Introduction To Machine Learning
- What is M.L?
- Why M.L?
- Introduction to Supervised M.L
- Introduction to Unsupervised M.L
M.L Glossary
- Bias- Variance Dichotomy
- Overfitting
- Underfitting
- Regularization
- Optimization
- Cross-Validation, Area Under Curve
- Data Split and Hyper Parameter Tuning
- Feature Engineering
- Data Preprocessing
SCIKIT LEARN
Supervised Machine learning Algorithms
- Regression
- What is Regression?
- Different Types of Regression
- Linear Regression
- Goodness of Fit
- Multiple Linear regression
- Regularization- L1 and L2.
- Real-Time Mini Project on Regression
Classification
- Logistic Regression
- K-Nearest Neighbor Algorithm
- Decision- Trees
- Random Forest
- Naive Bayes for text classification
- Real-Time Mini Project on Classification
Unsupervised Machine Learning Algorithms
Clustering
- What is Clustering?
- Types of Clustering-Hierarchical and Agglomerative
- K-Means Clustering Algorithm
- Real – Time Problem
Some Advanced M.l
- Curse of Dimensionality- PCA
- Support Vector Machines
- Bagging
- Boosting
- Gradient Boosting
- XGBoost Algorithm
- Ensemble Methods
ARTIFICIAL INTELLIGENCE
Introduction To Deep Learning
- What is Deep Learning?
- Why Deep Learning?
- How Deep Learning works?
- DrawBacks of Machine Learning
- Applications of Deep Learning
Neural Networks
- What is a neural network?
- Structure of Artificial Neuron
- Perceptrons
- Training of Perceptron
- Important Parameters of Perceptrons
- Activation Functions-sigmoid,Relu,SoftMax, Hyperbolic Fns
- Feed-forward Neural Network
- Introduction to Back-Propogation
Our Practices
Convolutional Neural Network
- Introduction to CNN
- Types of Convolution
- CNN Architecture Part-1(LeNet and AlexNet)
- CNN Architecture Part-2(VGG Net)
- CNN Architecture Part-3(Google Net)
- CNN Architecture Part-4(ResNet)
- CNN Architecture Part-5(Dense Net)
Applications of CNN
- Train Network for Image Segmentation
- Semantic Segmentation
- Hyperparameter Optimization
- Transfer Learning
- Segmentation of Brain Tumors from MRI
- Sample Code
ReCurrent Neural Network
- Introduction to RNNs
- Example- Sequence Classification
- Training RNNs- Loss BPTT
- RNN Architecture
- LSTM
- How and Why LSTM works
Implementation of NNs using TensorFlow
- Intro to tensorflow
- TensorFlow- Code Basics
- Tensorflow Graphs
- TF Regression- Example Code
- TF Classification- Example Code
- TensorBoard
- MNIST- Data Overview
- CNN MNIST code Example
- RNNs with Tensorflow API
Implementation of NNs using Keras API
Natural Language Processing
- Introduction to NLP
- Understanding Text Classification
- Bag of words Model
- TF-IDF Frequency
- NLTK python
- Tokenizing words and sentences using NLTK
- Stop Words with NLTK
- Stemming and chunking with NLTK
- Part Of Speech Tagging with NLTK
- NER with NLTK
- Lemmatization with NLTK
- Combining ML text algorithms with NLTK
- Twitter Sentiment Analysis with NLTK
Computer Vision
- Introduction to computer vision
- Applications of CV
- OpenCV- Python
- OpenCV- Installation Guide
Image Fundamentals
- How to read an Image
- How to save an image in different format
- Basic Image Operations
- Drawing shapes and writing text on image
- Image Processing
- Image Transformation
- Image Rotation
- Image Thresholding
- Image Filtering
- Gaussian Blur
- Median Blur
- Bilateral Filtering
- Feature Detection
- Canny Edge Detection
- Video Analysis
- Mini Projects:
- Real time Face Detection using webc
- ALPR- Automatic Number Plate Detection