Data Science Specialization by E&ICT, IIT Roorkee for $159 | Expires in

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Learn Machine Learning and Deep Learning with Certification

Learn Python, NumPy, Pandas, Scikit-learn, Regression, Clustering, Classification, SVM, Random Forests, Decision Trees, Dimensionality Reduction, TensorFlow, Convolutional & Recurrent Neural Networks, Autoencoders, Reinforcement Learning From Industry Experts

200 Ratings       525 learners

  100+ hours training

  90 days of Lab

  24x7 Support

  12 Projects


About the Specialization

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:

  • Better research and algorithms
  • Better computing resources
  • Distributed computing infrastructures
  • Availablity of Big Data

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

  • You would like to build a robot which can recognize faces or change the path after discovering obstacles on the path.
  • Or maybe you would like to unearth hidden gems (like predicting next year revenue or fraudulent transactions or building a recommendation engine etc) in your company's tons of data(logs, financial records, HR reports or e-commerce transactions reports).

See you in the specialization and happy learning!

Key Features

3 courses

1. Python for Machine Learning
2. Machine Learning
3. Deep Learning

Projects & Lab

Apply the skills you learn on a distributed cluster to solve real-world problems.

Certificate

Highlight your new skills on your resume or LinkedIn.

Best-in-class Support

24×7 support and forum access to answer all your queries throughout your learning journey.
Enrollment
SELF-PACED LEARNING
Machine Learning Specialization

90 days lab

Machine Learning Specialization

180 days lab

119 399
Learning Path
Download Course Syllabus

Course 1

Python for Machine Learning

You can choose to take this course only. Learn More

1. Introduction to Linux

2. Introduction to Python

3. Hands-on using Jupyter on CloudxLab

4. Overview of Linear Algebra

5. Introduction to NumPy & Pandas

6. Quizzes, gamified assessments & projects

Course 2

Machine Learning

You can choose to take this course only. Learn More

1. Introduction to Statistics

Statistical Inference, Types of Variables, Probability Distribution, Normality, Measures of Central Tendencies, Normal Distribution


2. Machine Learning Applications & Landscape

Introduction to Machine Learning, Machine Learning Application, Introduction to AI, Different types of Machine Learning - Supervised, Unsupervised, Reinforcement


3. Building end-to-end Machine Learning Project

Machine Learning Projects Checklist, Frame the problem and look at the big picture, Get the data, Explore the data to gain insights, Prepare the data for Machine Learning algorithms, Explore many different models and short-list the best ones, Fine-tune model, Present the solution, Launch, monitor, and maintain the system


4. Classifications

Training a Binary classification, Performance Measures, Confusion Matrix, Precision and Recall, Precision/Recall Tradeoff, The ROC Curve, Multiclass Classification, Multilabel Classification, Multioutput Classification


5. Training Models

Linear Regression, Gradient Descent, Polynomial Regression, Learning Curves, Regularized Linear Models, Logistic Regression


6. Support Vector Machines

Linear SVM Classification, Nonlinear SVM Classification, SVM Regression


7. Decision Trees

Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Gini Impurity or Entropy, Regularization Hyperparameters, Regression, Instability


8. Ensemble Learning and Random Forests

Voting Classifiers, Bagging and Pasting, Random Patches and Random Subspaces, Random Forests, Boosting, Stacking


9. Dimensionality Reduction

The Curse of Dimensionality, Main Approaches for Dimensionality Reduction, PCA, Kernel PCA, LLE, Other Dimensionality Reduction Techniques


10. Quizzes, gamified assessments & projects

Course 3

Deep Learning

You can choose to take this course only. Learn More

1. Introduction to Deep Learning

Deep Learning Applications, Artificial Neural Network, TensorFlow Demo, Deep Learning Frameworks


2. Up and Running with TensorFlow

Installation, Creating Your First Graph and Running It in a Session, Managing Graphs, Lifecycle of a Node Value, Linear Regression with TensorFlow, Implementing Gradient Descent, Feeding Data to the Training Algorithm, Saving and Restoring Models, Visualizing the Graph and Training Curves Using TensorBoard, Name Scopes, Modularity, Sharing Variables


3. Introduction to Artificial Neural Networks

From Biological to Artificial Neurons, Training an MLP with TensorFlow’s High-Level API, Training a DNN Using Plain TensorFlow, Fine-Tuning Neural Network Hyperparameters


4. Training Deep Neural Nets

Vanishing / Exploding Gradients Problems, Reusing Pretrained Layers, Faster Optimizers, Avoiding Overfitting Through Regularization, Practical Guidelines


5. Convolutional Neural Networks

The Architecture of the Visual Cortex, Convolutional Layer, Pooling Layer, CNN Architectures


6. Recurrent Neural Networks

Recurrent Neurons, Basic RNNs in TensorFlow, Training RNNs, Deep RNNs, LSTM Cell, GRU Cell, Natural Language Processing


7. Autoencoders

Efficient Data Representations, Performing PCA with an Undercomplete Linear Autoencoder, Stacked Autoencoders, Unsupervised Pretraining Using Stacked Autoencoders, Denoising Autoencoders, Sparse Autoencoders, Variational Autoencoders


8. Reinforcement Learning

Learning to Optimize Rewards, Policy Search, Introduction to OpenAI Gym, Neural Network Policies, Evaluating Actions: The Credit Assignment Problem, Policy Gradients, Markov Decision Processes, Temporal Difference Learning and Q-Learning, Learning to Play Ms. Pac-Man Using Deep Q-Learning


9. Quizzes, gamified assessments & projects

Projects

Projects

1. Analyze your mailbox

Download all the emails in your inbox using GYB command line tool. Then analyze your emails using Numpy and Pandas and churn it to come up with various interesting insights.


2. Predict the median housing prices in California

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.


3. Classify handwritten digits in MNIST dataset

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.


4. Noise removal from the images

Build a model that takes a noisy image as an input and outputs the clean image.


5. Predict the class of flower in IRIS dataset

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.


6. 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, you build a model to predict which passengers survived the tragedy.


7. Predict bikes rental demand

Build a model to predict the bikes demand given the past data.


8. Build a spam classifier

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.


9. Build cats classifier using neural network

In this project, you will build a basic neural network to classify if a given image is of cat or not.


10. Classify large images using Inception v3

Download images of various animals and then download the latest pretrained Inception v3 model. Run the model to classify downloaded images and display the top five predictions for each image, along with the estimated probability.


11. Classify clothes using TensorFlow

Build a model to classify clothes into various categories in Fashion MNIST dataset.


12. Predict the hourly rain gauge total

This is a time series prediction task: you are given snapshots of polarimetric radar values and asked to predict the hourly rain gauge total.

Certificate

Certificate

Earn your certificate

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.


Differentiate yourself

The knowledge you have gained from working on projects, videos, quizzes, hands-on assessments and case studies gives you a competitive edge.


Share your achievement

Highlight your new skills on your resume, LinkedIn, Facebook and Twitter. Tell your friends and colleagues about it.

 Course Certificate Sample
Course Creators
Sandeep Giri

Sandeep Giri

Founder at CloudxLab
Past: Amazon, InMobi, D.E.Shaw
Course Developer
Abhinav Singh

Abhinav Singh

Co-Founder at CloudxLab
Past: Byjus
Course Developer
 Jatin Shah

Jatin Shah

Ex-LinkedIn, Yahoo, Yale CS Ph.D.
IIT-B
Course Advisor

Reviews

(4.9 out of 5)
...

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.

...

Machine learning courses in especially the Artificial Intelligence for the manager course is excellent in CloudxLab. I have attended some of the course and able to understand as Sandeep Giri sir has taught AI course from scratch and related to our data to day life…

He even takes free sessions to helps students and provides career guidance.

His courses are worthy and even just by watching YouTube video anyone can easily crack the AI interview.

...

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!

...

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.

...

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…

...

I found the ML&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.

...

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.

...

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.

...

I have been using CloudxLab for Machine Learning and based on experience I can say that they have done a fabulous job in training and certification process which makes the user so interactive with faculty and software intuitive.

FAQ

In Self-paced learning, you will get,

  • Lifetime access to the self-paced course including videos, assessments, quizzes, and projects
  • Recordings of the previous batch of instructor-led training
  • 24x7 support using the discussion forum

You need to complete at least 60% of the topics from the course. You also need to complete project 1 and 2 from python, any 3 projects from Machine Learning and any 2 projects from Deep Learning. All the above requirements need to be met within 240 days from the course enrollment date to be eligible for the certificate.

Since it is comprised of 3 different courses, we will provide you three certificates for each course separately. Then after finishing the whole course, you will get ML specialization certificate.

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.

Please mail your required projects at reachus@cloudxlab.com. After submitting the projects, our course experts will review it and forward your details to EICT, IIT Roorkee. EICT will be issuing your certificate, it may take 3-7 days.

After completing the specialization you will have a sound understanding of Machine Learning and Deep Learning. You can apply for Machine Learning Engineer, Deep Learning Engineer, and Data Scientist jobs.

You will be certified only with what you have finished. For example, if you leave Deep Learning, you will be given 2 certificates for Python and Machine Learning.

Faculty includes Assistant Professors, Associate Professors, Professors and Teachers of public and private schools or colleges.

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.

Students and Faculties need to submit their college ID and Adhar card number.

You can check https://youtu.be/dXCx4anEcgU for watching the Course Preview.

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.
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Have more questions? Please contact us at reachus@cloudxlab.com