• Python- overview
• Python – Environment setup
• Python- Basic syntax
• Python-variable types

• NumPy
• pandas
• matplotlib
• seaborn
• scikit learn

• Tableau (visualization software)
• MS Excel

• SQL

• AnaConda
• pycharm

• jupyter

• Django
• Google cloud

• AWS

• Artificial Neural Network(ANN)
• Convolutional Neural Network(CNN)
• Recurrent Neural Network(RNN)

• Trasfer Learning
• AutoEncoders

• Techniques
• Image classification
• object detection
• semantic segmentation
• object tracking

• instance segmentation
• computer vision algorithms/model
• CNN
• AlexNet, ResNet, Inception, VGG16
• R-CNN, Fast R-CNN, Faster R-CNN, Masked R-CNN, R-FCN

• Techniques
• Named Entity Recognition(NER)
• Tokenization
• Stemming and Lemmatization
• Bag of Words
• Natural language generation
• Sentiment Analysis
• Sentence Segmentation

• NLP algorithms/model
• RNN
• BERT
• GRU
• ELMo
• GLoMo
• ULMFiT
• OpenAI Transformer

Topic 1: Introduction to the course and to machine learning

Topic 2: The Perception and Adaline machine learning models

Topic 3: Logistic regression model, regularization. Multiclass classification

Topic 4: Support Vector Machines and Kernel Machines.

Topic 5: Decision Trees and Decision Tree Pruning

Topic 6: Bayesian Learning

Topic 7: Ensemble Methods

Topic 8: Regression

Topic 9: Clustering and Nonparametric Models Including PCA. Dimensionality Reduction

Topic 10: Presentation of Final Projects