MACHINE LEARNING

Learn Machine Learning

At RisingStar Technologies, our Artificial Intelligence Course is a comprehensive and practical training program that equips you with the skills and knowledge necessary to develop data-driven business models. This course covers various aspects of AI, including Data Science, Deep Learning, Python Programming, and Python Libraries for Artificial Intelligence. Through hands-on projects, you will gain exposure to advanced concepts such as TensorFlow, Deep Neural Networks, R Programming, SAS Advanced Analytics, and AWS. By mastering the programming skills and concepts in this domain, you will be well-prepared to excel in the field of Artificial Intelligence and kickstart a successful career. Join our Artificial Intelligence training at RisingStar Technologies and unlock your potential in this dynamic and rapidly evolving field.

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RisingStar Technologies

Courses Include

30 Hours of Session

Flexible Schedule

Real Time Project Use

10 Hours of Lab

One-on-One Doubt Session

Certificate Oriented Curriculum

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Machine Learning Content

Below, you will find comprehensive details of the Machine Learning Training course, covering all the essential aspects you will be exposed to throughout the program.

Machine Learning Course

Enroll in the Machine Learning training program offered by RisingStar Technologies to acquire the necessary skills and expertise for a successful career as a Machine Learning Engineer. This comprehensive course provides a deep understanding of supervised and unsupervised learning, algorithms, support vector machines, and more. Through practical industry use cases, you will gain real-world experience and be well-prepared to pass the Machine Learning Certification Exam. Let RisingStar Technologies guide you towards becoming a proficient Machine Learning professional.

Python Programming

Introduction to Python

Anaconda Navigator Download & Installation

Anaconda Navigator Download
Anaconda Navigator Installation
Create environment and download libraries
Introduction to jupyter notebook
Python Object & Data Structure

Numbers:

Basic Arithmatic
Variable assignment

String

String
String indexing and slicing
String properties

List

Lists
List method
List comprehsions

Dictionary

Construct Dict
Dict Methods

Tuples
Sets and Booleans

Python Statement

Intro python state , if elif else

introduction to python statements
if,elif and else statement part 1
if,elif and else statement part 2

For loop

Introduction to for loops
For loop examples

While loops , Useful Operators

while loop
break , cont , pass
useful operator part 1
useful operator part 2

Methods and Function

lambda expression , nested statemet

map and filter
lambda expression
nested statement part 1
nested statement part 2
methods
functions part 1
functions part 2
list compressions

- OOPs
  - OOPs basics
  - OOPs inheritance
  - OOPs polymorphism
- - Python Libraries

Numpy

- - Creating array
  - Using arrary and scalers
  - Indexing arrays
  - Array transposition
  - Universal array functions
  - Array processing

Pandas

- - Series

Index objects

- - Reindex
  - Drop entry
  - Selecting entries
  - Data alignment
  - Rank and sort
  - Missing Data

Matplotlib
Seaborn

- Seaborn categorical plots part 1
- Seaborn categorical plots part 2
- Seaborn categorical plots part 3
- Seaborn distribution plot part 1
- Seaborn distribution plot part 2
- Seaborn regression plot
- Seaborn style and color

What is Machine Learning

Machine Learning application
Machine learning Process
How to become a machine learning engineer
Pattern Recognition

Artificial intelligence

What is AI
What is deep learning
AI tools and Models

Graphica Models

What is PGM
MRF

Stats and Prob

Introduction to statistic
Statistical analysis process
Kurtosis
Co-relation matrix
Statistics practical

Data Pre-Processing

Data preparation process
Type of Data
Feature Scaling

Machine Learning Types

Logistic reg

Logistic regression Data preprocessing
Feture scalling _ model making
Visualize training results

Multi and poly regression

Multiple linear regression
Polynomial regression part 1
Polynomial regression part 2

Simple linear regression

Regression data preprocessing
Regression model making
Supervised learning introduction
Linear regression
LMS algorithm
Objective and application of linear regression
Multiple and polynomial regression
Logistic regression
Objective and model eval
Intro unsupervised learning
Semi-supervised and important consideration

KNN

KNN practical

KNN Data preprocessing
KNN modeling
Visualize KNN model

Decision Tree

dt classifier

Decison tree Classifier
Visualize the DT

dt regressor

step 1 making DT regression
step 2 DT Structure

RF practical

RF practical part 1
RF practical part 2

Decision tree regression
Decision Tree Classification
Random Forest

SVM

SVM introduction
SVM Mathematics
Non-linear SVM

Clustering Analysis

Clustering Introduction
K-means theory
k-means Mathematical
kmeans practical part 1
kmeans practical part 2

ANN

Rise of artificial neuron
Introduction to ANN
Perceptron
Activation Functions
Feed forward Neural networks
Cost function in neural network
Back-propagation neural network
Introduction to CNN
CNN arch and Convolutional layer
Pooling layer and fully connected layer
RNN introduction
Recurrent neurons
Various configuration of RNNs
Training recurrent neural network
Tensorflow Introduction
Computationsl Graph
ANN practical
- Intro to ANN
- Part 1 data preprocessing
- Part 2 building ANN
- Part 3 testing ANN
CNN Practical
- Import libraries
- Part 1 data preprocessing
- Part 2 Building the CNN
- Part 3 Training CNN
- Part 4 making a single prediction
RNN practical
- Part 1 data preprocessing
- Part 2 Building RNN
- Part 3 testing the model

NLP

Basics of NLP
NLP application
Feature extraction
Gaussian NB
NLP practicals
- NLP practical part 1
- NLP practical part 2
- NLP practical part 3

Reinforcement Learning

Rf intro
Case study overview
Bellman eq
MDP
Q-learning
Dynamic programming
Q-learning practical
- Q-learning practical part 1
- Q-learning practical part 2
- Q-learning practical part 3

RisingStar Technologies

Benefits of Learning Machine Learning in RisingStar Technologies

By enrolling in the Machine Learning course at RisingStar Technologies, you can enjoy numerous benefits that will enhance your career prospects in the field. Gain in-depth knowledge of machine learning concepts, algorithms, and techniques through comprehensive training modules. Acquire hands-on experience through real-world industry projects, enabling you to apply your skills to practical scenarios. Benefit from expert guidance and mentorship from experienced instructors who will help you navigate the complexities of machine learning. Prepare for the Machine Learning Certification Exam and enhance your credibility as a qualified machine learning professional. Open doors to a wide range of career opportunities in industries such as finance, healthcare, technology, and more. Stay ahead in this rapidly growing field and unlock your potential with the Machine Learning course at RisingStar Technologies.

Machine Learning Training In India

July 1st
Mon-Fri (21 Days)

Timing 07:00 AM to 09:00 AM

August 1st
Mon-Fri (21 Days)

Timing 07:00 AM to 09:00 AM

September 1st
Mon-Fri (21 Days)

Timing 07:00 AM to 09:00 AM