Please note that this course has the following prerequisites which must be completed before it can be accessed
About This Course
The Python with Machine Learning course is designed to provide participants with a robust foundation in Python programming and its application to machine learning. This course covers essential Python concepts, libraries, and tools such as NumPy, Pandas, Matplotlib, and Scikit-Learn. Participants will learn to preprocess data, build and evaluate machine learning models, and implement various algorithms including regression, classification, clustering, and neural networks. The course emphasizes practical, hands-on experience through projects and real-world case studies, enabling learners to apply machine learning techniques to solve complex problems. Ideal for aspiring data scientists, analysts, and AI enthusiasts, this course equips learners with the skills needed to harness the power of Python and machine learning for data-driven decision-making and innovative solutions.
Skills You Will Learn
Python Programming: Master Python fundamentals including syntax, data structures, functions, and object-oriented programming concepts.
Data Manipulation: Use libraries like NumPy and Pandas for data manipulation, cleaning, and preprocessing.
Data Visualization: Create insightful visualizations using Matplotlib and Seaborn to explore and present data effectively.
Machine Learning Algorithms: Implement and apply supervised and unsupervised learning algorithms such as linear regression, logistic regression, decision trees, clustering, and dimensionality reduction.
Model Evaluation: Evaluate machine learning models using appropriate metrics and techniques to assess performance.
Neural Networks and Deep Learning: Understand the basics of neural networks and build basic models using TensorFlow or Keras.
Model Selection and Tuning: Select the best machine learning model and tune hyperparameters for optimal performance.
Deployment and Integration: Understand how to deploy machine learning models and integrate them into production systems.
Problem-Solving Skills: Develop critical thinking and problem-solving skills to approach data-driven challenges effectively.
Collaboration and Communication: Work effectively in teams, communicate findings, and collaborate on data science projects.
Key Highlights: Python with Machine Learning Training
Comprehensive Python Foundations: Gain a strong understanding of Python programming, covering essential concepts, syntax, and best practices.
Advanced Topics: Explore advanced machine learning topics such as ensemble methods, natural language processing, and time-series forecasting.
Industry Applications: Learn about various applications of machine learning across different industries such as healthcare, finance, marketing, and more.
Expert Instructors: Learn from experienced professionals and industry experts with deep knowledge of Python and machine learning.
Flexible Learning: Access self-paced modules, interactive sessions, and flexible schedules that fit your learning preferences.
Career Support: Receive guidance and resources for resume building, interview preparation, and job placement assistance to advance your career in data science and machine learning.
Learning Python for Data Science is essential due to its extensive libraries (like Pandas, NumPy) for data manipulation and machine learning, and its versatility in integrating with various tools, making it a cornerstone of modern data analysis workflows. Learning this skill makes you a hot skill person and helps you to get better placements in short span of time.
If you miss a live session, all classes are recorded and you can always watch later for review at your convenience. Additionally, you can typically reach out to instructors or access supplementary materials and forums, doubt sessions to ensure you stay updated and grasp the missed content effectively.
After obtaining a Data Science with Python certification, you can pursue roles such as Data Scientist, Data Analyst, Data modeller , Data Engineer, or Business Analyst, leveraging Python for data manipulation, analysis, visualization, and machine learning tasks across various industries.
A Data Science expert should be proficient in programming languages like Python and R, possess strong statistical and machine learning skills, and be adept at data visualization and communication to interpret and present insights effectively.
There is no limit as such, you can revise with multiple batches. But usually 2-3 batches are give per person and subject to availability and other factors like, trainer feedback, effort put on learning, reason for new batch allocation. We want you to have the best learning environment and know that people have no control over circumstances in their life so might miss out on some classes, so give you options to revise with next batches.
Curriculum
59 Lessons23h 15m
Introduction to Python for Data Science
Overview of Data Science and Analytics
Introduction to Python Programming
Setting Up the Python Environment (Anaconda, Jupyter Notebooks)
Python Basics (Variables, Data Types, Control Structures)
Introduction to Python Libraries (NumPy, Pandas, Matplotlib)
Data Manipulation with Pandas
Introduction to Pandas DataFrames and Series
Data Importing and Exporting (CSV, Excel, SQL, JSON)
Data Cleaning and Preprocessing
Handling Missing Values and Outliers
Data Transformation and Aggregation
Data Visualization with Matplotlib and Seaborn
Introduction to Data Visualization
Plotting with Matplotlib (Line Plots, Bar Plots, Histograms)
Advanced Visualization with Seaborn (Box Plots, Violin Plots, Heatmaps)
Customizing Plots and Visualizations
Creating Interactive Visualizations with Plotly
Exploratory Data Analysis (EDA)
Descriptive Statistics and Summary Statistics
Identifying Patterns and Outliers
Correlation Analysis
Data Visualization Techniques for EDA
Case Studies in EDA
Introduction to Machine Learning
Overview of Machine Learning Concepts
Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
Steps in a Machine Learning Workflow
Introduction to Scikit-Learn Library
Model Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
Supervised Learning – Regression
Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Regularization Techniques (Ridge, Lasso)
Model Evaluation and Validation (Train-Test Split, Cross-Validation)