4.11 (1404 reviews)
☑ Learn how to use NumPy, to do fast mathematical calculations in machine learning.
☑ Learn what is Machine Learning and Data Wrangling in machine learning.
☑ Learn how to use scikit-learn for data-preprocessing in machine learning.
☑ Learn different model selection and feature selections 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.
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 towards 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 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 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.
Introduction to Machine Learning & Data Wrangling
Black Box Introduction to Machine Learning
Essential Pandas for Machine Learning
Linear Models, Trees & Preprocessing
Linear Models for Regression & Classification
Pre-Processing Techniques using scikit
Model Evaluation, Feature Selection & Pipelining
Model Selection & Evaluation
Feature Selection Techniques
Composite Estimators using Pipelines & FeatureUnions
Bayes, Nearest Neighbours & Clustering
SVM, Anomalies, Imbalanced Classes, Ensemble Methods
Handling Imbalanced Classes
Support Vector Machine
Thus far, I have learnt that machine learning (ML) is divided into three sub-divisions namely, supervised, unsupervised and reinforced. Where the supervised is task driven, unsupervised is data driven while the reinforced is environmentally driven. Also, though raw data is applied as input to ML, this has to be converted to a vector array of numbers for ML utilization. I was introduced to Linear models, trees and preprocessing - all valuable techniques in ML.
I like the content and way of teaching but some topics are not here like Random forest, CNN , etc. Overall Mr. Avantik Sir, thank you so much for providing this course free. It's great experience to learn ML
Yes, this is a good match for me. Unfortunately, the absence of subtitles makes it difficult to understand in English.
Very theoretical. Good from the academic point of view. The presenter could present more "practical approach", what is really used presently.
As the name suggests, it is worth going through small details, bigger picture concepts and some code, at the same time.
There is no clarity in his voice. I cant understand his voice or maybe it is because of background sound.
Its a good course. Perfect match for me. Communication and concept explaining of the trainer needs to be better. Some places felt like he was unsure and was running. Also captions are needed to sometimes clearly understand the words
Udemy provides you the information where he/she can use in real world this course helped me alot in my projects and apllications.
Don't have subtitles. It have some long videos time (more than 30 min). Since "Linear Models for Regression & Classification" section, I don't understand the concepts.
This is my first course on Udemy and i think that this course is the best for machine leaning on all over the online platform.
This course is Excellent but you should have basic knowledge of python before getting started with machine machine.
AI & ML are future targeted course this is the extremest condition and all AI ML will have be implementing in automation....
The content and explanation is good but the instructor needs to get a good Microphone for voice modification. The voice in Section 1 module 2 is up the mark and hope to have the similar mic settings.
So far, the introduction has been good. The trainer's video is superimposed on the presentation. So, some parts of the presentation are not visible though we are able to infer it from the speech. It would be nice if the presentation contents are fully visible.
There was a lot of helpful information in the first lecture but I think using a good microphone would make it a 100 times better. I learned a lot of stuff