Python Certification Training Course
Python happens to be one of the fastest growing programming languages. This is because it is versatile and at the same time powerful for developers all over the world. Python can be used for web development and visualization that is sure to enable developers to code easily. Python Certification Training Course would teach you about the basics of python language and at the same time would help you understand the functions as well as controls of python. Python certification course would also teach you the various concepts of python and how it can be applied to either machine learning or deep learning.
Main Features
- Intensive instructor-led teaching, assistance, supervision as well as examples
- Understanding and mastery of the basics of the Python programming language.
- Understanding the various aspects and technologies that can be combined to ensure that they are able to code.
Learning Objectives
For Individuals
- Would offer them self-paced learning contents, designs, and models that have been created by top industry experts
For Businesses
- Would offer them cooperate solutions
- Would offer them advanced and enhanced individual and team reporting
- Would offer them well-blended learning contents
Python Certification Training Course Description
Python is the fastest growing and commonly used programming language, it is important that you know how it can be applied to solve some of the world’s problems. Also, Python has been used in different learning and data analysis, so it is important that developers and all programming enthusiasts understand how it functions.
- Would teach you to understand how python works as well as the concepts
- This course would teach you how python can be applied to help solve the world’s problems.
- This course would help you understand certain inter-workings of python and the various types.
- It would also teach you the control and conditional flow
- Created for developers who seek to make use of the Python programming language
Python Certification Course Curriculum
1. Introduction
1.1 Introduction to Data Science
Key Elements of Data Science
Data Warehousing
Business Intelligence
Data Visualization
Data Mining
Machine Learning
Artificial Intelligence
Cloud Computing
Big Data
1.2 Artificial Intelligence: A preview
What is Artificial Intelligence & its importance
Artificial Intelligence vs Machine Learning
2. Getting Started with Machine Learning
2.1 Overview of Machine Learnin
Quick tour of the types of machine learning
What is Machine Learning (ML)?
How machines learn
Types of learning: Supervised, Semi-supervised, Unsupervised, Reinforcement.
Basics of Classification, Regression and Clustering algorithms
Creating your first Prediction Model
Training & Model evaluation
Choosing Machine Learning algorithm
3. Back to Basics (Maths with Statistics)
3.1 A quick refresh on basic intermediate maths:
A quick refresh on basic intermediate maths:
Linear Algebra (Vectors, Matrix, Eigen Values)
Probability and Statistics
Hypothesis testing
Optimization
4. Getting Started with Python
4.1 A quick crash course on basics of Python
A quick crash course on basics of Python
What is Python
Working with Python
Basic scripts on
Read, write, data handling
Loops
Conditions (if-else)
Function
Code modularization
Scikit-Learn package
Basic visualization
5. Data Processing for Machine Learning
5.1 Introduction to Data Processing
Data Collection & Preparation
Data Mugging
Outlier Analysis
Missing value treatment
Feature Engineering
Data Transformation
Normalization vs Standardization
Creating Dummies
Dimensionality Reduction
Principal Component Analysis
6. Advanced Machine Learning Algorithm
6.1 Overview to Machine Learning Algorithms
Supervised Machine Learning algorithms
Linear Regression
Logistic Regression
Decision/Classification Tree
Ensemble Models
Bagging
Boosting
Random Forest
K-Nearest Neighbours (KNN)
Naive Bayes
Neural Network (Deep Learning)
Support Vector Machine
Unsupervised Machine Learning algorithms
Clustering with K-means Clustering
Bias-Variance Trade off
Regularization
Parameter tuning & grid search optimization
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