This Data Science Certification Program is a self-paced online course. This gives you complete freedom about your schedule and convenience.
This course has over 220+ hours of learning. This consists of 5 courses (Big Data with Hadoop, Big Data with Spark, Python, Machine Learning, and Deep Learning).
Additionally, this course comes with our exclusive lab access to gain the much needed hands-on experience to solve the real-world problems.
Upon successfully completing the course, you will get the certificate from CloudxLab which you can use for progressing in your career and finding better opportunities.
Program Highlights
Certificate of Completion by CloudxLab
220+ Hours of Self-Paced Learning
Work on about 26+ projects to get hands-on experience
CloudxLab is a team of developers, engineers, and educators passionate about building innovative products to make learning fun, engaging, and for life. We are a highly motivated team who build fresh and lasting learning experiences for our users. Powered by our innovation processes, we provide a gamified environment where learning is fun and constructive. From creative design to intuitive apps we create a seamless learning experience for our users. We upskill engineers in deep tech - make them employable & future-ready.
9.5 Evaluating Actions: The Credit Assignment Problem
9.6 Policy Gradients
9.7 Markov Decision Processes
9.8 Temporal Difference Learning and Q-Learning
9.9 Deep Q-Learning Variants
9.10 The TF-Agents Library
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Projects
Project 1. Process the New York Stock Exchange data
We will see how to process the NYSE (New York Stock Exchange) data using Hive for various insights.
Project 2. Sentiment analysis
We will do sentiment analysis of "Iron Man 3" movie using Hive and visualize the sentiment data using BI tools such as Tableau
Project 3. MovieLens Project
We will analyze MovieLens data using Hive
Project 4. Spark MLlib
We will learn to generate movie recommendations using Spark MLlib
Project 5. Spark GraphX
We will derive the importance of various handles at Twitter using Spark GraphX
Project 6. Churn the logs
We will see how to churn the logs of NASA Kennedy Space Center WWW server using Spark to find out useful business and devops metrics
Project 7. Spark application
We will understand how to write end-to-end Spark application starting from writing code on your local machine to deploying to the cluster
Project 8. Analytics Dashboard
Real-time analytics dashboard for an e-commerce company using Apache Spark, Kafka, Spark Streaming, Node.js, Socket.IO and Highcharts
Project 9. Analyze Emails
We will churn the mail activity from various individuals in an open source project development team.
Project 10. Predict bikes rental demand
We will understand how to build a model to predict the bikes demand given the past data.
Project 11. Noise removal from the images
We will build a model that takes a noisy image as an input and gives clean image as an output.
Project 12. Predict which passengers survived in the Titanic shipwreck
The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. In this project, we build a model to predict which passengers survived the tragedy.
Project 13. Build a spam classifier
We will learn how to 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.
Project 14. Build an Image Classifier in Fashion MNIST dataset
We will classify images from the Fashion MNIST dataset using scikit-learn, and Python.
Project 15. Deploy Machine Learning models to Production using Flask
We will learn how to deploy a machine learning model as a web application using the Flask framework.
Project 16. Build an Image Classifier in Fashion MNIST dataset
We will classify images from the Fashion MNIST dataset using Tensorflow 2, Matplotlib, and Python.
Project 17. Training from Scratch vs Transfer Learning
Learn how to train a neural network from scratch to classify data using TensorFlow 2, and how to use the weights of an already trained model to achieve classification to another set of data.
Project 18. Working with Custom Loss Function
Create a custom loss function in Keras with TensorFlow 2 backend.
Project 19. Image Classification with Pre-trained Keras models
Learn how to access the pre-trained models(here we get pre-trained ResNet model) from Keras of TensorFlow 2 to classify images.
Project 20. Build cats classifier using transfer learning
In this project, we will build a basic neural network to classify if a given image is of cat or not using transfer learning technique with Python and Keras.
Project 21. Mask R-CNN with OpenCV for Object Detection
We will learn how to read a pre-trained TensorFlow model for object detection using OpenCV.
Project 22. Art Generation Project
We will use TensorFlow 2 to generate an image that is an artistic blend of content image and style image using Neural Style Transfer.
Project 23. NYSE Stock Closing Price Prediction using TensorFlow 2 and Keras
Predict stock market closing prices for a firm using GRU, a state-of-art deep learning algorithm for sequential data, with Keras and Python.
Project 24. Sentiment Analysis using IMDB dataset
We will create a sentiment analysis model with the IMDB dataset using TensorFlow 2.
Project 25. Autoencoders for Fashion MNIST
We will learn how to practically implement the autoencoder, stacking an encoder and decoder using TensorFlow 2, and depict reconstructed output images by the autoencoder model using the Fashion MNIST dataset.
Project 26. Deploy Image Classification Pre-trained Keras model using Flask
We will learn how to deploy a deep learning model as a web application using the Flask framework.
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Prerequisites
This course is for engineers, product managers, and anyone who wants to learn. We will cover foundations of linear algebra, calculus and statistical inference where ever required so that you can learn the concepts effectively. There is no prerequisite or programming knowledge required.
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Data Science Specialization (lifetime course access) + 90 days Cloud Lab
“This course is suitable for everyone. Me being a product manager had not done hands-on coding since quite some time. Python was completely new to me. However, Sandeep Giri gave us a crash course to Python and then introduced us to Machine Learning. Also, the CloudxLab’s environment was very useful to just log in and start practising coding and playing with things learnt. A good mix of theory and practical exercises and specifically the sequence of starting straight away with a project and then going deeper was a very good way of teaching. I would recommend this course to all.”
Kamal Upadhyay
“This is one of the best-designed course, very informative and well paced. The killer feature of machine/deep learning coursed from CloudxLab is the live session with access to labs for hands-on practices! With that, it becomes easy following any discourse, even if one misses the live sessions(Read that as me!). Sandeep(course instructor) has loads of patience and his way of explaining things are just remarkable. I might have better comments to add here, once I learn more! Great Jobs guys!”
Dhyan Prem
“It has been a wonderful learning experience with CXL. This is one of the courses that will probably stay with me for a significant amount of time. The platform provides a unique opportunity to try hands-on simultaneously with the coursework in an almost real-life coding example. Besides, learning to use algebra, tech system and Git is a good refresher for anyone planning to start or stay in technology. The course covers the depth and breadth of ML topics. I specifically like the MNIST example and the depth to which it goes in explaining each and every line of code. Would definitely recommend the instructor-led course.”
Pratik Sonthaliya
“A very well structured instructor-led course. The instructor was very thorough, and always willing to answer questions and clarify coursework, no matter how minor. The course described the theory of machine/deep learning well, but also followed through with very thorough examples to demonstrate the practical implementations of the theory. This leads nicely into the student exercises, which served to solidify the instructor's teachings and encourage experimentation. The resources provided for students was exceptional and presented in a very user-friendly format.
My only complaint is that the course went quite overtime, but I also appreciate Sandeeps dedication to quality and ensuring that he finished teaching us everything adequately.”
James Ferris
“I have thoroughly enjoyed both the ML and DL courses from CloudXLab and will look forward to reviewing the videos/material at a later time. I’ve been to many meetups and paid sessions on ML /DL and this course beats most of them on the depth of topics and certainly breadth of topics. I’ve not taken any online courses (Andrew Ng, for example) to their conclusion, so I won’t draw a conclusion there. For an instructor-led, interactive course, I would expect to pay many times more for a class (ML and DL) such as this in the US. The instructor is easy to understand, has extensive experience, and truly cares about the student knowing the material.”
R Burns
“I found the ML and DL course very well structured with ample examples and hands on exercises. Sandeep was very patient in answering questions and he made the training sessions very interactive. I would recommend this training to all who plan to take a dive into the world of machine and deep learning.”
Shobhit Kumar
“I took both the machine learning and deep learning course at CloudXLab. I came into the first part of the course with some knowledge of machine learning but the class really helped me understand some of the topics a lot clearer. I think the best part of the class is the instructor Sandeep. He is very knowledgeable and does a really good job explaining topics that can be nebulous at times. Another favorite part of the course are the online labs. I would watch the 3hr lecture the next day, and then work on the labs. The labs reinforces the lectures with questions and coding assignments. There is also a message board and a slack channel. I preferred using slack, but I think you get a quicker response if you use the message board. As far as the deep learning portion of the course, it was all new to me but I was building CNN and RNN models using TensorFlow after each 3hr lecture. Overall, I was very pleased with the course. I am hoping that CloudxLab will put together an advanced class focusing more on deploying models to the clouds, working with pipelines, DevOps etc…”
Robert Milton
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Machine Learning Specialization Program by CloudxLab
This course is for engineers, product managers, and anyone who wants to learn. We will cover foundations of linear algebra, calculus and statistical inference where ever required so that you can learn the concepts effectively. There is no prerequisite or programming knowledge required.
If you are unhappy with the product for any reason, you can ask for a full refund up to 14 days after the start of the first live sessions. Please contact us at reachus@cloudxlab.com to request a refund within the stipulated time. We will be sorry to see you go though!
We understand that you might need course material for a longer duration to make most out of your subscription. You will get lifetime access to the course material so that you can refer to the course material anytime.
