Learn Python, NumPy, Scipy, Pandas, Jupyter, Scikit-learn, Regression, Clustering, Classification, Support Vector Machines, Random Forests, Decision Trees and Dimensionality Reduction From Industry Experts
The "idea" of machine learning has come in and out of favor a few times through history, each time leaving people thinking it was merely a fad. It is really only very recently that we've been able to put much of machine learning to any decent test. Nowadays, you can spin up and rent a $100,000 GPU cluster for a few dollars an hour, the stuff of PhD student dreams just 10 years ago. Machine learning got another up tick in the mid 2000's and has been on the rise ever since, also benefitting in general from Moore's Law. Beyond this, there are ample resources out there to help you on your journey with machine learning, like this tutorial. You can just do a Google search on the topic and find more than enough information to keep you busy for a while.
There is massive growth in the machine learning space, and job opportunities are skyrocketing, making this the perfect time to launch your career in this space.
Instructor-led Trainings
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.
We understand that you might need course material for a longer duration to make most out of your subscription. You will get lifetime access (Till the company is operational) to the course material so that you can refer to the course material anytime.
In online instructor-led training, Sandeep Giri along with his team of experts will train you with a group of our course learners for 25+ hours over online conferencing software like Zoom. Classes will happen every Saturday and Sunday
We offer mentoring sessions to our learners with industry leaders and professionals so you can get 1 on 1 help with any questions you may have, whether your questions are technical, job-related or anything else.
It is a paid service and exclusively available to learners enrolling for the course. We will provide more information on subscription information for the same after the course is launched.
At the end, of course, you will work on a real-time project. You will receive a problem statement along with a data-set to work on CloudxLab. Once you are done with the project (it will be reviewed by an expert), you will be awarded a certificate which you can share on LinkedIn.
Enrollment into self-paced course entails 90 days of free access to CloudxLab. Enrollment into instructor-led course entails 90 days of free access to Cloudxlab, depending on date of enrollment.
Yes. Java is generally required for understanding MapReduce. MapReduce is a programming paradigm for writing your logic in the form of Mapper and reducer functions. We provide a self-paced course on Java for free. As soon as you signup, it would be available in your account section.
Course requires a good internet (1 Mbps or more) and a browser to watch videos and do hands-on the lab. We've configured all the tools in the lab so that you can focus on learning and practicing in a real-world cluster.
For self-paced course, we provide 100/% fees refund if the request is raised within 7 days from enrollment date. Thereafter, no refund is provided.
For instructor-led course, we provide 100% refund if not more than 1 live session has been conducted -- and we provide 50% refund if 2-4 live sessions have been conducted. If 5 or more live sessions have been conducted, then no refund will be provided.
Yes, you can renew your subscription anytime. Please choose your desired plan for the lab and make payment to renew your subscription.
Yes, you can upgrade from self-paced course to instructor-led course by paying the differential amount. Please contact us at reachus@cloudxlab.com for the same
Have more questions? Please contact us at reachus@cloudxlab.com
Must have for practicing and perfecting hadoop. To setup in PC you need to have a very high end configuration and setup will be pseudo node setup.. For better understanding I recomend CloudxLab
They are great. They take care of all the Big Data technologies (Hadoop, Spark, Hive, etc.) so you do not have to worry about installing and running them correclty on your pc. Plus, they have a fantastic customer support. Even when I have had problems debugging my own programs, they have answered me with the correct solution in a few hours, and all of this for a more than reasonable price. I personally recommend it to everyone :)
I have been using CloudxLab for last 3 months for learning Hadoop and Spark, and I can vouch for it.
It’s a platform where you can learn from the tutorial videos and then practice in the lab they provide on cloud. The study materials are well-planned and I would be lying if I say its not great.
The video lectures explains the technical stuffs in very simple ways which makes it easier to grasp the concepts. Also, the customer service is great.
So, thumbs up for the team associated with CloudxLab.
To conclude my views, I would just say that, if you are willing to learn Big Data related stuff, I strongly recommend CloudxLab.
I think I can give some points on this . Am using cloudxlab for more than an year… my intention is for continuous learning.
For Students and technology change professionals :
In General Big data hadoop, (a) you can learn on your personal PC, but for that the minimum configuration of 12 GB Ram with good processing speed, still when you execute jobs it will take more time for processing jobs as it will be acting as single node.(b) If you try to install each and every components, it will take hell a lot of admin work , and some thing happens , you have to invest lot of time for debugging.
The main advantage of using cloudxlab,
a) Get 6 node production cluster with all installed components, just getting user and password, you can start working on it.
b) You have almost all the access.
c) Good amount of components installed.
d) You can play around with each of them with 5gb of test data.
e) So far I didnt experience any down time.
f) You can Practice in your college lab, on free time.
g) Good email support on technical perspective.
h) They have couple of test data, I use my own.
i) vi and nano editor supported.
j) Some of the components which I remember are HDFS,MapReduce2, YARN, Tez, ZooKeeper,Falcon,Storm, Kafka,Spark,Jupyter Notebook, Hive,HBase, Pig, Sqoop, Oozie, Flume,Accumulo,Ambari.
I have been using CloudxLab for sometime and based on my usage experience I can say that they have done a fabulous job.
The first problem anyone faces while learning Big Data technologies is running the VMs on his/her laptop. VMs require a good amount of dedicated RAM and so most of the times we end up spending in hardware upgrade. But even after an upgrade the requirement of a cluster is never met. The examples we try alaways runs on a single node setup.
To try this on a production like cluster setup we have something like AWS, but there is a good amount of cost involved in that. Also, they keep the credit card details with them which I feel not everyone feels safe to share.
And this is where I feel CloudxLab seems to be a better bet.Their pricing is very much competitive compared to the other offerings and also it doesn’t require any specific hardware requirement. Any desktop/laptop with any configuration which has connectivity to net is good for getting started.
No need to do any setups.Their clusters are fully loaded with all the latest Big Data packages.You can access them from anywhere.
The only thing you need to concentrate is on your learning :)
Hope this helps to anyone who is looking for an option beyond VMs.