AI Level 6- Advanced Machine Learning Algorithms

Time Series Algorithms

1
Introduction and Application
2
Parameters Affecting Time Series Algorithm: Trend, Seasonality, Cyclicity, Irregularity
3
Stationary and Non-stationary Data
4
Rolling Statistics, ADCF Test
5
ARIMA Model

Recommender System

1
Introduction and Application
2
Market Basket Analysis.
3
Apriori Algorithm: Support Count, Confidence, Antecedent, Consequent, Lift or Interest or Correlation, Conviction, Leverage or Piatetsky-Shapiro.
4
Case Study

OpenCV

1
Introduction and Application
2
imread, imshow, imsize
3
Motion Detection Using OpenCV
4
Optical Character Recognition Using Pytessaract
5
Gensim, Textblob, Polarity Package

Natural Language Processing

1
Introduction and Application
2
Steps Required Natural Language Processing
3
Tokenization
4
Stopwords
5
Stemming
6
Parts of Speech Tagging(Bag of Words, Unigram, Bigram, CBOW, Skipgram)
7
Wordcloud
8
Difference Between Count Vectorizer and TFID Vectorizer
9
Packages Used in NLP: NLTK, Stopwords, Wordcloud, PorterStemmer, CountVectorizer, TfidfVectorizer

Naive Bayes Algorithms

1
Introduction and Applications
2
Bernoulli Naive Bayes
3
Multinomial Naive Bayes
4
Gaussian Naive Bayes
5
Case Study 1: Binary Classification(Sentiment Analysis)
6
Case Study 2: Multiple Classification(News Classification)

Artificial Neural Network(ANN)

1
Introduction and Application
2
Terms Related to Neural Networks
3
Feed forward Neural Network
4
Back Propogation
5
Explanation of ANN With an Example
6
Activation Functions: Sigmoid Functions, Relu Functions
7
Case Study

Convolution Neural Networks(CNN)

1
Introduction and Application
2
Convolution Layers
3
Max Pooling Layers
4
Flattening
5
Fully Connected Layer
6
Case Study

Recurrent Neural Network(RNN)

1
Introduction and Application
2
Diving Deep into RNN
3
Case Study

Long and Short Time Memory (LSTM)

1
Introduction and Application
2
Diving Deep into LSTM
3
Case Study

Final Assessment

Live Project

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AI Level 6- Advanced Machine Learning Algorithms
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