8 Months

Course Duration




Lab Days



About the Course

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!

Program Highlights

  • Certificate of Completion by CloudxLab

  • Work on about 18+ projects to get hands-on experience

  • Timely Doubt Resolution

  • Best In Class Curriculum

  • Cloud Lab Access


What is the certificate like?

  • Why Cloudxlab?

    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 and future-ready.

Hands-on Learning

hands-on lab
  • Gamified Learning Platform

  • Auto-assessment Tests

  • No Installation Required

Course Creators

Instructor Sandeep Giri

Sandeep Giri

Founder at CloudxLab

Past: Amazon, InMobi, D.E.Shaw

Instructor Abhinav Singh

Abhinav Singh

Co-Founder at CloudxLab

Past: Byjus

Instructor Jatin

Jatin Shah

Yale CS, Ph.D. IIT-Bombay

Past: Ex-LinkedIn, Yahoo


Hours of Online Training
240 Days of Lab Access

Python for Machine Learning

1. Programming Tools and Foundational Concepts
1 Introduction to Linux
2. Python Foundations
3. Hands-on using Jupyter on CloudxLab
4. Overview of Linear Algebra
5. Introduction to NumPy and Pandas

Course on Machine Learning

1. Introduction to Statistics
1. Statistical Inference
2. Probability Distribution
3. Normality
4. Measures of Central Tendencies
5. Normal Distribution
2. Machine Learning Applications and Landscape
1. Introduction to Machine Learning
2. Machine Learning Application
3. Introduction to AI
4. Different types of Machine Learning - Supervised, Unsupervised
3. Building End-To-End Machine Learning Project
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
4. Classifications
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
5. Training Models
1. Linear Regression
2. Gradient Descent
3. Polynomial Regression
4. Learning Curves
5. Regularized Linear Models
6. Logistic Regression
6. Support Vector Machines
1. Linear SVM Classification
2. Nonlinear SVM Classification
3. SVM Regression
7. Decision Trees
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
8. Ensemble Learning and Random Forests
1. Voting Classifiers
2. Bagging and Pasting
3. Random Patches and Random Subspaces
4. Random Forests
5. Boosting and Stacking
9. Dimensionality Reduction
1. The Curse of Dimensionality
2. Main Approaches for Dimensionality Reduction
3. PCA
4. Kernel PCA
5. LLE
6. Other Dimensionality Reduction Techniques
10. Unsupervised Learning
1. What is Unsupervised Learning
2. Uses of Unsupervised Learning
3. Clustering

Course on Deep Learning

1. Introduction to Artificial Neural Networks
1.1 From Biological to Artificial Neurons
1.2 Implementing MLPs using Keras with TensorFlow Backend
1.3 Fine-Tuning Neural Network Hyperparameters
2. Training Deep Neural Networks
2.1 The Vanishing / Exploding Gradients Problems
2.2 Reusing Pretrained Layers
2.3 Faster Optimizers
2.4 Avoiding Overfitting Through Regularization
2.5 Practical Guidelines to Train Deep Neural Networks
3. Custom Models and Training with Tensorflow
3.1 A Quick Tour of TensorFlow
3.2 Customizing Models and Training Algorithms
3.3 Tensorflow Functions and Graphs
4. Loading and Preprocessing Data with Tensorflow
4.1 Introduction to the Data API
4.2 TFRecord Format
4.3 Preprocessing the Input Features
4.4 TF Transform
4.5 The TensorFlow Datasets (TDFS) Projects
5. Convolutional Neural Networks
5.1 The Architecture of the Visual Cortex
5.2 Convolutional Layer
5.3 Pooling Layer
5.4 CNN Architectures
5.5 Classification with Keras
5.6 Transfer Learning with Keras
5.7 Object Detection
5.8 YOLO
6. Recurrent Neural Networks
6.1 Recurrent Neurons and Layers
6.2 Basic RNNs in TensorFlow
6.3 Training RNNs
6.4 Deep RNNs
6.5 Forecasting a Time Series
6.6 LSTM Cell
6.7 GRU Cell
7. Natural Language Processing
7.1 Introduction to Natural Language Processing
7.2 Creating a Quiz Using TextBlob
7.3 Finding Related Posts with scikit-learn
7.4 Generating Shakespearean Text Using Character RNN
7.5 Sentiment Analysis
7.6 Encoder-Decoder Network for Neural Machine Translation
7.7 Attention Mechanisms
7.8 Recent Innovations in Language Models
8. Autoencoders and GANs
8.1 Efficient Data Representations
8.2 Performing PCA with an Under Complete Linear Autoencoder
8.3 Stacked Autoencoders
8.4 Unsupervised Pre Training Using Stacked Autoencoders
8.5 Denoising Autoencoders
8.6 Sparse Autoencoders
8.7 Variational Autoencoders
8.8 Generative Adversarial Networks
9. Reinforcement Learning
9.1 Learning to Optimize Rewards
9.2 Policy Search
9.3 Introduction to OpenAI Gym
9.4 Neural Network Policies
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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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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Frequently Asked Questions

What is your refund policy?

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 to request a refund within the stipulated time. We will be sorry to see you go though!

What do I need to fulfill to get the CloudxLab certificate for the course?

You should complete 100% of the course along with all the given projects in order to be eligible for the certificate.

Kindly note that there is no deadline for CloudxLab courses.

Do I need to install any software before starting this course?

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

What are the prerequisites and requirements for this course?

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

What is the validity of course material?

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.

Can I get a certificate for the projects completed?

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