|3. An Interface drives behaviour|
|8: Natural Language Processing and Python|
|1. Natural Language Processing with NLTK|
|2. Natural Language Processing with NLTK – See it in action|
|3. Web Scraping with BeautifulSoup|
|4. A Serious NLP Application : Text Auto Summarization using Python|
|5. Auto Summarize News Articles – I|
|6. Auto Summarize News Articles – II|
|7. Auto Summarize News Articles – III|
|9: Machine Learning and Python|
|1. Machine Learning – Jump on the Bandwagon|
|2. Plunging In – Machine Learning Approaches to Spam Detection|
|3. Spam Detection with Machine Learning Continued|
|4. News Article Classification using K-Nearest Neighbors|
|5. News Article Classification using Naive Bayes|
|6. Code Along – Scraping News Websites|
|7. Code Along – Feature Extraction from News articles|
|8. Code Along – Classification with K-Nearest Neighbours|
|9. Code Along – Classification with Naive Bayes|
|10. Document Distance using TF-IDF|
|11. News Article Clustering with K-Means and TF-IDF|
|12. Code Along – Clustering with K-Means|
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