Machine learning is a tool for turning information into knowledge. Machine learning is the concept that a computer program can learn and adapt to new data without human interference. Machine learning is of great help for businesses. They use it to solve complex issues, define patterns, get new insights, and take intelligent actions based on the data provided. Studying machine learning opens up a world of opportunities to develop cutting-edge machine learning technologies across diverse verticals–such as cybersecurity, image recognition, pharmacy, or face recognition–with every company seeking to implement AI in their market. With several machine learning firms on the verge of hiring skilled ML engineers, the brain behind business intelligence is becoming that. Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think. AI is accomplished by studying how human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems. Today, the amount of data that is generated, by both humans and machines, far outpaces humans’ ability to absorb, interpret, and make complex decisions based on that data.

- Module1: Python
- Module2: Machine Learning
- Machine Learning
- About R programming
- Installing RStudio
- Working in RStudio/dataset/about packages
- Fundamentals of R programming
- Preliminaries of Statistics
- Data types
- Measure of Central Tendency & Dispersion
- Graphical Representation
- Probability Theory/Normal Distribution
- Skewness/Kurtosis/Standard Normal Distribution (Z-score)/Q-Q plot
- Binomial & Poisson Distribution/Scatter Diagram/Correlation Analysis
- Confidence Interval/Z-distribution & t-distribution
- Case Study
- Regression Analysis: Linear Regression/Multiple Linear Regression/Logistic Regression
- Probability/confusion matrix
- Case Study
- Clustering/Euclidean Distance
- Hierarchical clustering
- K-means clustering
- Case Study
- Manhattan Distance/K-Medoids/DBSCAN/CLARA
- Decision Tree/Random Forest

- Dimension Reduction/PCAText Mining
- Word cloud/NLP/Emotion Mining
- Web Extraction
- Case study
- KNN/SVM/Neural Network
- Naive Bayes Classifier/Poisson Regression/Negative Binomial Regression
- Lasso & Ridge Regression/Zero-Inflated Regression
- Multinomial Regression/Survival Analysis
- Getting started with Python & Packages
- Basic statistics/Graphical representation
- Linear Regression/Logistic Regression
- Hierarchical Clustering
- k-means clustering
- kNN/Naïve Bayes/SVM
- Decision Tree/Neural Network
- Text Mining/Data Extraction
- Module3: Artificial Intelligence
- Introduction to Artificial Intelligence
- History & Applications
- Statistic essentials
- Pre-program preparation
- Python programming
- Introduction to machine learning
- Supervised algorithm for AI problem
- Introduction to OPEN-CV

- Library and packages to use for image and video processing
- Concept of image segmentation in A
- Introduction to NLP(nltk)
- Text mining using NLP
- Text mining processes tokenize
- Text mining processes (stemming/lemmanization/pos/syntax/chunking etc)
- Use of ML and NLP for solving problem
- How Deep Learning Works?
- How Neural Network works?
- Understanding various components of Neural Networks
- Keras
- Theano
- Tensor Flow -Installation
- Introduction to Keras
- Theano
- Tensor Flow
- Functionalities of Tensor flow
- Single and multi-layer perceptron
- Pros and cons of single and multi-layer perceptron
- Training using back propagation
- Convolution Neural Network
- Convolution Neural Network
- Use of OPEN-CV with deep learning
- Applications object segmentation/object recognition
- Applications sentimental analysis/chatbot