DATA SCIENCE COURSE TRAINING IN HYDERABAD

About LearnAi.co.in

LearnAI.co.in is a premier training institute committed to providing comprehensive data science courses suitable for all skill levels. Our tailored programs cater to beginners, offering foundational knowledge in a user-friendly and engaging format. For intermediate learners, we delve into advanced topics, ensuring a seamless transition to higher proficiency. Expert-level courses are designed to challenge and refine the skills of seasoned professionals, keeping pace with the latest industry trends. With a focus on hands-on experience and real-world applications, LearnAI.co.in guarantees a dynamic learning environment that empowers individuals to excel in the ever-evolving field of data science. Join us to unlock the full potential of your data science journey!

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 Data Science?

Data science is the art of extracting valuable insights and knowledge from data. It involves using techniques from statistics, mathematics, and computer science to analyze and interpret complex information, helping businesses make informed decisions based on data-driven evidence.

Why to learn Data Science?

Learning data science is a strategic career move, as it opens doors to a plethora of high-demand roles in various industries. With businesses increasingly relying on data-driven decision-making, acquiring data science skills ensures not only job security but also positions you for substantial career growth and advancement opportunities.

Who can Pursue Data Science?

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

Enroll to Course Now

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

Data Scientist
Data Analyst
Machine Learning Engineer
Business Intelligence Analyst
Data Engineer
Quantitative Analyst
AI Research Scientist
Database Administrator
Statistician
IoT Analyst

Components of Data Science

Data Collection Systems
Data Storage and Retrieval
Data Cleaning and Preprocessing
Exploratory Data Analysis (EDA)
Statistical Analysis
Machine Learning Algorithms
Data Visualization Tools
Model Deployment
Performance Monitoring and Optimization
Collaboration and Communication Platforms