Why this course?

Description

Machine Learning (ML) in Python involves creating algorithms and models that enable computers to learn from data, make predictions, and automate decision-making. Python's simplicity and powerful libraries like scikit-learn, Tensor Flow, and PyTorch make it a popular choice for ML development. Key steps include importing libraries, loading and exploring data, pre-processing, choosing a model, and training, evaluating, and making predictions. ML encompasses supervised learning (labeled data), unsupervised learning (unlabelled data), and reinforcement learning (learning through interaction). With Python, practitioners leverage NumPy, Pandas, and visualization tools for effective data manipulation and analysis, enabling the creation of robust ML solutions.

Machine Learning (ML) is a subfield of artificial intelligence (AI) that focuses on developing algorithms and models capable of learning from data to make predictions or decisions without explicit programming. Python has emerged as a prominent language for ML due to its readability, 
versatility, and a vast ecosystem of libraries tailored for data manipulation and model development.
 

Characteristics of Machine Learning in Python:
The characteristics of Machine Learning (ML) in Python are shaped by the language's features, the 
richness of its libraries, and the overall ecosystem. Python is known for its readability and simplicity. This makes it accessible for beginners and facilitates collaboration among data scientists and developers.
 

Versatility:
Python supports a wide range of ML techniques, from traditional statistical methods to advanced 
deep learning. It can be used for various ML tasks, including classification, regression, clustering, 
and more.
Extensive Libraries:
Python boasts powerful ML libraries such as sci-kit-learn, TensorFlow, PyTorch, and Keras. These 
libraries provide pre-built functions and classes, accelerating the development of ML models.
Visualization Tools:
Python offers powerful visualization tools such as Matplotlib and Seaborn. These tools enable users to create informative plots and charts, aiding in the interpretation of data and model results.

Credit Scoring: ML algorithms assess creditworthiness based on historical data.
Fraud Detection: ML identifies unusual patterns or behaviors indicating potential fraud.
Algorithmic Trading: ML models analyze market trends for automated trading decisions.
Recommendation Systems: ML algorithms suggest products based on user preferences.
Demand Forecasting: ML predicts future demand for products, optimizing inventory management.
Predictive Maintenance: ML forecasts equipment failures, enabling proactive maintenance.
Quality Control: ML identifies defects in real time during the manufacturing process.
Telecommunications:
Network Optimization: ML optimizes network performance and predicts potential issues.
Customer Service Chatbots: ML-powered chatbots handle customer queries intelligently.
Natural Language Processing (NLP):
Chatbots and Virtual Assistants: ML enables natural language understanding for human-computer
interactions.
Sentiment Analysis: ML models analyze text data to determine sentiment.
Personalized Learning: ML tailors educational content to individual student needs.
Student Performance Prediction: ML analyses academic data to predict student performance.
 

Modules OF MACHINE LEARNING (PYTHON):

1.) Introduction to ML and AI
2.) Python – Tools | Syntaxes & Data Structures
3.) ML Concepts
4.) Pandas
5.) Pandas – Data Structures
6.) Numpy library - Array Operations | Mathematical Functions
7.) Numpy - Sort, Search and Counting Functions | Byte Swapping
8.) Matplotlib, Histogram Using Matplotlib | I/O with Nump0079
9.) Matplotlib Library - Introduction, Pyplot API | Types of Plots
10.) Seaborn Library
11.) SKLearn Library
12.) Google Colab Notebook
13.) Data Preparation & Visualization
14.) Data wrangling
15.) Supervised Learning Algorithms
16.) Liver Disease Prediction - Linear Regression
17.) Flower Species using Regression
18.) Fake News using Naive Bayes
19.) Android Malware Detection
20.) Credit Card Fraud Detection
21.) Employee Salary Prediction - Unsupervised Learning
22.) Ad Prediction
23.) Agri price Prediction Model using ML
24.) Flower Species Data Visualization - PCA
25.) Market Basket Analysis - APRIARI
26.) Hate Speech Detection using ML
27.) Loan Prediction using XG Boost Algorithms
28.) Movie Review Recommendation using RNN
29.) Digit Classification using CNN
30.) AI – Cart Pole – Reinforcement Learning