Artificial Intelligence With Python

Curriculum

The Course Curriculam

Week 1: Python Programming Basics

Setting up the Python environment (Anaconda, Jupyter Notebook, etc.).
Python Basics: Data types, variables, and control flow.
Python Libraries for AI: Introduction to NumPy, pandas, and matplotlib
Hands-on Practice: Implementing basic operations and visualizing data with matplotlib.

Week 2: Introduction to AI and Machine Learning Concepts

Week 3: Exploratory Data Analysis (EDA)

Week 4: Supervised Learning Basics

Week 5: Unsupervised Learning

Week 6: Neural Networks and Deep Learning

Week 7: Natural Language Processing (NLP)

Week 8: AI Applications and Final Project

₹1000

Course Information

Instructor: vedupskilling@gmail.com
Lesson: 29
Duration: 40h
Quizze: 0

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Artificial Intelligence With Python

The Course Curriculam

Week 1: Python Programming Basics

Setting up the Python environment (Anaconda, Jupyter Notebook, etc.).

Python Basics: Data types, variables, and control flow.

Python Libraries for AI: Introduction to NumPy, pandas, and matplotlib

Hands-on Practice: Implementing basic operations and visualizing data with matplotlib.

Week 2: Introduction to AI and Machine Learning Concepts

What is Artificial Intelligence? AI vs. ML vs. Deep Learning.

Types of AI: Supervised, Unsupervised, and Reinforcement Learning.

Data Preprocessing: Cleaning and preparing datasets.

Hands-on Practice: Load and preprocess datasets using pandas.

Week 3: Exploratory Data Analysis (EDA)

Visualizing data distributions with seaborn and matplotlib.

Understanding feature engineering and selection.

Hands-on Practice: Perform EDA on a real-world dataset (e.g., Iris or Titanic).

Week 4: Supervised Learning Basics

Regression: Simple and multiple linear regression using scikit-learn.

Classification: Logistic regression and k-Nearest Neighbors (k-NN).

Evaluation Metrics: Accuracy, precision, recall, F1-score.

Hands-on Practice: Build and evaluate regression and classification models.

Week 5: Unsupervised Learning

Introduction to clustering: K-means and hierarchical clustering.

Dimensionality Reduction: PCA (Principal Component Analysis)

Hands-on Practice: Apply clustering techniques to analyze customer segmentation.

Week 6: Neural Networks and Deep Learning

Understanding perceptrons and multi-layer perceptrons (MLP).

Building neural networks with TensorFlow and Keras.

Activation functions and backpropagation basics.

Hands-on Practice: Create a simple neural network for MNIST digit classification.

Week 7: Natural Language Processing (NLP)

Text Preprocessing: Tokenization, stemming, and lemmatization.

Introduction to word embeddings: Word2Vec and GloVe.

Building a text classification model using scikit-learn.

Hands-on Practice: Create a sentiment analysis tool using real-world data.

Week 8: AI Applications and Final Project

AI in real-world use cases: Image recognition, speech processing, recommendation systems

Introduction to Reinforcement Learning: Basics of Q-Learning.

Final Project: Develop an AI application (e.g., chatbot, image classifier, or recommender system).