Course Curriculum
Python Basics & Supervised Learning+
- Python and its modules, Python Basics, Functions and Packages
- Working with Data Structures, Arrays, Vectors & Data Frames
- Jupyter Notebook – Installation & Functions
- Pandas, NumPy, Matplotlib, Seaborn
- Linear Regression, Multiple Variable Linear Regression
- Logistic Regression, Naive Bayes Classifiers
- k-NN Classification, Support Vector Machine
Ensemble Techniques & Unsupervised Learning+
- Ensemble Techniques – Decision Trees, Random Forests
- K-means Clustering, Hierarchical Clustering, Dimension Reduction – PCA
- Featurization, Model Selection & Tuning, Feature Engineering
- Model performance measures, Regularizing Linear models, ML pipeline
- Grid search CV, Randomized search CV, K-fold Cross Validation
- Projects: Customer churn, Breast cancer prediction, Travel insurance prediction
Neural Networks & Deep Learning+
- Introduction to Neural Networks and Deep Learning
- Perceptron, Activation and Loss Functions, Gradient Descent
- Batch Normalization, TensorFlow & Keras for Neural Networks
- Hyper Parameter Tuning
Computer Vision+
- Introduction to CNNs, Convolution, Pooling, Padding
- Forward Propagation & Backpropagation for CNNs
- CNN architectures – AlexNet, VGGNet, InceptionNet, ResNet
- Transfer Learning, Object Detection – YOLO, R-CNN, SSD
- Semantic Segmentation – U-Net, Face Recognition using Siamese Networks
NLP & Advanced Language Models+
- Introduction to NLP – Stop Words, Tokenization, Stemming, Lemmatization
- Bag of Words, TF-IDF, POS Tagging, Named Entity Recognition
- RNNs, Vanishing & Exploding Gradients, LSTMs, GRUs
- Time Series Analysis, LSTMs with Attention Mechanism
- Transformers, BERT, XLNet
Reinforcement Learning+
- Introduction to Reinforcement Learning (RL), RL Framework
- Component of RL Framework, Examples of RL Systems
- Types of RL Systems, Q-learning