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Working in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore: Master the advanced concepts, methodologies, and use cases of machine learning. Build ML applications for analytics, NLP and computer vision domains. Solve the most common problems in building machine learning models.

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Unlimited Duration

Last Updated

January 6, 2021

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Certification

Course Description

Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This course is your key to solving any kind of ML problem you might come across in your job. You’ll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you’ll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples. The course includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, you'll also learn to make more timely and accurate predictions. In addition, you'll deal with more advanced use cases, such as building a gaming bot, building an extractive summarization tool for medical documents, and you'll also tackle the problems faced while building an ML model. By the end of this course, you'll be able to fine-tune your models as per your needs to deliver maximum productivity.

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Course Curriculum

    • Credit Risk Modeling 00:00:00
    • Introducing the problem statement 00:00:00
    • Understanding the dataset 00:00:00
    • Feature engineering for the baseline model 00:00:00
    • Selecting machine learning algorithms 00:00:00
    • Training the baseline model 00:00:00
    • Understanding the testing matrix 00:00:00
    • Testing the baseline model 00:00:00
    • Problems with the existing approach 00:00:00
    • Optimizing the existing approach 00:00:00
    • Implementing the revised approach 00:00:00
    • Best approach 00:00:00
    • Stock Market Price Prediction 00:00:00
    • Introducing the problem statement 00:00:00
    • Collecting the dataset 00:00:00
    • Understanding the dataset 00:00:00
    • Data preprocessing and data analysis 00:00:00
    • Feature engineering 00:00:00
    • Selecting the Machine Learning algorithm 00:00:00
    • Training the baseline model 00:00:00
    • Understanding the testing matrix 00:00:00
    • Testing the baseline model 00:00:00
    • Exploring problems with the existing approach 00:00:00
    • Understanding the revised approach 00:00:00
    • Implementing the revised approach 00:00:00
    • The best approach 00:00:00
    • Customer Analytics 00:00:00
    • Introducing customer segmentation 00:00:00
    • Understanding the datasets 00:00:00
    • Building the baseline approach 00:00:00
    • Building the revised approach 00:00:00
    • The best approach 00:00:00
    • Customer segmentation for various domains 00:00:00
    • Recommendation Systems for E-Commerce 00:00:00
    • Introducing the problem statement 00:00:00
    • Understanding the datasets 00:00:00
    • Building the baseline approach 00:00:00
    • Building the revised approach 00:00:00
    • The best approach 00:00:00
    • Sentiment Analysis 00:00:00
    • Introducing problem statements 00:00:00
    • Understanding the dataset 00:00:00
    • Building the training and testing datasets for the baseline model 00:00:00
    • Feature engineering for the baseline model 00:00:00
    • Selecting the machine learning algorithm 00:00:00
    • Training the baseline model 00:00:00
    • Understanding the testing matrix 00:00:00
    • Testing the baseline model 00:00:00
    • Problem with the existing approach 00:00:00
    • How to optimize the existing approach 00:00:00
    • Implementing the revised approach 00:00:00
    • The best approach 00:00:00
    • Job Recommendation Engine 00:00:00
    • Introducing the problem statement 00:00:00
    • Understanding the datasets 00:00:00
    • Building the baseline approach 00:00:00
    • The best approach 00:00:00
    • Text Summarization 00:00:00
    • Understanding the basics of summarization 00:00:00
    • Introducing the problem statement 00:00:00
    • Understanding datasets 00:00:00
    • Building the baseline approach 00:00:00
    • Building the revised approach 00:00:00
    • The best approach 00:00:00
    • Developing Chatbots 00:00:00
    • Introducing the problem statement 00:00:00
    • Understanding datasets 00:00:00
    • Building the basic version of a chatbot 00:00:00
    • Implementing the rule-based chatbot 00:00:00
    • Testing the rule-based chatbot 00:00:00
    • Problems with the existing approach 00:00:00
    • Implementing the revised approach 00:00:00
    • Testing the revised approach 00:00:00
    • Problems with the revised approach 00:00:00
    • The best approach 00:00:00
    • Discussing the hybrid approach 00:00:00
    • Building a Real-Time Object Recognition App 00:00:00
    • Introducing the problem statement 00:00:00
    • Understanding the dataset 00:00:00
    • Transfer Learning 00:00:00
    • Setting up the coding environment 00:00:00
    • Features engineering for the baseline model 00:00:00
    • Selecting the machine learning algorithm 00:00:00
    • Building the baseline model 00:00:00
    • Understanding the testing metrics 00:00:00
    • Testing the baseline model 00:00:00
    • Problem with existing approach 00:00:00
    • How to optimize the existing approach 00:00:00
    • Implementing the revised approach 00:00:00
    • The best approach 00:00:00
    • Face Recognition and Face Emotion Recognition 00:00:00
    • Introducing the problem statement 00:00:00
    • Setting up the coding environment 00:00:00
    • Understanding the concepts of face recognition 00:00:00
    • Approaches for implementing face recognition 00:00:00
    • Understanding the dataset for face emotion recognition 00:00:00
    • Understanding the concepts of face emotion recognition 00:00:00
    • Building the face emotion recognition model 00:00:00
    • Understanding the testing matrix 00:00:00
    • Testing the model 00:00:00
    • Problems with the existing approach 00:00:00
    • How to optimize the existing approach 00:00:00
    • The best approach 00:00:00
    • Building Gaming Bot 00:00:00
    • Introducing the problem statement 00:00:00
    • Setting up the coding environment 00:00:00
    • Understanding Reinforcement Learning (RL) 00:00:00
    • Basic Atari gaming bot 00:00:00
    • Implementing the basic version of the gaming bot 00:00:00
    • Building the Space Invaders gaming bot 00:00:00
    • Implementing the Space Invaders gaming bot 00:00:00
    • Building the Pong gaming bot 00:00:00
    • Implementing the Pong gaming bot 00:00:00
    • Just for fun – implementing the Flappy Bird gaming bot 00:00:00

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