Machine Learning Course

What is Machine Learning?

Machine learning (ML) is a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

ML Short Syllabus

Sr.No Topic Duration
1 Introduction and Demonstration 1
2 Python Basics 4
3 Introduction of python, sklearn, numpy, tensorflow, keras(-rl) and supporting library 1
4 Supervise Learning, Classification, Naïve Bayes and Getting Idea of Feature Selection 2
5 Naïve Bayes hands on Digit Recognizing with MNIS 1
6 Label encoding, binarization, rescale data, standardize data, Normalize data 2
7 Understanding Unsupervised learning KNN 2
8 Hands on NLP ft-idf Generator, WordNet, Sentiment Analysis 2
9 Text Classification using KNN and Naïve Bayes 2
10 AIML: AIML Chat Bot, Linux Tools: Speak, Transact 2
11 Understanding Apriori algorithm 1
12 Neural Network, Types of NN, The Perceptron, 2
13 Writing first NN using Python 1
14 The Backpropagation Algorithm, Activation Function, Cost function 2

Syllabus

Sr.No Topic Duration
1 Introduction and Demonstration 1
2 Python Basics 5
3 Introduction of python, sklearn, numpy, tensorflow, keras(-rl) and supporting library 1
4 Supervise Learning, Classification, Naïve Bayes and getting Idea of feature Selection 2
5 Linear Algebra: Scalars, Vectors and Matrices, Probability Distribution 3
6 Naïve Bayes hands on Digit recognize MNIS 2
7 Label encoding, binarization, rescale data, standardize data, Normalize data 2
8 Linear Regression: Cost Function; Gradient Decent 4
9 Understanding Unsupervised learning KNN 2
10 Hands on NLP ft-idf Generator, WordNet 3
11 Sentiment Analysis using Naïve Bayes 2
12 Text Classification using KNN and Naïve Bayes 2
13 AIML: AIML Chat Bot, Linux Tools: Speak, Transact 2
14 Understanding Apriori algorithm 1
15 Understanding PCA 3
16 Reinforcement: Introduction HMM, Q-Learning, DQL 4
17 Neural Network, Types of NN, The Perceptron, 2
18 Writing first NN using Python 1
19 The Backpropagation Algorithm 2
20 Activation Function, Cost function 3
21 Writing Image classifier using NN 3

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