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Transformational advancements in technology in today’s world are making it possible for data scientists to develop machines that think for themselves. Based on complex algorithms that can glean information from data, today’s computers can use neural networks to mimic human brains, and make informed decisions based on the most likely scenarios. The immense possibilities that machine learning can unlock are fascinating, and with data exploding across all fields, it appears that in the near future Machine Learning will be the only viable alternative simply because there is nothing quite like it!
With so many opportunities on the horizon, a career as a Machine Learning Engineer can be both satisfying and rewarding. A good workshop, such as the one offered by KnowledgeHut, can lead you on the right path towards becoming a machine learning expert.
So what is Machine Learning? Machine learning is an application of Artificial Intelligence which trains computers and machines to predict outcomes based on examples and previous experiences, without the need of explicit programming.
Our Machine learning course will help you to master this science and understand Machine Learning algorithms, which include Supervised Learning, Unsupervised Learning, Reinforcement Learning and Semi-supervised Learning algorithms. It will help you to understand and learn:
The Machine Learning Course with Python by KnowledgeHut is a 48 hour, instructor-led live training course, with 80 hours of MCQs and assignments. It also includes 45 hours of hands-on practical session, along with 10 live projects.
Our Machine Learning course with Python will help you get hands-on experience of the following:
Machine Learning is an application of Artificial Intelligence that allows machines and computers to learn automatically to predict outcomes from examples and experiences, without there being any need for explicit programming. As the name suggests, it gives machines and computers the ability to learn, making them similar to humans.
The concept of machine learning is quite simple. Instead of writing code, data is fed to a generic algorithm. The generic algorithm/machine will build a logic which will be based on the data provided. The provided data is termed as ‘training data’ as they are used to make decisions or predictions without any program to perform the task.
1) Stanford defines Machine Learning as:
“Machine learning is the science of getting computers to act without being explicitly programmed.”
2) Nvidia defines Machine Learning as:
“Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.”
3) McKinsey & Co. defines Machine Learning as:
“Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”
4) The University of Washington defines Machine Learning as:
“Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.”
5) Carnegie Mellon University defines Machine Learning as:
“The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?”
Today, algorithms of machine learning enable computers and machines to interact with humans, write and publish sport match reports, autonomously drive cars, and find terrorist suspects as well. Let’s peek through the origins of machine learning and its recent milestones.
Alan Turing created a ‘Turing Test’ in order to determine if a computer has real intelligence. A computer should fool a human into believing that it is also a human to pass the test.
The first computer learning program was written by Arthur Samuel. The program was a game of checkers. The more that the IBM computer played the game, the more it improved at the game, as it studied the winning strategies and incorporated those moves into programs.
The first neural network for computers was designed by Frank Rosenblatt. It stimulates the thought process of the human brain.
The ‘nearest neighbour’ was written. It allowed computers to use basic pattern recognition.
Explanation-Based Learning was introduced, where a computer analyses the training data and creates a general rule which it can follow by discarding the unimportant data.
The approach towards the work on machine learning changes from a knowledge-driven approach to machine-driven approach. Programs were now created for computers to analyze a large amount of data and obtain conclusions from the results.
IBM’s Deep Blue beat the world champion in a game of chess.
Geoffrey Hinton coined the term ‘deep learning’ that explained new algorithms that let the computer distinguish objects and texts in videos and images.
The Microsoft Kinect was released, which tracked 20 human features at a rate of 30 times per second. This allowed people to interact with computers via gestures and movements.
IBM’s Watson beat its human competitors at Jeopardy.
Google Brain was developed. It discovered and categorized objects similar to the way a cat does.
Google’s X Labs developed an algorithm that browsed YouTube videos and identified those videos that contained cats.
Facebook introduced DeepFace. It is an algorithm that recognizes and verifies individuals on photos.
Microsoft launched the Distributed Machine Learning Toolkit, which distributed machine learning problems across multiple computers.
An artificial intelligence algorithm by Google, AlphaGo, beat a professional player at a Chinese board game Go.
The algorithm of machine learning is trained using a training data set so that a model can be created. With the introduction of any new input data to the ML algorithm, a prediction is made based on the model.
The accuracy of the prediction is checked and if the accuracy is acceptable, the ML algorithm is deployed. For cases where accuracy is not acceptable, the Machine Learning algorithm is trained again with supplementary training data set.
There are various other factors and steps involved as well. This is just an example of the process.
Various industries work with Machine Learning technology and have recognized its value. It has helped and continues to help organisations to work in a more effective manner, as well as gain an advantage over their competitors.
Machine Learning technology is used in the financial industry due to two key reasons: to prevent fraud and to identify important insights in data. This helps them in deciding on investment opportunities, that is, helps the investors with the process of trading, as to identify clients with high-risk profiles.
Machine learning is finding varied uses in running government initiatives. It helps in detecting fraud and minimizes identity theft. It’s also used to filter and identify citizen data.
Machine Learning in the health care sector has introduced wearable devices and sensors that use data to assess a patient’s health in real time, which might lead to improved treatment or diagnosis.
There are numerous use cases for the oil and gas industry, and it continues to expand. A few of the use cases are: finding new energy sources, predicting refinery sensor failure, analyzing minerals in the ground, etc.
Websites use Machine Learning to recommend items that you might like to buy based on your purchase history.
Machine learning has transformed various sectors of industries including retail, healthcare, finance, etc. and continues to do so in other fields as well. Based on the current trends in technology, the following are a few predictions that have been made related to the future of Machine Learning.
Understand the behavior of data as you build significant models
Learn about the various libraries offered by Python to manipulate, preprocess and visualize data
Learn about Supervised and Unsupervised Machine Learning.
Learn to use optimization techniques to find the minimum error in your machine learning model
Learn various machine learning algorithms like KNN, Decision Trees, SVM, Clustering in detail
Learn the technique to reduce the number of variables using Feature Selection and Feature Extraction
Understand Neural Network and apply them to classify image and perform sentiment analysis
Learn to use multiple learning algorithms to obtain better predictive performance
For Machine Learning, it is important to have sufficient knowledge of at least one coding language. Python being a minimalistic and intuitive coding language becomes a perfect choice for beginners.
Sign up for this comprehensive course and learn from industry experts who will handhold you through your learning journey, and earn an industry-recognized Machine Learning Certification from KnowledgeHut upon successful completion of the Machine Learning course.
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Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.
Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.
Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.
Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.
Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Get reviews and feedback on your final projects from professional developers.
Hands-on: No hands-on
Hands-on: No hands-on
Hands-on: No hands-on
With attributes describing various aspects of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descent.
This dataset classifies people described by a set of attributes as good or bad credit risks. Using logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers.
Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set contains 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes - biodegradable and non-biodegradable. Build Models to study the relationships between chemical structure and biodegradation of molecules and correctly classify if a chemical is biodegradable
In marketing, if you’re trying to talk to everybody, you’re not reaching anybody. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns.
Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).
The KnowledgeHut course covered all concepts from basic to advanced. My trainer was very knowledgeable and I really liked the way he mapped all concepts to real world situations. The tasks done during the workshops helped me a great deal to add value to my career. I also liked the way the customer support was handled, they helped me throughout the process.
KnowledgeHut is a great platform for beginners as well as experienced professionals who want to get into the data science field. Trainers are well experienced and participants are given detailed ideas and concepts.
The instructor was very knowledgeable, the course was structured very well. I would like to sincerely thank the customer support team for extending their support at every step. They were always ready to help and smoothed out the whole process.
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It is always great to talk about Knowledgehut. I liked the way they supported me until I got certified. I would like to extend my appreciation for the support given throughout the training. My trainer was very knowledgeable and I liked the way of teaching. My special thanks to the trainer for his dedication and patience.
I really enjoyed the training session and am extremely satisfied. All my doubts on the topics were cleared with live examples. KnowledgeHut has got the best trainers in the education industry. Overall the session was a great experience.
Knowledgehut is the best platform to gather new skills. Customer support here is very responsive. The trainer was very well experienced and helped me in clearing the doubts clearly with examples.
Machine learning came into its own in the late 1990s, when data scientists hit upon the concept of training computers to think. Machine learning gives computers the capability to automatically learn from data without being explicitly programmed, and the capability of completing tasks on their own. This means in other words that these programs change their behaviour by learning from data. Machine learning enthusiasts are today among the most sought after professionals. Learn to build incredibly smart solutions that positively impact people’s lives, and make businesses more efficient! With Payscale putting average salaries of Machine Learning engineers at $115,034, this is definitely the space you want to be in!
By the end of this course, you would have gained knowledge on the use of machine learning techniques using Python and be able to build applications models. This will help you land lucrative jobs as a Data Scientist.
There are no restrictions but participants would benefit if they have elementary programming knowledge and familiarity with statistics.
Yes, KnowledgeHut offers this training online.
On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.
Your instructors are Machine Learning experts who have years of industry experience.
Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details.
In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.
Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor.
Melbourne is the capital of Australia's Victoria State and is one of the most visited destinations in the country. This city is known as being Australia?s capital and is famous for its fine dining, boasting of many Michelin star restaurants. Considered as one of the most liveable cities in the world, Melbourne is host to many festivals that celebrate art and culture like the Melbourne International Film Festival, Melbourne International Comedy Festival, and the Melbourne Fringe Festival. This city is also home to some of the biggest companies in the region and has a fertile economic landscape. With this business culture in the backdrop, KnowledgeHut presents the Data Analysis using Python course in Melbourne.
About the Course
One of the fastest-growing programming languages, Python allows users to create small and large scale programs. For serious developers looking for a high-level language, Python is a great choice as it is easily learned and deployed and has many features that support easy reading and comprehension. This dynamic language is used for many sciences, engineering, and business applications, along with others like web apps and games. The Data Analysis training using Python in Melbourne is a comprehensive online training provided by KnowledgeHut that will help you grasp the fundamentals of the widely used framework. The 5 days of instructor led online classes will also include assignments and project work. In the Machine Learning using Python course in Melbourne, you will first be familiarised with the application scenarios where you can use Python for data analysis. As the course progresses, you will learn design methods in data analysis solutions like parallel processing and clustering amongst others. There will be an overview of frameworks that are available for data analysis and you will learn about sections like Matplotlib and Scipy & Numpy. During the course, our instructors will deliver lessons dedicated to Pandas, the IPython toolkit and scikit-learn. Another important section of the Data Analysis using Python course in Melbourne is learning Python's role with the context of the Hadoop eco system. At the end of this training, you will receive a course completion certification from KnowledgeHut with a credit for every hour attended. After finishing this Machine Learning training using Python, you will be able to confidently pass any exam based on the topic. The KnowledgeHut Advantage With a presence in cities in over 70 countries, KnowledgeHut is an industry leader in the field of e-learning. Our courses are delivered using the best online learning methods that combine an interactive classroom environment with easy accessibility. Along with expert tutorship, you will also receive a downloadable e-book that will give you extra guidance. Available at a great price, this Machine Learning using Python course in Melbourne is an ideal learning experience for web masters, scientists, programmers, analysts, professional software developers, and entrepreneurs and others who want to get an in-depth understanding of Python.