Machine Learning Certification Training using Python
Access Duration - 365 Days
4.5( 4 REVIEWS )
121 STUDENTS
What Will I Learn?
Gain insight into the 'Roles' played by a Machine Learning Engineer
Learn how to automate data analysis using python
Learn how to work with real-time data
Learn tools and techniques for predictive modelling
Learn machine Learning algorithms and their implementation
Overview
Machine learning is a branch of Artificial Intelligence (AI) that presents systems with the ability to learn automatically to increase their accuracy without being programmed. The primary aim is to enable the machine systems to learn on their own, without any form of human intervention. Even though most people must have heard about it, only a few fully understand what it is and its benefits to eLearning.
Machine learning focuses on creating computer algorithms that can access data, and then using it to make future predictions. Its learning process begins with observing, then checking for data, and finally making better decisions. This course is designed to help you grasp the concepts of Machine Learning. Machine learning and Data Science are intricately linked. To take your career as high as you can’t even imagine, you can become competent in both these fields, which will enable you to analyse a vast amount of data, and then proceed to extract value and provide insight on the data.
Why You Should Consider Taking this Course at Global Edulink?
Global Edulink is a leading online provider for several accrediting bodies, and provides learners the opportunity to take this exclusive course awarded by CPD. At Global Edulink, we give our fullest attention to our learners’ needs and ensure they have the necessary information required to proceed with the Course. Learners who register will be given excellent support, discounts for future purchases and be eligible for a TOTUM Discount card and Student ID card with amazing offers and access to retail stores, the library, cinemas, gym memberships and their favourite restaurants.
- Access Duration
- Who is this Course for?
- Entry Requirement
- Method of Assessment
- Certification
- Awarding Body
- Career Path & Progression
The course will be delivered directly to you, and from the date you joined the course you have 12 months of access to the online learning platform. The course is self-paced, and you can complete it in stages at any time.
- Developers aspiring to be a ‘Machine Learning Engineer'
- Analytics Managers who are leading a team of analysts
- Business Analysts who want to understand Machine Learning (ML) Techniques
- 'Python' professionals who want to design automatic predictive models
- Information Architects who want to gain expertise in Predictive Analytics
- Learners should be over the age of 16, and have a basic understanding of English, ICT and numeracy.
- A sound educational background
In order to complete the course successfully, learners will take an online assessment. This online test is marked automatically, so you will receive an instant grade and know whether you have passed the course.
Upon the successful completion of the course, you will be awarded the ‘Machine Learning Certification Training using Python’ by CPD.
CPD is an internationally recognised qualification that will make your CV standout and encourage employers to see your motivation at expanding your skills and knowledge in an enterprise.
Once you successfully complete the course, you will gain an accredited qualification that will prove your skills and expertise in the subject matter. With this qualification you can further expand your knowledge by studying related courses on this subject, or you can go onto get a promotion or salary increment in your current job role. Below given are few of the jobs this certificate will help you in, along with the average UK salary per annum according to http://payscale.com/
- Business Analyst – Up to £58k per annum
- Information Architect – Up to £55k per annum
- Analytics Manager – Up to £88k per annum
Key Features
Gain an Accredited UK Qualification
Access to Excellent Quality Study Materials
Personalised Learning Experience
Support by Phone, Live Chat, and Email
Eligible for TOTUM Discount Card
UK Register of Learning Providers Reg No : 10053842Course Curriculum
| 1: Introduction to Data Science | |||
| What is Data Science? | |||
| What does Data Science involve? | |||
| Era of Data Science | |||
| Business Intelligence vs Data Science | |||
| Life cycle of Data Science | |||
| Tools of Data Science | |||
| Introduction to Python | |||
| 2: Data Extraction, Wrangling, & Visualization | |||
| Data Analysis Pipeline | |||
| What is Data Extraction | |||
| Types of Data | |||
| Raw and Processed Data | |||
| Data Wrangling | |||
| Exploratory Data Analysis | |||
| Visualization of Data | |||
| 3: Introduction to Machine Learning with Python | |||
| Python Revision (numpy, Pandas, scikit learn, matplotlib) | |||
| What is Machine Learning? | |||
| Machine Learning Use-Cases | |||
| Machine Learning Process Flow | |||
| Machine Learning Categories | |||
| Linear regression | |||
| Gradient descent | |||
| 4: Supervised Learning - I | |||
| What is Classification and its use cases? | |||
| What is Decision Tree? | |||
| Algorithm for Decision Tree Induction | |||
| Creating a Perfect Decision Tree | |||
| Confusion Matrix | |||
| What is Random Forest? | |||
| 5: Dimensionality Reduction | |||
| Introduction to Dimensionality | |||
| Why Dimensionality Reduction | |||
| PCA | |||
| Factor Analysis | |||
| Scaling dimensional model | |||
| LDA | |||
| 6: Supervised Learning - II | |||
| What is Naïve Bayes? | |||
| How Naïve Bayes works? | |||
| Implementing Naïve Bayes Classifier | |||
| What is Support Vector Machine? | |||
| Illustrate how Support Vector Machine works? | |||
| Hyperparameter optimization | |||
| Grid Search vs Random Search | |||
| Implementation of Support Vector Machine for Classification | |||
| 7: Unsupervised Learning | |||
| What is Clustering & its Use Cases? | |||
| What is K-means Clustering? | |||
| How K-means algorithm works? | |||
| How to do optimal clustering | |||
| What is C-means Clustering? | |||
| What is Hierarchical Clustering? | |||
| How Hierarchical Clustering works? | |||
| 8: Association Rules Mining and Recommendation Systems | |||
| What are Association Rules? | |||
| Association Rule Parameters | |||
| Calculating Association Rule Parameters $ | |||
| Recommendation Engines | |||
| How Recommendation Engines work? | |||
| Collaborative Filtering | |||
| Content-Based Filtering | |||
| 9: Reinforcement Learning | |||
| What is Reinforcement Learning | |||
| Why Reinforcement Learning | |||
| Elements of Reinforcement Learning | |||
| Exploration vs Exploitation dilemma | |||
| Epsilon Greedy Algorithm | |||
| Markov Decision Process (MDP) | |||
| Q values and V values | |||
| Q – Learning | |||
| α values | |||
| 10: Time Series Analysis | |||
| What is Time Series Analysis? | |||
| Importance of TSA | |||
| Components of TSA | |||
| White Noise | |||
| AR model | |||
| MA model | |||
| ARMA model | |||
| ARIMA model | |||
| Stationarity | |||
| ACF & PACF | |||
| 11: Model Selection and Boosting | |||
| What is Model Selection? | |||
| Need of Model Selection | |||
| Cross – Validation | |||
| What is Boosting? | |||
| How Boosting Algorithms work? | |||
| Types of Boosting Algorithms | |||
| Adaptive Boosting | |||
| 12: In-Class Project | |||
| How to approach a project | |||
| Hands-On project implementation | |||
| What Industry expects | |||
| Industry insights for the Machine Learning domain | |||
| Need of Model Selection | |||
Abram Mann
This course is great. It explains everything properly and is easily understandable.
Warren Burgess
I really liked this course and the methodology of teaching. It is to the point, tells you most of the important things and covers the entire spectrum.
Harper Roberts
The approach to machine learning section is gradual as well, providing the necessary theoretical tool to study and go deep.
Marsh George
The course is fairly good. It explains the basic theory of data science and gives you basic understanding of this world.