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Artificial Intelligence & Machine Learning

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Overview
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    Cloud & GPU Labs
    60+ Hours of Cloud & GPU Labs for hands-on practice, highest in the industry.
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    Online Lectures
    30+ Hours of Online Live Lectures by industry experts.
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    Hands-on Tools
    Get hands-on experience with ML tools like Scikit Library, TensorFlow, Jupyter and Keras.
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    Free Python Course
    Free course on Python Programming for Data Science worth Rs 8000 with the course.
  • manipal overview icon
    Live Meet-Ups
    Get the chance to network with AI experts and enthusiasts through meetups.
manipal overview icon
Cloud & GPU Labs
60+ Hours of Cloud & GPU Labs for hands-on practice, highest in the industry.
manipal overview icon
Online Lectures
30+ Hours of Online Live Lectures by industry experts.
manipal overview icon
Hands-on Tools
Get hands-on experience with ML tools like Scikit Library, TensorFlow, Jupyter and Keras.
manipal overview icon
Free Python Course
Free course on Python Programming for Data Science worth Rs 8000 with the course.
manipal overview icon
Live Meet-Ups
Get the chance to network with AI experts and enthusiasts through meetups.
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Course Curriculum
1.1 Introduction to AI
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Sophia
Alpha Go and the rebirth of AI
Sneak peek into the future
Current trends in AI

1.2 Applications of AI
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Enterprise Applications of AI-Industries
Consumer Applications - Gaming, Home Automation

1.3 AI, ML and DL
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Understanding Artificial intelligence, Machine learning and Deep learning.
Use case driven comparison of AI, ML and DL

2.1 Introduction to ML
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What is ML, Need for ML, classification of ML algorithms- supervised and unsupervised learning.
Types of ML algorithms

2.2 ML Techniques
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Classification and Regression – understanding classification and regression techniques with case studies

2.3 ML with Scikit-learn
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Introduction to Scikit- learn package in python.
Implementing ML algorithms in python using Scikit-learn

3.1 ML Algorithms
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Linear, Logistic regression; Decision trees
Support Vector Machines. Python hand on using Scikit-learn

3.2 Training and Deploying models
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Creating training models using ML algorithms and deploying the models,
Understand how to deploy models after training

3.3 Text Analytics
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Basics of text processing, lexical processing, syntax and semantics of text processing,
other problems in text analytics

3.4 Natural Language Processing
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Introduction and applications of NLP.
Learning the business use cases of Natural language processing. Statistical NLP and text similarity,
syntax and parsing techniques, text summarization techniques, semantics and generation

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