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Learn Python, NumPy, Pandas, Scikit-learn, Regression, Clustering, Classification, SVM, Random Forests, Decision Trees, Dimensionality Reduction From Industry Experts
The Electronics & ICT Academy program is sponsored by the Ministry of Electronics and Information Technology, Govt. of India.
The E&ICT Academy IIT Roorkee conducts short courses/FDPs in the emerging areas to enrich & upgrade subject knowledge and technical skills benefiting faculty, working professionals and Govt. employees.
The trained beneficiaries are expected to create a cascading effect, transforming the competencies and standards in the parent institutes/organizations.
E & ICT Academy IIT Roorkee supported by Ministry of Electronics and Information Technology (MeitY) with CloudxLab as industry partner, is conducting a training program in Machine Learning Specialization.
The E&ICT courses lay special emphasis on hands-on learning with participation from industry experts. These programs also enable the participants and institutes to build industry connects, upgrade lab facilities and create opportunities for collaboration.
E&ICT courses are at par with QIP for recognition/credits.
As of now the E&ICT Academy, IIT Roorkee has conducted 91 courses and trained over 5,000 beneficiaries.
For more details, please visit the E&ICT Academy (IIT Roorkee) official website here: https://eict.iitr.ac.in/
Have you ever wondered how self-driving cars are running on roads or how Netflix recommends the movies which you may like or how Amazon recommends you products or how Google search gives you such an accurate results or how speech recognition in your smartphone works or how the world champion was beaten at the game of Go?
Machine learning is behind these innovations. In the recent times, it has been proven that machine learning and deep learning approach to solving a problem gives far better accuracy than other approaches. This has led to a Tsunami in the area of Machine Learning.
Most of the domains that were considered specializations are now being merged into Machine Learning. This has happened because of the following:
Every domain of computing such as data analysis, software engineering, and artificial intelligence is going to be impacted by Machine Learning. Therefore, every engineer, researcher, manager or scientist would be expected to know Machine Learning.
So naturally, you are excited about Machine learning and would love to dive into it. This specialization is designed for those who want to gain hands-on experience in solving real-life problems using machine learning and deep learning. After finishing this specialization, you will find creative ways to apply your learnings to your work. For example
See you in the specialization and happy learning!
Training a Binary classification, Performance Measures, Confusion Matrix, Precision and Recall, Precision/Recall Tradeoff, The ROC Curve, Multiclass Classification, Multilabel Classification, Multioutput Classification
Linear Regression, Gradient Descent, Polynomial Regression, Learning Curves, Regularized Linear Models, Logistic Regression
Linear SVM Classification, Nonlinear SVM Classification, SVM Regression
Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Gini Impurity or Entropy, Regularization Hyperparameters, Regression, Instability
Voting Classifiers, Bagging and Pasting, Random Patches and Random Subspaces, Random Forests, Boosting, Stacking
The Curse of Dimensionality, Main Approaches for Dimensionality Reduction, PCA, Kernel PCA, LLE, Other Dimensionality Reduction Techniques
We start Machine Learning course with this end-to-end project. Learn various data manipulation, visualization and cleaning techniques using various libraries of Python like Pandas, Scikit-Learn and Matplotlib.
The MNIST dataset is considered as "Hello World!" of Machine Learning. Write your first classification logic. Starting with Binary Classification learn Multiclass, Multilabel, Multi-output classification and different error analysis techniques.
Build a model which takes noisy image as an input and outputs the clean image.
IRIS dataset contains 3 classes of 50 instances each, where each class refers to a type of iris plant. The three classes in this dataset are Setosa, Versicolor, and Verginica. Learn Decision Trees, CART algorithm and Ensemble method. Then use Random Forest classifier to make predictions.
The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. In this project, you build a model to predict which passengers survived the tragedy.
Build a model to predict the bikes demand given the past data.
Build a model to classify email as spam or ham. First, download examples of spam and ham from Apache SpamAssassin’s public datasets and then train a model to classify email.
Our course is exhaustive and the certificate rewarded by us is proof that you have taken a big leap in Machine Learning and Deep Learning.
The knowledge you have gained from working on projects, videos, quizzes, hands-on assessments and case studies gives you a competitive edge.
Highlight your new skills on your resume, LinkedIn, Facebook and Twitter. Tell your friends and colleagues about it.
Complete at least 60% of the topics of the course along with any 3 mandatory(non optional) projects. All the above requirements need to be met within 90 days from the course enrollment date to be eligible for the certificate.
It is a self-paced course. You will get access to videos, quizzes, hands-on assessments and projects. If you have any doubts during your learning journey, you can post it on the discussion forum. Our experts and community will assist you over there.
Please note that you will not get a certificate from EICT academy, IIT Roorkee if your deadline to earn the EICT academy, IIT Roorkee certificate is over. You have two options in such case-
Please note though course access is valid for the lifetime, to earn the EICT academy, IIT Roorkee certificate, you are required to finish the formalities within the stipulated deadlines. You can renew the lab anytime here
Have more questions? Please contact us at firstname.lastname@example.org