Specialization Course in
Machine Learning & Deep Learning

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

(525 Learners)

  100+ hours training

  180 days of Lab

  24x7 Support

  12 Projects

Looking for a scholarship? Apply now for the scholarship test here »

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

Learn from industry experts. 100+ hours of live instructor-led training

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.

1:1 Mentoring

Subscribe to 1:1 mentoring sessions and get guidance from industry leaders and professionals.

Best-in-class Support

24×7 support and forum access to answer all your queries throughout your learning journey.
Enrollment

Instructor-led Trainings

6 Oct
Fri, Sat
(20 weeks)
9:30 p.m. - 12:30 a.m. America/New_York

180 days lab
849 898
Learning Path

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 which takes 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

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

Reviews

40 reviews
(4.9 out of 5)
...

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.

FAQ

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.

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 never lose any lecture. You can view the recorded session of the class in your LMS.

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.

Enrollment into this instructor-led course entails 180 days of free access to Cloudxlab, depending on date of enrollment.

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