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Machine Learning Solutions

  • Course Code: Data Science - Machine Learning Solutions
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for those who wants Practical, hands-on solutions in Python to overcome any problem in Machine Learning.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Machine Learning skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants Practical, hands-on solutions in Python to overcome any problem in Machine Learning. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

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 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Select the right algorithm to derive the best solution in ML domains 
  • Perform predictive analysis effciently using ML algorithms 
  • Predict stock prices using the stock index value 
  • Perform customer analytics for an e-commerce platform 
  • Build recommendation engines for various domains 
  • Build NLP applications for the health domain 
  • Build language generation applications using different NLP techniques 
  • Build computer vision applications such as facial emotion recognition 

Audience & Pre-Requisites 

This course is designed for developers wants Practical, hands-on solutions in Python to overcome any problem in Machine Learning 

Pre-Requisites:  Students should have familiar with  

  • Basics of ML  
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

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