Description
Course Name: Certificate in Python Machine Learning
Course Id: CPML/Q1001.
Eligibility: 10th Grade (high school) or Equivalent.
Duration: Two Month.
Objective: The Certificate in Python Machine Learning course aims to equip learners with comprehensive knowledge and hands-on skills in applying Python programming to solve real-world problems using machine learning techniques. The course focuses on building a strong foundation in Python, data preprocessing, model building, evaluation, and deployment, enabling students to design intelligent systems and data-driven solutions.
How to Enroll and Get Certified in Your Chosen Course:
Step 1: Choose the course you wish to get certified in.
Step 2: Click on the “Enroll Now” button.
Step 3: Proceed with the enrollment process.
Step 4: Enter your billing details and continue to course fee payment.
Step 5: You will be redirected to the payment gateway. Pay the course and exam fee using one of the following methods:
Debit/Credit Card, Wallet, Paytm, Net Banking, UPI, or Google Pay.
Step 6: After successful payment, you will receive your study material login ID and password via email within 48 hours of fee payment.
Step 7: Once you complete the course, take the online examination.
Step 8: Upon passing the examination, you will receive:
• A soft copy (scanned) of your certificate via email within 7 days of examination.
• A hard copy (original with official seal and signature) sent to your address within 45 day of declaration of result.
Step 9: After certification, you will be offered job opportunities aligned with your area of interest.
Online Examination Detail:
Duration- 60 minutes.
No. of Questions- 30. (Multiple Choice Questions).
Maximum Marks- 100, Passing Marks- 40%.
There is no negative marking in this module.
| Marking System: | ||||||
| S.No. | No. of Questions | Marks Each Question | Total Marks | |||
| 1 | 10 | 5 | 50 | |||
| 2 | 5 | 4 | 20 | |||
| 3 | 5 | 3 | 15 | |||
| 4 | 5 | 2 | 10 | |||
| 5 | 5 | 1 | 5 | |||
| 30 | 100 | |||||
| How Students will be Graded: | ||||||
| S.No. | Marks | Grade | ||||
| 1 | 91-100 | O (Outstanding) | ||||
| 2 | 81-90 | A+ (Excellent) | ||||
| 3 | 71-80 | A (Very Good) | ||||
| 4 | 61-70 | B (Good) | ||||
| 5 | 51-60 | C (Average) | ||||
| 6 | 40-50 | P (Pass) | ||||
| 7 | 0-40 | F (Fail) | ||||
Key Benefits of Certification- Earning a professional certification not only validates your skills but also enhances your employability. Here are the major benefits you gain:
Practical, Job-Ready Skills – Our certifications are designed to equip you with real-world, hands-on skills that match current industry demands — helping you become employment-ready from day one.
Lifetime Validity – Your certification is valid for a lifetime — no renewals or expirations. It serves as a permanent proof of your skills and training.
Lifetime Certificate Verification – Employers and institutions can verify your certification anytime through a secure and reliable verification system — adding credibility to your qualifications.
Industry-Aligned Certification –All certifications are developed in consultation with industry experts to ensure that what you learn is current, relevant, and aligned with market needs.
Preferred by Employers – Candidates from ISO-certified institutes are often prioritized by recruiters due to their exposure to standardized, high-quality training.
Free Job Assistance Based on Your Career Interests – Receive personalized job assistance and career guidance in your preferred domain, helping you land the right role faster.
Syllabus:-
Introduction to Python for Machine Learning: Introduction to Python and its applications in ML, Setting up the Python environment (Anaconda, Jupyter Notebook), Basic Python programming concepts, Data structures (lists, tuples, dictionaries, sets), Control structures (loops, conditionals, functions), Working with Python libraries (NumPy, Pandas, Matplotlib, Seaborn), File handling and data input/output, Introduction to object-oriented programming (OOP), Working with APIs and web scraping for data collection, Best practices in Python coding.
Data Preprocessing and Cleaning: Understanding data types and formats, Handling missing values (mean, median, mode imputation), Data transformation techniques (scaling, normalization, encoding), Feature engineering and selection, Removing duplicates and irrelevant data, Handling categorical variables (one-hot encoding, label encoding), Outlier detection and treatment, Data augmentation techniques, Automating data preprocessing with pipelines, Exploratory data analysis (EDA).
Exploratory Data Analysis (EDA) and Visualization: Understanding descriptive statistics, Data visualization with Matplotlib and Seaborn, Univariate and bivariate analysis, Identifying data distributions, Correlation and covariance analysis, Pair plots and box plots for feature insights, Dimensionality reduction (PCA, t-SNE), Feature importance visualization, Interactive data visualization with Plotly, Best practices for effective storytelling with data.
Introduction to Machine Learning: Definition and types of machine learning (supervised, unsupervised, reinforcement), Understanding bias-variance tradeoff, Training vs. testing vs. validation sets, Model evaluation metrics (accuracy, precision, recall, F1-score, RMSE), Overfitting and underfitting, Cross-validation techniques, Hyperparameter tuning and optimization, Implementing ML models using Scikit-learn, Introduction to ML workflows and pipelines, Best practices for model selection.
Supervised Learning – Regression Models: Linear regression fundamentals, Polynomial regression, Multiple linear regression, Ridge and Lasso regression, Decision tree regression, Random forest regression, Support vector regression (SVR), Evaluation metrics for regression (MSE, RMSE, R²), Hyperparameter tuning for regression models, Real-world applications of regression models.
Supervised Learning – Classification Models: Logistic regression and its applications, K-nearest neighbors (KNN) algorithm, Decision trees for classification, Random forest classification, Support vector machines (SVM), Naïve Bayes classifier, Gradient boosting (XGBoost, LightGBM, CatBoost), ROC curve and AUC score evaluation, Handling imbalanced datasets (SMOTE, class weighting), Practical applications of classification models.
Job Opportunities after Certificate in Python Machine Learning
Graduates of this program gain expertise in Python programming, machine learning algorithms, deep learning, data analysis, and AI model development. This equips them for roles in IT, data science, artificial intelligence, business intelligence, and research.
Key Career Options: Machine Learning Engineer, Data Scientist, Data Analyst, AI Engineer, Python Developer (Machine Learning Focus), Business Intelligence Analyst, Deep Learning Engineer, NLP Engineer, Research Scientist (AI/ML), Quantitative Analyst (Quant).
Salary Range (India):
Entry-level: ₹4–8 LPA
Mid-level: ₹8–15 LPA
Senior-level: ₹15–30 LPA
Industries Hiring Graduates: IT & Software Development, E-commerce, Financial Services & Banking, Healthcare & Pharmaceuticals, Automotive & Autonomous Vehicles, Research & Development, AI Startups, Telecommunications, Retail & Marketing, Government & Defense.
Skills Developed: Python programming, machine learning algorithms, deep learning, neural networks, data preprocessing & visualization, natural language processing (NLP), AI model implementation, statistical analysis, predictive modeling, and business intelligence reporting.
Graduates can advance to senior roles such as Senior Machine Learning Engineer, Lead Data Scientist, AI Engineer Lead, NLP Specialist, or Quantitative Analyst. Additional certifications, specialized training in deep learning, AI, NLP, or cloud-based machine learning platforms can significantly enhance career growth and earning potential.
