Introduction Of Machine Learnig (Basic)

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Day 1 | 8 hrs

Mathematics for Machine Learning 
1. Introduction to Calculus
2. Introduction to Linear Algebra
3. Introduction to Probability & Statistics

Machine Learning Basics
1. Fundamentals of Machine Learning
2. Machine Learning Practical Applications: Classification, Regression etc.
3. Supervised Learning
4. Semi Supervised Learning
5.Unsupervised Learning
6. Neural Networks and Deep Learning

Practical Machine Learning
1. Machine Learning Frameworks : Python, Google COLAB, TensorFlow, Pytorch, Keras
2. Code Classification and Time series Prediction Models
3. Implementing a Neural Network from Scratch


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