What you'll be learning
This Python with Machine Learning course is designed for individuals with basic Python knowledge who want to specialize in machine learning. It covers Python fundamentals, data manipulation with libraries like Pandas and NumPy, and introduces machine learning algorithms using Scikit-learn. You’ll learn techniques in supervised and unsupervised learning, data preprocessing, model evaluation, and deep learning. The course also includes practical applications through projects like recommendation systems and sentiment analysis. By the end, you will have gained skills in Python programming, AI, and machine learning, along with hands-on experience to enhance your resume and increase your job prospects.
- Designed for individuals with basic Python knowledge aiming to specialize in machine learning.
- Covers Python fundamentals and data manipulation using libraries like Pandas and NumPy.
- Introduces machine learning algorithms with Scikit-learn and techniques for supervised and unsupervised learning.
- Explores data preprocessing, model evaluation, and deep learning concepts.
- Includes hands-on projects like recommendation systems and sentiment analysis for practical experience.
- Equips learners with Python programming, AI, and machine learning skills to boost resumes and job opportunities.
Module 1: Core Python
Introduction to Python, its syntax, variables, data types, operators, and basic input/output operations. Learn about control statements, loops, and various data structures like lists, tuples, sets, and dictionaries.
Module 2: Control Statements & Data Structures
Focus on conditional statements, loops, and in-depth exploration of data structures such as strings, lists, tuples, sets, and dictionaries. Learn data manipulation and common operations with each structure.
Module 3: Functions
Understanding Python functions, their types, arguments, and the concept of recursion. Explore advanced function concepts like lambda, map, filter, reduce, decorators, and generators.
Module 4: Advanced Python
Covers exception handling, regular expressions, file handling, and directory management. Introduces essential Python libraries like NumPy and Pandas for data analysis, with tools for data visualization using Matplotlib and Seaborn.
Module 5: Pandas
In-depth exploration of the Pandas library for data manipulation. Learn how to handle Series and DataFrame objects, perform indexing, sorting, merging, and apply functions to data. Focus on missing data handling and visualization techniques.
Module 6: NumPy & Data Analytics
Introduction to NumPy arrays, indexing, and array manipulation. Includes case studies like image analysis. Learn advanced mathematical functions, data sorting, and searching with NumPy.
Module 7: Machine Learning Fundamentals
Learn about supervised and unsupervised machine learning algorithms, data preprocessing, and model evaluation. Explore techniques like linear regression, decision trees, SVM, and ensemble methods like Random Forest and XGBoost.
Module 8: Machine Learning & Deep Learning
Detailed study of advanced machine learning algorithms, deep learning techniques, and NLP concepts. Learn how to implement clustering, dimensionality reduction (PCA), and work on industry projects like recommendation systems and sentiment analysis.
Module 9: Industry Projects
Apply the skills learned in real-time projects such as building a recommendation system for e-commerce and performing sentiment analysis on product reviews using machine learning techniques.
By completing this course, you will:
- Master Python programming, including advanced concepts and libraries.
- Understand and apply core data manipulation and analysis tools like Pandas and NumPy.
- Build and evaluate machine learning models using Scikit-learn.
- Gain expertise in supervised and unsupervised learning techniques.
- Perform data preprocessing, feature engineering, and model validation.
- Visualize data effectively using Matplotlib and Seaborn.
- Understand deep learning basics and implement NLP techniques.
- Work on real-world projects like recommendation systems and sentiment analysis.
- Enhance your portfolio and job prospects in AI and machine learning fields.
