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Python Machine Learning Training

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curriculum

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Course Introduction

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.

Python Machine Learning

Introduction to Python/R for Data Science:

Building the environment
Variables & Data Types: Strings, integers, floats, Booleans
Data Structures: Vector, Lists, Arrays, Dataframes etc.
Functions & Methods
Introduction to Packagesin Python: numpy, pandas, matplotlib, seaborn, scikit-learn etc.
Introduction to Packages in R: dplyr, tidyr, tidyverse, ggplot2, caret etc.

Data Importing

Flat files
Excel
Databases
Web

Data Cleaning

Inconsistent column names
Duplicate rows
Datatype conversions
Working with Datetime
Tidying data: Long vs Wide
Dealing with missing values:MCAR, MAR, MNAR
Dealing with Outliers

Business Statistics: Probability & Statistics

Introduction
Population & Sample
Hypothesis Testing
Statistical Distributions –Gaussian & T distribution
Univariate & Multivariate distribution
Test for Normality –Q-Q plot, statistical tests such as Shapiro, Jacque Bera etc.
Box-Cox Transformation
Covariance & Correlation

Exploratory Data Analysis

Exploring Categorical Variable: Counts/Proportion etc.
Exploring Numerical Variable: Summarising data
Summarising data:
- Measures of Centre:Mean, Median & Mode
- Measures of Variability: Standard deviation/Variance, IQR, Range
- Modality:Unimodal, Bimodal, Multimodal, Uniform
- Skewness
- Kurtosis
Data Visualization: Barchart, Histogram, Boxplots, Scatter plots, Heatmaps etc.
Data Manipulation:select, mutate, filter, groupby, summarise etc.

Pre-processing Data for Modelling - Adding value to dataset

Standardizing Data
Feature Engineering: Encoding categorical variables (Binary, One-hot), Engineering Numerical Variable
Feature Selection: PCA
Splitting the data (training&testing), cross validation etc.

Machine Learning

Supervised: Labelled information - Regression & Classification
- Linear Regression
- KNN
- Naïve Bayes
- Logistic Regression
- Decision Trees
- Ensemble Models: Random Forest, Boosted Tree, Bagged Tree
- Support Vector Machines etc.
Model Evaluation Parameters
- Regression –R-Squared & Adj. R-Squared, RMSE, MSE, MAE etc.
- Classification –Accuracy, Confusion Matrix, Precision, Recall etc.
Regularization & Hyperparameter tuning

Unsupervised: No labelled information

Clustering: K-means, Hierarchical etc.
Dimensionality Reduction

FAQ

What are the Pre-Requisites of this course?

There are no particular prerequisites to learn Python course but basic knowledge in any of the programming language will be added a value advantage.

Why should you take Python Training?

Python is a highly popular object-oriented language which is simple to learn and easy to deploy as well. Python can easily run on various systems like Linux, Windows, and Mac. After the completion of , Python Training in Hyderabad, aspirants can easily work on Big Data Hadoop Environment for very high salary package range. Python is completely with Language Interoperability and documentation system with hierarchical module system to boost revenues.

Who can take this Python Course?

Freshers and Graduates
Those who are showing interest to build their career in Python
IT Professionals
Communication Professionals
Analytical Professionals
Big Data Professionals
Network Professionals

Can I attend a demo session before enrollment?

We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight into how are the classes conducted, quality of instructors and the level of interaction in a class.

Can you schedule the training based upon my availability?

Yes, we will discuss with our instructor and will schedule according to the time convenient to you.

What if I have more queries?

Just give us a call at +91 9989971070 OR email at info@pythonschool.in