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Learnai.co.in offers a comprehensive range of professional IT and Non-IT courses that are designed to cater to an aspiring group of professionals who went a tailored program on making them career ready. Our programs are driven by a constant need to be job achieving by following industry-oriented course curriculum and stimulating, taking into consideration the dynamic nature of technology with practical knowledge work on different modules, tools and are taught by world-class professionals with specific domain expertise. Simply one stop learning for all trending technologies.

Upcoming Batches

Course	Branch	Training Type	Week	Day
Data Science	Dilsukhnagar	Hybrid	1st	Wednesday
Data Science	Dilsukhnagar	Hybrid	2nd	Monday
Data Science	Dilsukhnagar	Hybrid	3rd	Wednesday
Data Science	Dilsukhnagar	Hybrid	4th	Monday

What is Artificial Intelligence?

Artificial Intelligence is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured.

Why to Artificial Intelligence?

Artificial Intelligence can be defined as a blend of mathematics, business understanding, tools, algorithms and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions.

Who can Pursue Artificial Intelligence?

Any Degree pursuing or Graduated,
 Bachelor’s, Master’s, PhD, and
 Anyone interested in course

Enroll to Course Now

Services	Essential Course Package	Mid-Level Career Booster	Mastery Career Package
Members	>20	<15	<10
Duration	3 Months	7 Months	7 Months + 1 Year
Portal
Course Completion Certificate
One to One
Resume Preparation
Mock Interviews
Job Assistance
Internship
Job Guarantee

COURSE CURRICULUM

Python

Introduction
 Installation
 Fundamental of Python
 Variables
 Comments
 Print Statement
 Operators
 Mutable Data Types
Data Types
Special Data Types
Conditions
Loops
Functions
Break, Continue and Pass Statements
String Object and working
List
Tuple
Set
Dictionaries
Map
Reduce
Filter
Classes
Objects
Inheritance
Multiple Inheritance
Modules
Error Handling

Data Visualization

Matplotlib
 Seaborn
 Plotly
 Cuflinks
 Bokesh

Libraries

Numpy
Pandas
Random
Math
Scipy
sklearn
Keras
Tensorflow
OpenCV
NLTK
Spacy
Lot more…

Tableau

Introduction to Data Visualization
Introduction to Tableau
Basics charts and dashboards
Special Char Types
Dashboard design and principles
Connections with servers
Local file access
Hands on experience with worksheet

Web Scraping

 Url
 Beautiful Soup

Data base

 SQL
 MongoDB

Statistics

Basics of Statistics
Descriptive Statistics
Inferential Statistics
Qualitative Analysis
Quantitative Analysis
Hypothesis Testing
Data Distribution
Probability Distribution
Normal Distribution
Poison Distribution
Outlier Detection
Other Statistical Fundamentals

Machine Learning

Supervised Learning
Unsupervised Learning
Semi Supervised Learning
Reinforcement Learning

Supervised Learning

 Regression Models
Classification Models

Regression Models

Introduction to Regression Models
Linear Models
Linear Regression
Multiple Regression
Ordinary Least Square Method
Non-Linear Models
Support Vector Regressor
Random Forest Regressor
Decision Tree

Evaluation Metrics for Regression Models

R-Square
 Adjusted R Square
 Mean Square Error
Root Mean Square Error
Mean Absolute Error

Classification Models

Introduction to Classification Models
 Logistics Regression
 Naïve Bayes
 Support Vector Classifier
 K-NN Classifier
 Decision Tree Classifier
 Random Forest Classifier

Evaluation Metrics for Classification Models

Accuracy
Precision
Recall
F1 Score
ROC Curve

Unsupervised Learning

Introduction to Unsupervised Learning

Clustering Models

 Introduction to Clustering Models
 K- Mean Clustering
 Hierarchical Clustering
High Dimensional Clustering

Dimension Reduction

Principal Component Analysis (PCA) Reinforcement Learning

Reinforcement Learning

Introduction to Reinforcement Learning

Featurization, Model Selection & Tuning

Feature Extraction
Model Defects & Evaluation Metrics
Model Selection and Tuning
Comparison of machine learning models

Neural Networks

Introduction to Deep Learning
Fundamental of Neural Networks
TensorFlow and Keras
Artificial Neural Networks (ANN)
Convolution Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Evaluation of Deep Learning
Neural Networks Basics
Gradient Descent
Introduction to Perceptron & Neural Networks
Batch Normalization
Activation and Loss Functions
Hyper parameter tuning
SoftMax
Deep Neural Networks
Weights initialization

Image Pre-processing

Computer Vision
Open CV
Noise Detection
Noise Reduce
Low Pass Filters
Forward Propagation
Backward Propagation
Pooling & Padding

Natural Language Processing

Introduction to NLP
 Corpus
 Natural Language Understanding (NLU)
 Natural Language Generator (NLG)
Tokenization (Word, Sentence, Blank, RegEx)
Frequency Distribution
Filtering
Stemming
Lemmatization
Stop Words
Regular Expressions
 POS Tagging
 Syntax Tree
 Chunking
 Lemmas
 Hypernyms
 Hyponyms
 Synonyms
 Antonyms
 Distance Between words
Wordnet
 Name Entity Recognition
 Bag of words or Document Matrix
 Count Vectorization
 Term Frequency
 Inverse Document Frequency
 Sentimental Analysis

Job Roles

 Business Analytics Professional
 Business Intelligence Professional
 Data Scientist
 Data Analyst
 Data Engineer
 Big Data Analysts
 HR Analytics Professionals
 Marketing Analytics Professionals

Components of Artificial Intelligence

Business Understanding
Understanding Problem Statement
 Data Mining
 Data Cleaning
 Data Analysis
Data Analytics
 Data Exploration
 Stats and Mathematics
 Feature Engineering
 Feature Selection
 Feature importance
 Modeling
 Predictive Modeling
 Deploying Model