Training in Machine Learning

In every area, machine learning and artificial intelligence are making waves. There is no better time than now to learn one of today’s most in-demand skills. C Cube Technologies is a premier Machine Learning Training Center where you may advance your profession.

Why is C Cube Technologies training in Machine Learning?

Machine learning has emerged as a key component of modern technology. As a result, studying this course from the Best Machine Learning Training Institute is critical. C Cube Technologies believes that providing students and professionals with technically adequate machine learning training will help them advance in their careers opportunities. Our instructors keep class groups to a maximum of four students per batch and emphasise hands-on training. We also provide career counselling to help you locate the right job.

Machine Learning is attracting the attention of major corporations.

All that is required are the appropriate abilities.

Machine learning positions are still in their infancy. The majority of companies want you to take initiative and be creative. Because of the great demand for this fantastic employment, skilled professionals are required. The trick is to have the appropriate talents. Then you’ll have no trouble getting in.

 

Machine Learning Course Prerequisites

Computer abilities, a basic understanding of mathematics, and a basic understanding of data science principles are all required.

Who is eligible to take Machine Learning classes?

This course will benefit the following individuals:

You could be a programmer, a mathematics graduate, or a bachelor’s degree holder in computer applications. You can enrol in a Machine Learning class. Even students with disabilities

In every industry, machine learning profiles are available. Companies may use data and machine learning to create smart goods and provide wonderful services to their customers.

Skills are required for Machine Learning jobs

If you’re a recent graduate seeking for work in machine learning, focus on developing abilities that will be valuable. Machine Learning Classroom Training can help you understand machine learning ideas. Because the roles are skill-based, engineers from other disciplines can accomplish them efficiently. As a result, machine learning positions are in high demand.

All that is required are the appropriate abilities.

Machine learning positions are still in their infancy. The majority of companies want you to take initiative and be creative. Because of the great demand for this fantastic employment, skilled professionals are required. The trick is to have the appropriate talents. Then you’ll have no trouble getting in.

Machine Learning Course Prerequisites

Computer abilities, a basic understanding of mathematics, and a basic understanding of data science principles are all required.

Who is eligible to take Machine Learning classes?

This course will benefit the following individuals:

You could be a programmer, a mathematics graduate, or a bachelor’s degree holder in computer applications. You can enrol in a Machine Learning class. All that is required are the appropriate abilities.

Machine learning positions are still in their infancy. The majority of companies want you to take initiative and be creative. Because of the great demand for this fantastic employment, skilled professionals are required. The trick is to have the appropriate talents. Then you’ll have no trouble getting in.

Machine Learning Course Prerequisites

Computer abilities, a basic understanding of mathematics, and a basic understanding of data science principles are all required.

Who is eligible to take Machine Learning classes?

This course will benefit the following individuals:

  • You could be a programmer, a mathematics graduate, or a bachelor’s degree holder in computer applications. You can enrol in a Machine Learning class.
  • This course is open to students having a master’s degree in Economics or Social Science.
  • Developers interested in pursuing a career as a machine learning engineer or data scientist
  • Managers in charge of a team of analysts
  • Business analysts who want to learn data science approaches in depth
  • Information architects interested in learning about machine learning methods
  • Analytics specialists who want to work in machine learning or artificial intelligence should apply.
  • Graduates interested in a career in data science or machine learning should apply.
  • Experienced experts that want to make the most of machine learning in their fields to gain new insights

Module 1: Introduction to Data Science

Define Data Science

Discuss the era of Data Science

Describe the Role of a Data Scientist

Illustrate the Life cycle of Data Science

List the Tools used in Data Science

State what role Big Data and Hadoop, Python, R and Machine Learning play in Data Science

Syllabus of Machine Learning Certification Training using Python Course

Module 2: Data Extraction, Wrangling, & Visualization

Data Analysis Pipeline

What is Data Extraction

Types of Data

Raw and Processed Data

Data Wrangling

Exploratory Data Analysis

Visualization of Data

Module 3: Introduction to Machine Learning with Python

Python Revision (numpy, Pandas, scikit learn, matplotlib)

What is Machine Learning?

Machine Learning Use-Cases

Machine Learning Process Flow

Machine Learning Categories

Linear regression

Gradient descent

Module 4: Supervised Learning – I

What is Classification and its use cases?

What is Decision Tree?

Algorithm for Decision Tree Induction

Creating a Perfect Decision Tree

Confusion Matrix

What is Random Forest?

Module 5: Dimensionality Reduction

Introduction to Dimensionality

Why Dimensionality Reduction

PCA

Factor Analysis

Scaling dimensional model

LDA

Module 6: Supervised Learning – II

What is Naïve Bayes?

How Naïve Bayes works?

Implementing Naïve Bayes Classifier

What is Support Vector Machine?

Illustrate how Support Vector Machine works?

Hyperparameter optimization

Grid Search vs Random Search

Implementation of Support Vector Machine for Classification

Module 7: Unsupervised Learning

What is Clustering & its Use Cases?

What is K-means Clustering?

How K-means algorithm works?

How to do optimal clustering

What is C-means Clustering?

What is Hierarchical Clustering?

How Hierarchical Clustering works?

Module 8: Association Rules Mining and Recommendation Systems

What are Association Rules?

Association Rule Parameters

Calculating Association Rule Parameters

Recommendation Engines

How Recommendation Engines work?

Collaborative Filtering

Content Based Filtering

Module 9: Reinforcement Learning

What is Reinforcement Learning

Why Reinforcement Learning

Elements of Reinforcement Learning

Exploration vs Exploitation dilemma

Epsilon Greedy Algorithm

Markov Decision Process (MDP)

Q values and V values

Q – Learning

α values

Module 10: Time Series Analysis

What is Time Series Analysis?

Importance of TSA

Components of TSA

White Noise

AR model

MA model

ARMA model

ARIMA model

Stationarity

ACF & PACF

Module 11: Model Selection and Boosting

What is Model Selection?

Need of Model Selection

Cross – Validation

What is Boosting?

How Boosting Algorithms work?

Types of Boosting Algorithms

Adaptive Boosting