Course Content
Day-2: How to use VScode (an IDE) for Python?
Day-3: Basics of Python Programming
This section will train you for Python programming language
Day-4: Data Visualization and Jupyter Notebooks
You will learn basics of Data Visualization and jupyter notebooks in this section.
Day-5: MarkDown language
You will learn whole MarkDown Language in this section.
Day-10: Data Wrangling and Data Visualization
Data Wrangling and Visualization is an important part of Exploratory Data Analysis, and we are going to learn this.
Day-11: Data Visualization in Python
We will learn about Data Visualization in Python in details.
Day-12,13: Exploratory Data Analysis (EDA)
EDA stands for Exploratory Data Analysis. It refers to the initial investigation and analysis of data to understand the key properties and patterns within the dataset.
Day-15: Data Wrangling Techniques (Beginner to Pro)
Data Wrangling in python
Day-26: How to use Conda Environments?
We are going to learn conda environments and their use in this section
Day-37: Time Series Analysis
In this Section we will learn doing Time Series Analysis in Python.
Day-38: NLP (Natural Language Processing)
In this section we learn basics of NLP
Day-39: git and github
We will learn about git and github
Day-40: Prompt Engineering (ChatGPT for Social Media Handling)
Social media per activae rehna hi sab kuch hy, is main ap ko wohi training milay ge.
Python ka Chilla for Data Science (40 Days of Python for Data Science)
About Lesson

Maximum likelihood estimation is a common approach used for parameter estimation in statistical models. Here are the key aspects of maximum likelihood:

  • Given a statistical model with parameters and some data, the method estimates the set of parameters that maximize the likelihood or probability of observing the given data.

  • Likelihood is the conditional probability of obtaining the sample data given the parameters of the distribution.

  • Maximizing likelihood finds the parameters that make the observed data most probable or likely.

  • For example, in linear regression the parameters (slope, intercept) that maximize the Gaussian likelihood of the residuals are estimated.

  • Common methods used for maximization include iterative procedures like gradient ascent.

  • In statistics, maximum likelihood produces consistent, efficient and asymptotically normal estimates under regularity conditions.

  • Used widely in building probabilistic models like logistic regression, Naive Bayes, mixture models etc.

  • Provides natural way to estimate structured probabilistic models with latent variables.

  • Objectively evaluates which parameter values best “explain” the data without making prior assumptions.

So in summary, maximum likelihood is a principled way to estimate parameters of statistical distributions from observed data in an objective, model-consistent manner.

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