Module 1 – Introduction To Machine Learning:

Basic Concept:

• Train,Test & Validation Distribution
• ML Strategy
• Computation Graph
• Evaluation Metric
• Human Level Performance

Module 2 – Machine Learning:

Supervised:

• Linear Regression
• Logistic Regression
• Gradient Descent
• Decision Tree
• Random Forest
• Bagging & Boosting
• KNN

Unsupervised

• K-Means
• Hierarchical Clustering

Module 3 – Python Programming:

Python

• Basic Programming
• NLP Libraries
• Open CV

Module 4 – Mathematics Foundation:

Basic Statistics

• Sampling & Sampling Statistics
• Hypothesis Testing

Calculus

• Derivatives
• Optimization

Linear Algebra

• Function
• Scalar-Vector-Matrix
• Vector Operation

Probability

• Space
• Probability
• Distribution

Module 5 – Data Processing:

Environment

• Scikit Learn
• NLTK
• Spacy & Gen sim
• Open CV
• Tensorflow
• Keras

Text Processing

• Representation
• Data Cleaning
• Data Preprocessing
• Similarity

Image Processing

• Image
• Image Transformation
• Filters
• Noise Removal
• Correlation & Convolution
• Edge Detection
• Non Maximum Suppression & Hysteresis
• Fourier Domain
• Video Processing

Speech Data Analytics

Feature Extraction

• Image Feature
• Descriptors

Object Detection

• Detection  & Classification