01
Introduction
Algorithm
Flow Chart
GIT
02
STATISTICS
Introduction to Statistical Analysis
Descriptive statistics
Inferential statistics
Mean, Median, Mode
Standard deviation, Variance, Range
Outliers , Quartile range , Interquartile range
Probability
Estimation and Hypothesis
Marquee,Testing , Scatter Diagram
Central limit theorem
03
PYTHON LIBRARIES
NumPy
Pandas
Matplotlib
04
EDA
Data Wrangling and Manipulation
Descriptive statistics
Identifying Patterns and Outliers
Missing value and Outliers
Imputation techniques
Transformation techniques
Standardization
Normalization
05
MACHINE LEARNING
Introduction
Regression and classification
Linear Regression
Logistic Regression
Naive Bayes , Decision Trees , Random Forest
Support Vector Machines
K-Nearest Neighbor , Model validation
Model Evaluation , Gridsearchcv
Adaboost , Gradient Boosting
Hierarchical clustering
K-Means clustering
06
DEEP LEARNING
Introduction to deep learning
Introduction to tensorflow, Keras
Introduction to computer vision
Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
07
NATURAL LANGUAGE PROCESSING
Introduction to NLP
Spacy
ntroduction to NLP
Pipeline-transformers
Text preprocessing
Tokenization
Stemming
Lemmatization
08
PROJECT
Mini Project - Using machine learning
Main Project - Both machine learning and deep learning