Get faster, deeper, and wider insights from your data using machine learning techniques and models. If you enjoy finding answers to big questions, this course in machine learning will give you the foundational skills and knowledge to create your own machine learning solutions. Develop algorithms and models to make predictions, identify patterns, and forecast future trends using Python programming language and libraries.
COURSE LENGTH:
COURSE STRUCTURE:
- Introduction to Machine Learning
- Advanced Statistical Learning
- Supervised Learning
- Unsupervised Learning
- Model Evaluation & Hyperparameter Tuning
- Ensemble Methods
- Neural Networks
- Final Project
STUDY OPTIONS:
Part Time
1 hour live online evening class per week over 8 weeks. Plus 32 hours of online learning.
SUMMARY:
This exciting and practical course will help you solve real-world business problems by creating predictive models to extract insight from large volumes of data. You will learn to manipulate huge datasets, including cleaning, joining, and transforming them for use in machine learning models. You will build and train machine learning models using Python libraries such as scikit-learn and TensorFlow, making accurate predictions or classifications based on data.
You will also discover how to evaluate model performance using established metrics such as accuracy, precision, recall, and F1 score, and to choose the best model for a given task. The skills taught are invaluable for data-focused roles in any industry (ecommerce, healthcare, finance, and entertainment to name only a few), allowing you to analyse the increasingly diverse and complex datasets collected by organisations today.
An important feature of this course is that you will be given access to self-guided learning on the DataCamp platform, a browser-based data science learning tool used by many of the world’s biggest companies and institutions. After each live lecture, you will go on to further explore the concepts through DataCamp topics, practical demonstrations, and exercises. This hands-on practice of the techniques learned will help to cement and develop your newly acquired skills.
CAREER OPPORTUNITIES:
This course will help you advance your career by demonstrating your practical knowledge of machine learning algorithms, experience in using machine learning libraries such as scikit-learn and TensorFlow, and proficiency using Python for data analysis and machine learning applications. These skills are transferable across diverse data-centric roles, making you a competitive candidate for positions ranging from data analyst to machine learning engineer. Data science skills such as the ones learnt on this course can be leveraged by professionals from fields as diverse as human resources, finance, sales, manufacturing, healthcare, marketing, and education. Data analytics and machine learning techniques help you make better decisions and improve outcomes in any role.
ASSESSMENT AND CERTIFICATION
Your progress through the course will be marked by milestones that are reviewed and evaluated by the instructor, who will provide you with feedback. Your formal, summative assessment is through a final project, with supporting documentation, which is the application of machine learning models to a real-world dataset. You can use any dataset that is relevant to your work or a personal interest, as long as it does not contain personal information. The final project should demonstrate your ability to apply the concepts and techniques learned in the course and to communicate the results and insights effectively.
Your certificate will be issued electronically on a secure platform, with a link that you can share with employers and others wishing to verify your credentials. You’ll also be able to add the certification to your LinkedIn profile to demonstrate your achievement to your network as well as recruiters and potential employers searching for individuals with your skills and experience.
This course is not on the National Framework of Qualifications.
LEARNER PROFILE:
This course is ideal for anyone who has already built a skillset importing, cleaning, joining, and manipulating datasets, for example by completing our Professional Academy Certificate in Data Analytics:Visualisation. A knowledge of Python is essential, as is previous experience working with packages such as pandas and NumPy for data import, cleaning, manipulation, and visualisation. You may be a data analyst, data scientist, business analyst, or other professional who regularly works with data and wants to improve their ability to create predictive models and extract insights from data. The course does not cover other programming languages such as R or Julia, or advanced topics in NLP or CV, big data processing, or distributed computing frameworks like Apache Spark. There is a large self guided portion to this course – you will be expected to attend eight hours of live online lectures and complete 32 hours of online learning on the DataCamp platform, plus time for exercises and assignments.
