
Machine Learning Certification Training using Python
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Syllabus
Introduction to Data Science
What is Data Science?
What does Data Science involve?
Era of Data Science
Business Intelligence vs Data Science
Life cycle of Data Science
Tools of Data Science
Introduction to Python
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
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
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?
Dimensionality Reduction
Introduction to Dimensionality
Why Dimensionality Reduction
PCA
Factor Analysis
Scaling dimensional model
LDA
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
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?
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
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
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
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
Complimentary sessions on communication presentation and leadership skills.
Benefits from the course
Mode of Teaching
Live Interactive
Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms.
This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.After completing this Machine Learning Certification Training using Python, you should be able to:
- Gain insight into the 'Roles' played by a Machine Learning Engineer
- Automate data analysis using python
- Describe Machine Learning
- Work with real-time data
- Learn tools and techniques for predictive modeling
- Discuss Machine Learning algorithms and their implementation
- Validate Machine Learning algorithms
- Explain Time Series and it’s related concepts
- Gain expertise to handle business in future, living the present
Prerequisite:
- The pre-requisites for the Machine Learning Certification Training using Python includes development experience with Python.
- Fundamentals of Data Analysis practised over any of the data analysis tools like SAS/R will be a plus. However, Python would be more advantageous.
- You will be provided with complimentary “Python Statistics for Data Science Course” as a self-paced course once you enrol for the course.
Prerequisites
- The pre-requisites for the Machine Learning Certification Training using Python includes development experience with Python.
- Fundamentals of Data Analysis practised over any of the data analysis tools like SAS/R will be a plus. However, Python would be more advantageous.
- You will be provided with complimentary “Python Statistics for Data Science Course” as a self-paced course once you enrol for the course.
Course Duration:
36 Hours
Class Hours:
2 Hours Day time slots or 3 Hours week end Slots (May change)