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 solve data problems using major Machine Learning algorithms, which includes 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 sessions 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.
1950:
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.
1952:
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.
1957:
The first neural network for computers was designed by Frank Rosenblatt. It stimulates the thought process of the human brain.
1967:
The ‘nearest neighbour’ was written. It allowed computers to use basic pattern recognition.
1981:
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.
1990:
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.
1997:
IBM’s Deep Blue beat the world champion in a game of chess.
2006:
Geoffrey Hinton coined the term ‘deep learning’ that explained new algorithms that let the computer distinguish objects and texts in videos and images.
2010:
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.
2011:
IBM’s Watson beat its human competitors at Jeopardy.
2011:
Google Brain was developed. It discovered and categorized objects similar to the way a cat does.
2012:
Google’s X Labs developed an algorithm that browsed YouTube videos and identified those videos that contained cats.
2014:
Facebook introduced DeepFace. It is an algorithm that recognizes and verifies individuals on photos.
2015:
Microsoft launched the Distributed Machine Learning Toolkit, which distributed machine learning problems across multiple computers.
2016:
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 well as identify clients with high-risk profiles.
Machine learning has various sources of data that can be drawn used for insights. It also helps in detecting fraud and minimizes identity theft.
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.
What is the future of Machine Learning?
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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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).
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The KnowledgeHut course taught us concepts ranging from basic to advanced. My trainer was very knowledgeable and I really liked the way of teaching. Various concepts and tasks during the workshops given by the trainer helped me to add value to my career. I also liked the way the customer support was handled, they helped me throughout the process.
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Knowledgehut is the best training provider with the best trainers in the education industry. Highly knowledgeable trainers have covered all the topics with live examples. Overall the training session was a great experience.
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.
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KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.
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.
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Home to Gandhi's Ashram, the city of Ahmedabad is one of the most significant cultural and historical cities of India. It is the largest city in the prosperous state of Gujarat and the Sabarmati River runs through its centre. The Calico Museum of Textiles is one of the famous landmarks of this city, saluting the rich business heritage of the region. India's second oldest stock exchange is also in Ahmedabad and the grand Sardar Patel stadium is a testimony to the town's love for cricket. Ahmedabad is one of the most important centres of business and innovation in the region. In this enterprising city, KnowledgeHut presents the Data Analysis using Python course in Ahmedabad.
About the Course
A very popular programming language that is used by serious programmers across the world, Python facilitates small and large scale programs and has an easy learning curve as well as being easy to deploy. Python is a language equipped with many useful features including easy readability and comprehension. Python is used extensively in business, engineering and scientific applications and this dynamic language is very popular amongst makers of games and web apps. A comprehensive training program by KnowledgeHut, the Data Analysis training using Python in Ahmedabad will help participants grasp the features and uses of this programming language, especially with respect to data analysis and machine learning applications. This course will be led by expert instructors and along with the theory, will also involve practical exercises. After finishing this online training you, KnowledgeHut will award you a course completion certification. These interactive online classes will arm you with the knowledge and proficiency to crack any exam on data analysis using Python. During the Data Analysis using Python course in Ahmedabad you will subject to many intensive exercises and lessons where our instructors will teach you techniques to analyse, manipulate, manage and organize data using specialized tools and libraries. The online program commences with an introduction to data analysis with Python and its various application scenarios. Clustering and machine learning are just two amongst the various design methodologies in data analysis that will be taught at this Machine Learning using Python course in Ahmedabad. There will also be a section on plotting Matplotlib and you will also be coached on the application of Python packages like Pandas, Scipy and Numpy for data modeling and analysis. Towards the end of this Machine Learning training using Python, you will be taught about integrating Python into a Hadoop environment. As part of this KnowledgeHut training, you will also get a downloadable e-book to strengthen your basics.
The KnowledgeHut Advantage
A global leader in e-learning, KnowledgeHut has helped students in over 70 countries. Our courses are delivered using cutting edge technology that combines easy accessibility and live classroom environments. The Machine Learning using Python course in Ahmedabad is available to you at a very reasonable price and is recommended for professionals who assume roles like professional software developers, programmers, web masters, scientists, analysts, entrepreneurs and others looking to learn about Python and its usage in data analysis.