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

A neural network is a machine learning model inspired by the human brain. Here are some key things to know about neural networks:

  • Neural networks contain interconnected nodes called neurons that pass signals to each other. They are organized in interconnected layers – an input layer, multiple hidden layers, and an output layer.

  • Each neuron receives input signals and performs mathematical operations to determine an output value. The connections between neurons have numeric weights associated with them that are tuned during training.

  • Neural networks have the ability to learn complex patterns and relationships in large amounts of data through a process called training. During training, examples are fed into the network and adjusting weights allows the network to fit the data.

  • Common neural network models include multilayer perceptrons (MLP), convolutional neural networks (CNNs), recurrent neural networks (RNNs).

  • MLPs are feedforward networks used for classification and regression tasks. CNNs are used primarily for image and video recognition. RNNs work well for sequence data like text and time series.

  • Neural networks excel at complex pattern recognition tasks like image classification, speech recognition, machine translation etc. where traditional algorithms perform poorly.

  • Their ability to model highly complex nonlinear relationships is what makes them very powerful predictive models.

So in summary, neural networks mimic the human brain in learning from large amounts of data through adjustment of weights between interconnected nodes.

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Syed Abdul Qadir Gilani 9 months ago