A beginners guide to learning Machine Learning (including Hands-on projects – From Basic to Advance Levels)
This course includes:
- 7 hours of on-demand video
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
4.2
(1,890 ratings)
273,772 students
Created by EdYoda Digital University, Awantik Das
What you’ll learn
- Learn how to use NumPy, to do fast mathematical calculations in machine learning.
- Learn what Machine Learning and Data Wrangling in machine learning are.
- Learn how to use sci-kit-learn for data preprocessing in machine learning.
- Learn different model selection and feature selection techniques in machine learning.
- Learn about cluster analysis and anomaly detection in machine learning.
- Learn about SVMs for classification, regression, and outliers detection in machine learning.
Course content
5 sections • 16 lectures • 6h 49m total lengthCollapse all sections
Introduction to Machine Learning & Data Wrangling3 lectures • 1hr 22min
- Black Box Introduction to Machine LearningPreview18:30
- Essential NumPy – Machine Learning22:25
- Essential Pandas for Machine Learning41:05
Linear Models, Trees & Preprocessing3 lectures • 2hr 14min
- Linear Models for Regression & Classification – Machine Learning51:35
- Pre-Processing Techniques using Scikit – Machine Learning48:11
- Decision Trees – Machine Learning34:38
Model Evaluation, Feature Selection & Pipelining3 lectures • 1hr 8min
- Model Selection & Evaluation – Machine Learning29:55
- Feature Selection Techniques – Machine Learning14:49
- Composite Estimators using Pipelines & FeatureUnions – Machine Learning22:58
Bayes, Nearest Neighbours & Clustering3 lectures • 1hr 1min
- Naive Bayes – Machine Learning20:24
- Nearest Neighbors – Machine Learning19:55
- Cluster Analysis – Machine Learning20:40
SVM, Anomalies, Imbalanced Classes, Ensemble Methods4 lectures • 1hr 4min
- Anomaly Detection – Machine Learning17:41
- Handling Imbalanced Classes – Machine Learning13:17
- Support Vector Machine – Machine Learning16:42
- Ensemble Methods – Machine Learning16:39
Requirements
- Basic knowledge of scripting and programming
- Basic knowledge of python programming
Description
If you are looking to start your career in Machine learning then this is the course for you.
This is a course designed in such a way that you will learn all the concepts of machine learning right from basic to advanced levels.
This course has 5 parts as given below:
- Introduction & Data Wrangling in machine learning
- Linear Models, Trees & Preprocessing in machine learning
- Model Evaluation, Feature Selection & Pipelining in machine learning
- Bayes, Nearest Neighbors & Clustering in machine learning
- SVM, Anomalies, Imbalanced Classes, Ensemble Methods in machine learning
For the code explained in each lecture, you can find a GitHub link in the resources section.
Who’s teaching you in this course?
I am Professional Trainer and consultant for Languages C, C++, Python, Java, Scala, Big Data Technologies – PySpark, Spark using Scala Machine Learning & Deep Learning- sci-kit-learn, TensorFlow, TFLearn, Keras, h2o and delivered at corporates like GE, SCIO Health Analytics, Impetus, IBM Bangalore & Hyderabad, Redbus, Schnider, JP Morgan – Singapore & HongKong, CISCO, Flipkart, MindTree, DataGenic, CTS – Chennai, HappiestMinds, Mphasis, Hexaware, Kabbage. I have shared my knowledge that will guide you to understand the holistic approach toward ML.
Machine learning is the fuel we need to power robots, alongside AI. With Machine Learning, we can power programs that can be easily updated and modified to adapt to new environments and tasks to get things done quickly and efficiently.
Here are a few reasons for you to pursue a career in Machine Learning:
1) Machine learning is a skill of the future – Despite the exponential growth in Machine Learning, the field faces a skill shortage. If you can meet the demands of large companies by gaining expertise in Machine Learning, you will have a secure career in a technology that is on the rise.
2) Work on real challenges – Businesses in this digital age face a lot of issues that Machine learning promises to solve. As a Machine Learning Engineer, you will work on real-life challenges and develop solutions that have a deep impact on how businesses and people thrive. Needless to say, a job that allows you to work and solve real-world struggles gives you high satisfaction.
3) Learn and grow – Since Machine Learning is on the boom, by entering into the field early on, you can witness trends firsthand and keep on increasing your relevance in the marketplace, thus augmenting your value to your employer.
4) An exponential career graph – All said and done, Machine learning is still in its nascent stage. And as the technology matures and advances, you will have the experience and expertise to follow an upward career graph and approach your ideal employers.
5) Build a lucrative career– The average salary of a Machine Learning engineer is one of the top reasons why Machine Learning seems a lucrative career to a lot of us. Since the industry is on the rise, this figure can be expected to grow further as the years pass by.
6) Side-step into data science – Machine learning skills help you expand avenues in your career. Machine Learning skills can endow you with two hats- the other of a data scientist. Become a hot resource by gaining expertise in both fields simultaneously and embark on an exciting journey filled with challenges, opportunities, and knowledge.
Machine learning is happening right now. So, you want to have an early bird advantage of toying with solutions and technologies that support it. This way, when the time comes, you will find your skills in much higher demand and will be able to secure a career path that’s always on the rise.
Enroll Now!! See You in Class.
Happy learning
Team Edyoda
Who this course is for:
- Beginners who want to become a data, scientists
- Software developers who want to learn machine learning from scratch
- Python developers who are interested to learn machine learning
- Professionals who want to start their career in Machine Learning
