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Certification Course on
Machine Learning Specialization

Learn Python, NumPy, Pandas, scikit-learn, Regression, Clustering, Classification, SVM, Random Forests, Decision Trees, Dimensionality Reduction, TensorFlow 2, Keras, Convolutional & Recurrent Neural Networks, Autoencoders, Reinforcement Learning From Industry Experts

200 Ratings       15525 learners

  170+ hours of online training

  240 days of Lab

  18+ Projects

  Timely Doubt Resolution


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

Cloud 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.

Projects

Work on 18+ projects to get hands-on experience.

Best-in-class Support

Timely Doubt Resolution through the Discussion Forum with the help of international community of peers
Enrollment
SELF-PACED LEARNING
Course + Lab + Certificate

240 days lab

399 557
INSTRUCTOR-LED TRAINING
STARTS FROM: 3 Sep
Fri, Sat
9:30 p.m. - 11:30 p.m. America/New_York
240 days lab
529 839
Get a callback from a Course Counselor - Click Here
Learning Path
Download Course Syllabus

Course 1

Python for Machine Learning

You can choose to take this course only. Learn More
1.1 Introduction to Linux
1.2 Introduction to Python
1.3 Hands-on using Jupyter on CloudxLab
1.4 Overview of Linear Algebra
1.5 Introduction to NumPy & Pandas

Course 2

Machine Learning

You can choose to take this course only. Learn More
1. Statistical Inference
2. Probability Distribution
3. Normality
4. Measures of Central Tendencies
5. Normal Distribution
1. Introduction to Machine Learning
2. Machine Learning Application
3. Introduction to AI
4. Different types of Machine Learning - Supervised, Unsupervised
1. Machine Learning Projects Checklist
2. Get the data
3. Launch, monitor, and maintain the system
4. Explore the data to gain insights
5. Prepare the data for Machine Learning algorithms
6. Explore many different models and short-list the best ones
7. Fine-tune model
1. Training a Binary classification
2. Multiclass,Multilabel and Multioutput Classification
3. Performance Measures
4. Confusion Matrix
5. Precision and Recall
6. Precision/Recall Tradeoff
7. The ROC Curve
1. Linear Regression
2. Gradient Descent
3. Polynomial Regression
4. Learning Curves
5. Regularized Linear Models
5. Logistic Regression
1. Linear SVM Classification
2. Nonlinear SVM Classification
3. SVM Regression
1. Training and Visualizing a Decision Tree
2. Making Predictions
3. Estimating Class Probabilities
4. The CART Training Algorithm
5. Gini Impurity or Entropy
6. Regularization Hyperparameters
7. Instability
1. Voting Classifiers
2. Bagging and Pasting
3. Random Patches and Random Subspaces
4. Random Forests
5. Boosting and Stacking
1. The Curse of Dimensionality
2. Main Approaches for Dimensionality Reduction
3. PCA
4. Kernel PCA
5. LLE
6. Other Dimensionality Reduction Techniques
1. What is Unsupervised Learning
2. Uses of Unsupervised Learning
3. Clustering

Course 3

Deep Learning

You can choose to take this course only. Learn More
1. From Biological to Artificial Neurons
2. Implementing MLPs using Keras with TensorFlow Backend
3. Fine-Tuning Neural Network Hyperparameters
1. The Vanishing / Exploding Gradients Problems
2. Reusing Pretrained Layers
3. Faster Optimizers
4. Avoiding Overfitting Through Regularization
5. Practical Guidelines to Train Deep Neural Networks
1. A Quick Tour of TensorFlow
2. Customizing Models and Training Algorithms
3. Tensorflow Functions and Graphs
1. Introduction to the Data API
2. TFRecord Format
3. Preprocessing the Input Features
4. TF Transform
5. The TensorFlow Datasets (TDFS) Projects
1. The Architecture of the Visual Cortex
2. Convolutional Layer
3. Pooling Layer
4. CNN Architectures
5. Classification with Keras
6. Transfer Learning with Keras
7. Object Detection
8. YOLO
1. Recurrent Neurons and Layers
2. Basic RNNs in TensorFlow
3. Training RNNs
4. Deep RNNs
5. Forecasting a Time Series
6. LSTM Cell
7. GRU Cell
1. Introduction to Natural Language Processing
2. Creating a Quiz Using TextBlob
3. Finding Related Posts with scikit-learn
4. Generating Shakespearean Text Using Character RNN
5. Sentiment Analysis
6. Encoder-Decoder Network for Neural Machine Translation
7. Attention Mechanisms
8. Recent Innovations in Language Models
1. Efficient Data Representations
2. Performing PCA with an Under Complete Linear Autoencoder
3. Stacked Autoencoders
4. Unsupervised Pre Training Using Stacked Autoencoders
5. Denoising Autoencoders
6. Sparse Autoencoders
7. Variational Autoencoders
8. Generative Adversarial Networks
1. Learning to Optimize Rewards
2. Policy Search
3. Introduction to OpenAI Gym
4. Neural Network Policies
5. Evaluating Actions: The Credit Assignment Problem
6. Policy Gradients
7. Markov Decision Processes
8. Temporal Difference Learning and Q-Learning
9. Deep Q-Learning Variants
10. The TF-Agents Library
Projects

Projects

1. Analyze Emails

Churn the mail activity from various individuals in an open source project development team.


2. Predict bikes rental demand

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


3. Noise removal from the images

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


4. 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.


5. 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.


6. Build an Image Classifier in Fashion MNIST dataset

Classify images from the Fashion MNIST dataset using scikit-learn, and Python.


7. Deploy Machine Learning models to Production using Flask

Learn how to deploy a machine learning model as a web application using the Flask framework.


8. Build an Image Classifier in Fashion MNIST dataset

Classify images from the Fashion MNIST dataset using Tensorflow 2, Matplotlib, and Python.


9. 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.


10. Working with Custom Loss Function

Create a custom loss function in Keras with TensorFlow 2 backend.


11. 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.


12. Build cats classifier using transfer learning

In this project, you 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.


13. Mask R-CNN with OpenCV for Object Detection

Learn how to read a pre-trained TensorFlow model for object detection using OpenCV.


14. Art Generation Project

Use TensorFlow 2 to generate an image that is an artistic blend of a content image and style image using Neural Style Transfer.


15. NYSE Stock Closing Price Prediction using TensorFlow 2 & 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.


16. Sentiment Analysis using IMDB dataset

Create a sentiment analysis model with the IMDB dataset using TensorFlow 2.


17. Autoencoders for Fashion MNIST

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.


18. Deploy Image Classification Pre-trained Keras model using Flask

Learn how to deploy a deep learning model as a web application using the Flask framework.


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.

MLS CloudxLab Certificatee
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

If you are unhappy with the product for any reason, let us know within 7 days of purchasing or upgrading your account, and we'll cancel your account and issue a full refund. Please contact us at reachus@cloudxlab.com to request a refund within the stipulated time. We will be sorry to see you go though!

You will be required to complete at least 75% of the course content and complete at least 3 of the mandatory real-world projects included in the course. The above requirement needs to be met within 180 days of enrollment in order to be eligible for the certificate from IIT Roorkee.

No, we will provide you with the access to our online lab and BootML so that you do not have to install anything on your local machine

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.

Please log in at CloudxLab.com with your Gmail Id and access your course under "My Courses".

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.

We have created a set of Guided Projects on our platform. You may complete these guided projects and earn the certificate for free. Check it out here



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