Machine Learning with Python

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Overview Learn how to solve real-life problems in your personal life, business or job with Machine Learning algorithms. Machine Learning …


Learn how to solve real-life problems in your personal life, business or job with Machine Learning algorithms. Machine Learning with Python course will take learners from beginner level to advanced level. By the completion of the course, individuals would gain an understanding of machine learning algorithms that would enable them to tackle most real-world problems.  It is a step by step approach and hands-on training that will teach learners how to gain an understanding of machine learning tools, communicate effectively using Seaborn and learn how to use decision trees.

It is a comprehensive course that makes machine learning enjoyable and fun. If you are a data scientist, this course is perfect for you. Some things you can do upon understanding machine learning algorithms include how to classify flowers, identify staff that wants to leave before their time, identify handwriting and so much more.

The Machine Learning with Python course is created for individuals who have basic experience in Python programming. It is a widely used programming language that is often sought out by big name companies and corporations. Machine Learning with Python course will enable learners to take their Python programming skills to the next level and an opportunity to advance in their career.

  • Why consider learning at Study 365?
  • Learning Outcomes
  • Access Duration
  • Who is this Course aimed at?
  • Entry Requirement
  • Method of Assessment
  • Certification & Awarding Body
  • Tutor Info
  • Progression and Career Path
  •  Other Benefits

With so many commitments in our lives, we may not have the time to learn new skills. The beauty of studying online is that you get to find this balance between your education and your daily commitments

Study 365 offers hundreds of online courses to students across the world. Gaining new skills has never been this easy as many of our courses are open to students with little or no qualifications or previous experience. All the courses are up-to-date, relevant, affordable, and will build on existing expertise or give you a jump-start to a new career. The courses are meticulously designed and equals what is offered in a classroom setting. With a solid reputation that has established and made us made us one of the most trusted and reliable online course providers we offer the most convenient path to gain recognised skills and training that will give you the opportunity to put into practice your knowledge and expertise in your chosen career. You can learn at your own pace at Study 365 and you will be provided with all the necessary material, tutorials, qualified course instructor, narrated e-learning modules, interactive quizzes and free resources which include Free CV writing pack, free career support and course demo to make your learning experience enriching and more rewarding.

  • Learn about Movielens – a famous dataset with movie ratings
  • Use Pandas to read and play around with the data
  • Learn how to use Scipy and Numpy
  • Introduction to Latent Factor Methods
  • Introduction to Memory-based Approaches
  • Design & implement a Recommendation System in Python

The course will be directly delivered to you, and you have 12 months access to the online learning platform from the date you joined the course.  The course is self-paced and you can complete it in stages, revisiting the lectures at any time.

  • Analytics professionals, big data professionals who haven’t had exposure to machine learning
  • Engineers who want to understand or learn machine learning and apply it to real-world problems
  • Product managers
  • Tech executives who are interested in big data, machine learning
  • Anyone who has an interest in machine learning and want to move beyond the basics
  • This course requires no formal pre-requisites
  • It would be helpful to have working knowledge of Python

At the end of the course, 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.

Those who successfully complete the course will be awarded the Machine Learning with Python certificate by CPD & iAP. The qualification will make you valuable to employers, and your motivation at gaining new skills will be recognised.

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

The qualification will prove your expertise in Machine Learning and make you a competitive candidate in the industry. You can demonstrate your knowledge and impress potential employers. You have the opportunity to gain a wide range of job roles with this certification.

Once you successfully complete the Machine Learning with Python you will gain an accredited qualification that will give your career the jumpstart you have always wanted. With this qualification you can further expand your education or go onto work in numerous positions that will also put you in line to demand a higher salary or job promotion. The average UK salary per annum according to is given below.

  • .Net Programmer - £30,875 per annum
  • Web Developer - £24,833 per annum
  • Computer Programmer - £30,400 per annum
  • Software Engineer - £32,516 per annum
  • Software Developer - £24,833 per annum
  • Product Manager - £34,271 per annum
  • Written and designed by the industry’s finest expert instructors with over 15 years of experience
  • Repeat and rewind all your lectures and enjoy a personalised learning experience
  • Gain access to quality video tutorials
  • Unlimited 12 months access from anywhere, anytime
  • Save time and money on travel
  • Learn at your convenience and leisure
  • Eligible for TOTUM discount card

Course Curriculum

1: Course Introduction
1.1 Course Introduction
2: Machine Learning Concepts
2.1 Section Introduction
2.2 Supervised and Unsupervised Learning
2.3 Semi-Supervised Learning
2.4 Section Summary
3: First Ml Application
3.1 Section Introduction
3.2 Installing the Environment
3.3 Hello World
3.4 Installing Aaconda and Deep Learning Libraries
3.5 Email Spam Checker – Part 1
3.6 Email Spam Checker – Part 2
3.7 Email Spam Checker Results
3.8 Iris 70:30 – Part 1
3.9 Iris 70:30 – Part 2
3.10 Section Summary
4: Data Analysis
4.1 Section Introduction
4.2 Data Analysis – Example 1
4.3 Data Analysis – Example 2
4.4 Data Visualization
4.5 Section Summary
5: Linear Algebra
5.1 Section Introduction
5.2 Parametric Algorithms
5.3 Linear Algebra
5.4 Linear Regression Calculation – Part 1
5.5 Linear Regression Calculation – Part 2
5.6 Regression on Larger Dataset – Part 1
5.7 Regression on Larger Dataset – Part 2
5.8 Regression on Larger Dataset – Part 3
5.9 Section Summary
6: Natural Language Processing
6.1 Section Introduction
6.2 Natural Language Processing – Part 1
6.3 Natural Language Processing – Part 2
6.4 Tokenizing Content
6.5 Processing Unique Words
6.6 Summarizing Headlines – Part 1
6.7 Summarizing Headlines – Part 2
6.8 Summarizing Headlines – Part 3
6.9 Section Summary
7: Clustering
7.1 Section Introduction
7.2 Cluster Introduction
7.3 EM and M Clustering
7.4 Clustering Code Walkthrough
7.5 Clustering Iris Data – Part 1
7.6 Clustering Iris Data – Part 2
7.7 Clustering Iris Data – Part 3
7.8 Dendrogram Graphs
7.9 Section Summary
7.10 Course Summary

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