Introduction to Deep Learning |Artificial Neural Networks ANN (keras)| AI - Part 30

Day 30:

50 Free online session on Artificial Inteligence /Python /Machine learning / Deep learning /Reinforcement Learning session Part 1 to Part 50
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Date and time : Nov 13th 2020 to Jan 02nd 2021
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Dec 16th 2020:
Day 30:Introduction to Deep Learning on AI - Part 30 |Artificial Neural Networks ANN (keras)

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Day 1: Basics of Artificial Inteligence /Descriptive Statistics
Day 2-Inferential Statistics
Day 3:Probability Distribution
Day 4:Python-Setting up Environment Data Types,Variables,Oparators
Day 5:Python Control Structures *if clause *Loop *Jump Statements
Day 6:Python *Sequence Data Types *String Manipulation
DAY 7:Python *List*Tuple
Day 8:Python *Sets *Dictionary
DAY 9:Python Functions *User defined *Build in *Lambda *Recursion functions
DAY 10:Python *iterators and genertors *Exception Handling *try except final *File reading writing
Day 11:Python *class and objects *types of variables *types of methods *constructor
Day 12:Python *inheritance*polymorphism*Method overloading*Method overriding *operator overloading
Day 13:Python *Modules*some usefull modules *Data Base connectivity
Day 14:Python *Numpy *Matplotlib*Seaborn *Pandas
Day 15:Machine Learning *Introduction to Machine Learning *Types of Machine Learning *Data Preparation
Day 16:Machine Learning *Simple Linear Regression *Multiple Linear Regression
Day 17:Machine Learning - Part 17 | *Polynomial Regression
Day 18:Machine Learning - Part 18 | *Logistic Regression
Day 19:Machine Learning Part 19 | *KNearestKnieghbhors
Day 20:Machine Learning Part 20 | *Decision Tree Classifier
Day 21:Machine Learning Part 21 | *Random Forest Classifier
Day 22:Machine Learning Part 22 | *Naive Bayes Thearome
Day 23:Machine Learning Part 23 | Support Vector Machine(SVM)
Day 24:Machine Learning Part 24 | K-Means Clustering | DBSCAN
Day 25:Machine Learning Part 25 |Mean Shift |Hierarchical Model
Day 26:Machine Learning Part 26 | Mean Shift |Association Rule | Apriori |Eclat
Day 27:Machine Learning Part 27 | Dimensionality Reduction | PCA
Day 28:Machine Learning Part 28 | Linear Discriminant Analysis(LDA) |Kernel PCA
Day 29:Machine Learning Part 29 | Model Selection | Boosting
Day 30:Introduction to Deep Learning on AI - Part 30 |Artificial Neural Networks ANN (keras)
Day 31:Deep Learning on AI - Part 31 | Convolutional Neural Networks (keras)
Day 32:Deep Learning on AI - Part 32 | Recurrent Neural Networks RNN (keras)
Day 33:Deep Learning on AI - Part 33 | Tensorflow | Artificial Neural Networks(Tensorflow)
Day 34:Deep Learning on AI - Part 34 | Convolutional Neural Networks(Tensorflow)
Day 35:Deep Learning on AI- Part 35 | Recurrent Neural Networks(Tensor flow)
Day 36:Deep Learning on AI - Part 36 | Auto Encoders
Day 37:Deep Learning on AI - Part 37 | Generative Adversarial Networks(GAN) -Project
Day 38:Deep Learning on AI - Part 38 | Transfer Learning
Day 39:Object detection Open CV - Part 39 | Deep Learning Project
Day 40:Deep Learning on AI - Part 40 | Recommender Systems (Project)
Day 41:Natural Language Processing - Part 41
Day 42:Natural Language Processing Project RASA - Part 42
Day 43:Reinforcement Learning on AI- Part 43 |Upper confidence Bound | Thomson Sampling
Day 44:Reinforcement Learning on AI Markov Decision Process- Part 44
Day 45:Q-Learning |Reinforcement Learning on AI- Part 45
Day 46:Gaming Project -Deep Convolutional Q Learning Part 46
Day 47:Reinforcement Learning on AI Part 47 | A3C
Day 48:Reinforcement Learning on AI Twin Delayed DDPG - Part 48
Day 49:SARSA - Part 49 Reinforcement Learning
Day 50:ARIMA -Reinforcement Learning - Part 50