Machine Learning using Python – Advanced

About Course

Data Science is one of the fastest growing fields in technology. Get the dream job of Data scientist by mastering the skills you need to analyze data with SQL and Python.

Objective of the program

To introduce the participant to the advanced statistical algorithms in Machine learning and how it can be implemented in Python.

Topics covered

Introduction to Statistics
Exploratory Data Analysis (EDA)
Model Selection & Evaluation
Linear Regression
Prediction of Car Prices – Case Study
Logistic Regression
Decision Trees and Random Forests
Clustering (K-means)
Naive Bayes
Support Vector Machines (SVM)

Why choose this program?

Certification Program
Self paced, with tons of additional learning materials to refer
Learn advanced statistical algorithms in one single course
A dedicated mail id for query support and guidance, unlike other online courses
Step by step approach to guide you from basics on every topic
2 case studies with solutions
Quiz for every module to test your understanding

Key Takeaways

Build different ML models using advanced statistical algorithms
Understand how advanced statistical algorithms work and applied in market

Course Content

Introduction to Statistics
Statistics is fundamental to machine learning, providing the tools to analyze data, make inferences, and validate models. It bridges the gap between data and decision-making by enabling practitioners to understand data distributions, patterns, relationships, and uncertainties. In machine learning, statistics helps in selecting, training, evaluating, and interpreting models to make reliable predictions.

Role of Statistics in Machine Learning
Basic Statistics and Inferential statistics

18:24
Normal Distribution

12:38
Hypotheses Testing

07:33

Exploratory Data Analysis

Model Selection & Evaluation
Model Selection & Evaluation

Linear Regression

Logistic Regression

Decision Trees & Random Forests

Clustering

Naive Bayes

Support Vector Machines

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About Course

Course Content

Role of Statistics in Machine Learning

Basic Statistics and Inferential statistics

Normal Distribution

Hypotheses Testing

Exploratory Data Analysis

Introduction to EDA

Data Cleaning

Univariate Bivariate Analysis

CrispDM Approach

Model Selection & Evaluation Model Selection & Evaluation

Introduction to Model Selection & Evaluation

Bias and Variance

Linear Regression

Introduction to Linear Regression

Building Linear Regression Model

Building Linear Regression Model using Python – Part 1

Building Linear Regression Model using Python – Part 2

Building Linear Regression Model using Python – Part 3

Logistic Regression

Introduction to Logistic Regression

Building the model using Logistic Regression

Building the model using Logistic Regression with Python – Part 1

Building the model using Logistic Regression with Python – Part 2

Decision Trees & Random Forests

Introduction to Decision Trees

Building Decision Trees

Implementation in Python – Decision Trees

Random Forests Algorithm

Implementation in Python – Random Forests – Part-1

Implementation in Python – Random Forests – Part-2

Clustering

Introduction to Clustering

Building the model- Clustering

Implementation in Python K-Means – Part 1

Implementation in Python K-Means – Part 2

Naive Bayes

Introduction to Naive Bayes

Building the model -Naive Bayes

Implementation in Python – Naive Bayes

Support Vector Machines

Introduction to Support Vector Machines

Building the Model – SVM

Implementation in Python – SVM

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Model Selection & Evaluation
Model Selection & Evaluation