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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