Introduction Of Machine Learnig (Advance)

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2 Days | 18 hrs

Mathematics for Machine Learning
1. Introduction to Calculus, Linear Algebra, Probability, Statistics and Random Variables
2. Introduction to Python, numpy, pandas etc.
3. Python assignments.

Machine Learning Basics
1. Fundamentals of Machine Learning
2. Application in Machine Learning- Classification, Regression etc.
3. Introduction to the theory and algorithms of :
→ Supervised Learning
→ Semi Supervised Learning
→ Unsupervised Learning
→ Graphical Models
→ Predictive Modelling

Practical Machine Learning-Frameworks
1. Machine Learning Frameworks :
→ Google COLAB
→ Sci-kit-learn
→ TensorFlow
→ PyTorch
→ Keras
2. Industry grade tools and technologies for implementing a practical machine learning project
3. Assignments – classification, regression and mathematical models

Neural Network and Deep Learning
1. Introdution to theory of neural networks and stochastic gradient descent
2. Deep neural networks, CNN, RNN, Auto Encoders
3. LSTM, GAN, Capsule networks

Practical Machine Learning – Your own models
1. Implementing a Neural Network from scratch
2. Implementing a Deep Neural Network (CNN, RNN, GAN) in Tensorflow/PyTorch
3. Developing AI projects and practical caveats in implementing machine learning models
4. Organizing Machine Learning Projects

Research and Applications
1. Applications of AI in Industry and Academia
2. Computer Vision
3. Natural Language Processing
4. What’s hot in AI research – a discussion on state of the art and recent trends in AI

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Machine Learning Course Advance Overview

This Machine Learning online course offers an in-depth overview of Machine Learning topics including working with real-time data, developing algorithms using supervised & unsupervised learning, regression, classification, and time series modeling. Learn how to use Python in this Machine Learning certification training to draw predictions from data.

Day 1 | 9 hrs

Mathematics for Machine Learning

Machine Learning Basics

Practical Machine Learning-Frameworks

Day 2 | 9 hrs

Neural Network and Deep Learning

Practical Machine Learning – Your own models

Research and Applications


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