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Home » Technology » Big Data & Data Science » Machine Learning Solutions

Course Skill Level:

Foundational to Intermediate

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:

    MLSOLUL21E09

Who should attend & recommended skills:

Those with basic Python & machine learning skills

Who should attend & recommended skills

  • This course is geared for those desiring practical, hands-on solutions in Python to overcome any problem in Machine Learning.
  • Skill-level: Foundation-level Machine Learning skills for Intermediate skilled team members. This is not a basic class.
  • Machine Learning: Basic (1-2 years’ experience)
  • Python: Basic (1-2 years’ experience)

About this course

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, overfitting 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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:
  • Mastering the advanced concepts, methodologies, and use cases of machine learning
  • Building ML applications for analytics, NLP and computer vision domains
  • Solving the most common problems in building machine learning models
  • Selecting the right algorithm to derive the best solution in ML domains
  • Performing predictive analysis efficiently using ML algorithms
  • Predicting stock prices using the stock index value
  • Performing customer analytics for an e-commerce platform
  • Building recommendation engines for various domains
  • Building NLP applications for the health domain
  • Building language generation applications using different NLP techniques
  • Building computer vision applications such as facial emotion recognition

Course breakdown / modules

  • 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

  • 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

  • Introducing customer segmentation
  • Understanding the datasets
  • Building the baseline approach
  • Building the revised approach
  • The best approach
  • Customer segmentation for various domains

  • Introducing the problem statement
  • Understanding the datasets
  • Building the baseline approach
  • Building the revised approach
  • The best approach

  • 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

  • Introducing the problem statement
  • Understanding the datasets
  • Building the baseline approach
  • Building the revised approach
  • The best approach

  • Understanding the basics of summarization
  • Introducing the problem statement
  • Understanding datasets
  • Building the baseline approach
  • Building the revised approach
  • The best approach

  • 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

  • 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

  • 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

  • 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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