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 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:
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
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