Machine Learning Certification Training using Python



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Data Science

Course Curriculum

Learning Objective: Understand how to analyze large and unstructured data with different tools.

* What is Data Science?
* What does Data Science involve?
* The Era of Data Science
* Business Intelligence VS Data Science
* The Life cycle of Data Science
* Tools of Data Science
* Introduction to Python

Learning Objective: Learn about how to extract data, arrange it into structured data, and represent the data in a graphical format.

* Data Analysis Pipeline
* What is Data Extraction
* Types of Data
* Raw and Processed Data
* Data Wrangling
* Exploratory Data Analysis
* Visualization of Data

Learning Objective: Learn about the concept of machine learning and its type.

* Python Revision, including Numpy, Pandas, scikit learn, Matplotlib
* What is Machine Learning?
* Machine Learning Use-Cases
* Machine Learning Process Flow
* Machine Learning Categories
* Linear regression
* Gradient descent

Learning Objective: Understand what supervised learning techniques are and study about the logistic regression.

* 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?

Learning Objective: Learn the impact of the dimensions within the data and perform PCA analysis.

* Introduction to Dimensionality
* Why Dimensionality Reduction
* Factor Analysis
* Scaling dimensional model

Learning Objective: Learn about supervised learning techniques and its implementation.

* 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
* How to Implement of Support Vector Machine for Classification

Learning Objective: Learn about the various types of clustering that can be used to analyze the data.

* What is Clustering & its Use Cases?
* What is K-means Clustering?
* How does K-means algorithm work?
* How to do optimal clustering
* What is C-means Clustering?
* What is Hierarchical Clustering?
* How Hierarchical Clustering works?

Learning Objective: Define association rules and learn about the backend engines, developed using Python.

* What are Association Rules?
* Association Rule Parameters
* Calculating Association Rule Parameters
* Recommendation Engines
* How do Recommendation Engines work?
* Collaborative Filtering
* Content-Based Filtering

Learning Objective: Explain the concept of reinforcement learning and understand Markov’s decision process.

* 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
* Values

Learning Objective: Explain the time series analysis and understand ARIMA modeling.

* What is Time Series Analysis?
* Importance of TSA
* Components of TSA
* White Noise
* AR model
* MA model
* ARMA model
* ARIMA model
* Stationarity

Learning Objective: Discuss the model selection, boosting, its needs, and explain the working of boosting algorithm.

* What is Model Selection?
* Need of Model Selection
* Cross –Validation
* What is Boosting?
* How Boosting Algorithms work?
* Types of Boosting Algorithms
* Adaptive Boosting

Course Description

Our Machine Learning Certification using Python is designed to help candidates understand the concept of machine learning. This training provides you with a deep understanding of machine learning, its importance, and its implementation in the Python programming language. Opting for machine learning certification online will teach you about reinforcement learning, which in fact, is an important aspect of Artificial Intelligence. Moreover, you will be able to work on real-life scenarios using machine learning algorithms.

Machine learning certification using Python includes data science techniques that enable computers to learn desired behaviour and possess the necessary skills like data pre- processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms, including regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.

After completing the machine learning certification, you will be able to:
? Derive insights into the 'Roles' played by a Machine Learning professional
? Data analysis using python Automation
? Proper description for Machine Learning
? Work with real-time data
? Study tools and techniques for predictive modeling
? Discuss Machine Learning algorithms, implementation, and applications
? Validate Machine Learning algorithms
? Explain Time Series
Who should opt for this Machine Learning Certification Training using Python?
? Developers meaning to be a ‘Machine Learning Engineer'
? Analytics Managers who are leading a team of technical analysts
? Business Analysts who want to know Machine Learning (ML) Techniques
? Information Architects who want to realize expertise in Predictive Analytics

? 'Python' professionals who want to style automatic predictive models


Frequently Asked Questions (FAQs):

 There are no prerequisites for the Course, and you only require a good Internet Connection with a Laptop to undertake this Course.

With this Course, you will gain an essential Certification in Machine Learning, which will help you level up your familiarity with various concepts and know-how of Machine Learning Algorithms in Python.

Absolute beginners to Programming can take up this Course to improve their understanding of Machine Learning or gain the Machine Learning Certification and Training using Python.

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