Machine Learning COURSE TRAINING IN HYDERABAD

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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
Machine Learning	Dilsukhnagar	Hybrid	1st	Wednesday
Machine Learning	Dilsukhnagar	Hybrid	2nd	Monday
Machine Learning	Dilsukhnagar	Hybrid	3rd	Wednesday
Machine Learning	Dilsukhnagar	Hybrid	4th	Monday

What is Machine Learning?

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

Why to learn Machine Learning?

Machine Learning 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 Machine Learning

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

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

Clustering Models

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

Job Roles

Data Analyst
Machine Learning Engineer

Components of Machine Learning

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