Learn Python, NumPy, Scipy, Pandas, Jupyter, Scikit-learn, Regression, Clustering, Classification, Support Vector Machines, Random Forests, Decision Trees and Dimensionality Reduction From Industry Experts
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!
Self-Paced Learning
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.
In Self-paced learning, you will get,
In Instructor-led training, you will get
This course is for engineers, product managers and anyone who has a basic know-how of any programming language. We will cover foundations of linear algebra, calculus and statistical inference where ever required so that you can learn the concepts effectively.
It will take around 7 weeks with 6-8 hours of effort per week.
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.
The first session is completely free and further, it is a paid service available to learners enrolling in the course.
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.
At CloudxLab, we have always believed in quality education must be affordable for everyone so that we can help learners achieving career goals and build innovative products.
Please follow this post for more details on the financial aid.
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.