Machine Learning (ML) and Artificial Intelligence (AI) are transforming the workplace. It refers to the process of developing self-learning algorithms by way of simple data input. The goal of machine learning is to increase accuracy. It does this by optimizing the execution of a job from knowledge gained by operating through linked datasets. Any addition to the accuracy of a particular use offers up a world of advantages, especially with industry.
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IIT Kanpur was the first institute in India to start a Computer Science Department.
IIT Kanpur has a rich base of alumni in this space who have made a remarkable impact around the world (Dr. Arvind Krishnan, CEO IBM, Dr. Rajeev Motwani, Google mentor, Dr. Narayan Murthy, Founder Infosys, Mr. Amit Agarwal, CTO Amazon to name few)
While education is impacted, some of the institutions continue to do well as they have realized the importance of practice. There is no substitution to practice. While other jobs are impacted due to COVID 19 coding jobs are still in demand.
To solve the problem of teaching introductory programming to Engineering students, Dr. Amey Karkare, Computer Science Department at IIT Kanpur has developed software - Prutor, that is being used at IIT Bombay, IIT Goa, IIT Kanpur, IISC Bangalore, IISER Bhopal and EICT Academy IIT Kanpur to teach programming to more than 40,000+ students.
We believe in the practical and industry-based approach of teaching at Prutor.ai
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Self-Paced Learning With Faculty Support and Virtual Lab
Self-Paced Learning With Faculty Support and Virtual Lab
|1. Welcome to Machine Learning|
|1. Introduction To Machine Learning||2. History and Evolution||3. Artificial Intelligence Evolution||4. Find out where Machine Learning is applied in Technology and Science.|
|2. Machine Learning Categories|
|1. Supervised Learning||2. Unsupervised Learning|
|3. Machine Learning Python Packages|
|1. Data Analysis Packages||2. NumPy||3. SciPy||4. Matplotlib||5. Pandas||6. Slkearn|
|4. Supervised Learning|
|1. Regression||2. Classification||3. Generalization, Overfitting, and Underfitting|
|1. Understand how continuous supervised learning is different from discrete learning.||2. Code a Linear Regression in Python with scikit-learn.||3. Understand different error metrics such as SSE, and R Squared in the context of Linear Regressions.|
|7. Supervised Machine Learning Algorithms|
|1. k-Nearest Neighbor||2. Linear models||3. Naive Bayes Classifiers||4. Decision trees||5. Support Vector Machines|
|8. Unsupervised Learning and Preprocessing|
|1. Challenges in unsupervised learning||2. Preprocessing and Scaling||3. Applying data transformations||4. Scaling training and test data the same way|
|9. Dimensionality Reduction and Feature Extraction|
|1. Principal Component Analysis (PCA)|
|10. Introduction to Deep Learning|
|1. A revolution in Artificial Intelligence||2. Limitations of Machine Learning||3. What is Deep Learning?||4. Advantage of Deep Learning over Machine learning|
|11. Introduction To Neural Networks with TensorFlow|
|1. How Deep Learning Works?||2. Activation Functions||3. Training a Perceptron||4. TensorFlow code-basics||5. Tensorflow data types||6. Tensorflow methods|
|7. Introduction to Neural Networks||8. Neural Network Architecture||9. Linear Regression example revisited||10. The Neuron||11. Neural Network Layers||12. The MNIST Dataset|
|13. Coding MNIST NN|
|12. Introduction to Convolutional Neural Networks (CNN) with TensorFlow|
|1. Understand the limitations of a Single Perceptron||2. Deepening the network||3. Convolutional Neural Networks||4. ConvNet Architecture||5. Overfitting and Regularization||6. Max Pooling and ReLU activations|
|7. Dropout||8. Strides and Zero Padding||9. Coding Deep ConvNets demo||10. Visualizing NN using Tensorflow||11. Tensorboard|
|13. Keras API|
|1. How to compose Models in Keras||2. Sequential Composition||3. Functional Composition||4. Predefined Neural Network Layers|
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"Overall Very Good. More focus is given to practical exposures of subject. Coding part that is required is nicely taught here. All the topics are taught from the basics to advanced level so that every branch of engineering student can understand the topics easily. Last but not the least, faculties clear your every small doubt of students and are always ready to help every students in every possible way. So overall this is a good platform where we learn machine learning like subjects easily without any hesitation."
"Totally satisfied with the course. Everything was nice and upto mark. Great course and great mentors."
"This is one of the best course i have ever done.I'm going to recommend this course to my those friends who are interested in AI and ML"
"Faculties are awesome. They teachs every little thing very smoothly. It is easy to understand big problems through your resources."
"The syllabus coverage was prettey good and the faculty was also very good."
"All the algorithms explained in very simple way."
"It was awesome experience."
"ML is a vast field and I learn that there are myriad scopes in this field. Learning new concepts and working upon techniques to build models was really interesting. The session was informative and a lot of concepts were cleared. Hands on experience was also gained. Had a productive and wonderful time!"