Python-Based Learning
Learn by writing and running code.
MACHINE LEARNING WITH PYTHONCURRICULUM PREVIEW
Machine Learning with Python
Course fee: INR 18,000 + 18% GST (INR 3,240). Total including GST: INR 21,240.
Confirm exclusions, materials, assessments and payment options with admissions.
Turn Data into Intelligence. Build the Future with Machine Learning.
Learn how to build intelligent systems that identify patterns, make predictions, automate decisions, and solve real-world problems using Python and machine learning algorithms.
Machine Learning with Python | Practical, Career-Focused Training
90 hourscourse duration
Contact admissionsupcoming batch
Confirm online / offline availabilitylearning mode
Course duration: 90 hours. Contact admissions for batch dates, daily timings and delivery arrangements.
Program Highlights
AI & ML Fundamentals
Understand algorithms and intelligent systems.
Data-Driven Projects
Work with datasets and predictive models.
Career Preparation
Build portfolio and interview skills.
Program Overview
Turn Data into Intelligence. Build the Future with Machine Learning.
The Machine Learning with Python program at E Careerpluz combines programming fundamentals, data analysis, statistical concepts, machine learning algorithms, and hands-on projects to help learners develop industry-relevant technical skills.
Whether you are a student, graduate, working professional, or aspiring AI entrepreneur, this course helps you move from Python basics to building practical machine learning solutions.
What you will learn
- Python programming for machine learning.
- Data cleaning, analysis, and visualization.
- Supervised and unsupervised learning algorithms.
- Model training, testing, evaluation, and optimization.
- Real-world machine learning projects using industry-relevant tools.
- Introduction to AI applications and deploying ML models.
Curriculum Preview
From Python Fundamentals to Intelligent Models. The curriculum progresses from essential programming and data handling to model development, evaluation, and practical applications.
Module 1: Python Programming Fundamentals
- Variables, data types, operators and control statements.
- Loops, functions, modules and exception handling.
- Lists, tuples, dictionaries, sets and file handling.
- Object-oriented programming fundamentals.
Module 2: Data Analysis and Preparation
- NumPy and Pandas for data manipulation.
- Data cleaning, missing values and duplicate handling.
- Exploratory data analysis (EDA).
- Matplotlib and Seaborn for data visualization.
Module 3: Mathematics and Statistics for ML
- Descriptive statistics and probability.
- Correlation, distributions and hypothesis concepts.
- Linear algebra fundamentals.
- Understanding model performance and data relationships.
Module 4: Supervised Machine Learning
- Linear and logistic regression.
- Decision trees, random forests and K-nearest neighbours.
- Support Vector Machines (SVM).
- Classification, regression and prediction.
Module 5: Unsupervised Machine Learning
- K-means clustering and hierarchical clustering.
- Dimensionality reduction using PCA.
- Customer segmentation and pattern discovery.
- Feature selection and engineering fundamentals.
Module 6: Model Evaluation and Optimization
- Training, validation and testing datasets.
- Accuracy, precision, recall and F1-score.
- Confusion matrices and regression metrics.
- Cross-validation, hyperparameter tuning and overfitting prevention.
Module 7: Model Deployment and Practical Applications
- Saving and loading trained models.
- Introduction to Flask or FastAPI for model APIs.
- Basic model deployment and application integration.
- Introduction to Git, GitHub and responsible AI practices.
Adjust the depth of each module according to the final course duration, learner entry level and trainer's delivery plan. Confirm the approved syllabus before enrolling.
Tools & Technologies
Learn the Tools Used to Build Machine Learning Solutions
Gain practical experience with Python libraries, development environments, visualization tools, and model-building frameworks used in data science and machine learning workflows.
Machine Learning Tools & Platforms
Python
The core programming language used for data processing, automation, machine learning algorithms and model development.
NumPy
Perform numerical calculations, array operations, mathematical computations and scientific data processing.
Pandas
Clean, transform, filter and analyze structured datasets using powerful data manipulation techniques.
Scikit-learn
Build predictive models for classification, regression, clustering, preprocessing and model evaluation.
Jupyter Notebook & Google Colab
Write code, visualize results, experiment with algorithms and document machine learning workflows interactively.
Matplotlib & Seaborn
Create charts, statistical plots and visual reports to discover patterns and communicate insights.
TensorFlow & Keras
Explore neural networks and introductory deep learning applications, subject to course level.
Git & GitHub
Track code changes, manage project repositories and showcase your portfolio to prospective employers.
Discuss tool access and any separate provider accounts with admissions. Additional technologies: SQL, VS Code, XGBoost, Streamlit, Flask, FastAPI and OpenCV, where included in the selected course track. Technology images are example. Include only tools actually taught in the final syllabus. Third-party names and logos belong to their respective owners. Confirm software access, subscriptions and any additional charges with admissions.
Projects & Practical Work
Build Projects. Solve Problems. Showcase Your Skills.
Turn theoretical concepts into working solutions through guided coding exercises, practical assignments and portfolio-building projects.
PROJECT 1
House Price Prediction
Build a regression model to estimate property prices using features such as location, size and property characteristics.
Skills: Pandas, regression, feature engineering, model evaluation.
PROJECT 2
Customer Segmentation
Group customers according to selected behavioural or purchasing characteristics to support targeted marketing decisions.
Skills: EDA, K-means clustering, visualization.
PROJECT 3
Spam Email Classification
Train a classification model to distinguish spam messages from legitimate emails using an appropriate dataset.
Skills: Text preprocessing, feature extraction, classification metrics.
PROJECT 4
Movie Recommendation System
Develop a basic recommendation engine that suggests movies using content similarity or user preference data.
Skills: Similarity measures, data processing, recommendation logic.
PROJECT 5
Customer Churn Prediction
Identify customers who may discontinue a service and explore how businesses can use predictions to improve retention.
Skills: Classification, model evaluation, business interpretation.
Your project deliverables
- Python notebooks and source code.
- Dataset preparation and analysis reports.
- Model performance evaluation.
- GitHub portfolio repositories.
- A final project presentation and viva.
Your project deliverables can include Python notebooks and source code, dataset preparation and analysis reports, model performance evaluation, GitHub portfolio repositories, and a final project presentation and viva.
Learning Goals
What Will You Be Able to Do After This Course?
By completing the program and its practical assessments, learners should be able to:
Write Python programs for data processing and analysis.
Prepare datasets and handle missing or inconsistent values.
Select suitable machine learning algorithms for defined problems.
Train, test and evaluate predictive models.
Interpret model results using appropriate metrics and visualizations.
Build end-to-end machine learning projects using real or realistic datasets.
Present technical findings and explain business implications.
Create a portfolio that demonstrates practical skills to potential employers.
Industry Salary Growth & Business Opportunities
Turn Your Machine Learning Skills into Career Opportunities
Machine learning skills can be applied across IT services, banking, healthcare, retail, manufacturing, education, logistics and digital businesses.
Career pathways
Machine Learning Engineer
Develop, test and integrate machine learning models into applications.
Data Scientist
Explore data, identify patterns and develop predictive insights.
Data / BI Analyst
Transform data into business intelligence and actionable reports.
AI Solutions Entrepreneur
Develop data-driven services and automation solutions for businesses.
Indicative salary progression in India
Compare these planning ranges with current local opportunities. Pay varies by role, qualifications, experience and employer.
Entry Level
INR 4-8 LPAApproximately 0-2 years of relevant experienceMid-Level
INR 8-15 LPAApproximately 2-5 years, with demonstrated project experienceSenior-Level
INR 15-30+ LPAExperienced professionals with strong technical skillsBusiness opportunities
- AI-powered business analytics and forecasting.
- Customer segmentation and marketing intelligence.
- Predictive maintenance and operational optimization.
- Recommendation systems for online businesses.
- Data analytics consulting and reporting services.
- Custom machine learning solutions for small and medium businesses.
Placement Assistance
Learn with Confidence. Prepare for Your Career.
E Careerpluz supports learners in preparing for employment opportunities through career guidance, portfolio development and interview preparation.
Career support services
Resume & Portfolio Building
Develop a professional resume highlighting Python skills, machine learning projects and technical competencies.
Mock Interviews
Practise Python programming questions, ML fundamentals, project explanations and HR interview questions.
GitHub & Project Presentation
Organize project code and learn to explain the problem, methodology, results and limitations.
Job Application Guidance
Receive guidance on identifying relevant openings, preparing applications and understanding entry-level career requirements.
Career Counselling
Explore suitable job roles, additional learning pathways and possible next steps after training.
Our commitment: We help learners become better prepared for the job market. Interviews, internships, referrals and employment depend on eligibility, performance and actual employer opportunities.
Example learning journeys
Learn. Practise. Explore new possibilities.
Explore example machine learning projects and learning journeys.
Example learning journey01
Student Success Story 1
Python & Data Analysis Learner
The practical exercises helped me understand how Python can be used to clean data, visualize patterns and solve analytical problems.
Practice focus: Python, data cleaning and visualization.
Example learning journey02
Student Success Story 2
Machine Learning Project Learner
Working on a classification project gave me a clearer understanding of model training, evaluation and the importance of quality data.
Practice focus: Classification, evaluation and data quality.
Example learning journey03
Student Success Story 3
Career Development Learner
Building and explaining my project helped me practise technical communication and prepare for discussions about machine learning.
Practice focus: Project presentation and technical communication.
Example learning journey04
Student Success Story 4
Data Science Learner
Learning to work with datasets and compare model performance helped me understand the practical workflow of a data science project.
Practice focus: Datasets, model comparison and project workflow.
Data-Driven Learning, Delivered with Quality
Practical Training with a Focus on Real Skills
Our learning approach emphasizes understanding, experimentation, practical application and continuous improvement rather than memorizing code.
Our learning approach
Hands-On Coding
Practise programming and model development through guided demonstrations and individual exercises.
Realistic Datasets
Work with suitable datasets to understand the challenges of incomplete, inconsistent and complex data.
Mentor-Guided Learning
Build confidence through explanations, doubt-clearing sessions, project feedback and learning support.
Assessment & Improvement
Evaluate progress through assignments, quizzes, code reviews and practical demonstrations.
Responsible AI Practices
Understand data privacy, bias, model limitations and the importance of validating predictions before real-world use.
Meet Your Mentors
Learn from Industry Experts and Experienced Professionals.
Mr. P. PRAKASH RAJ
Software Developer
Mrs. S. SIVANISHA
IT Trainer
Mr. S. MOHAMED RIYAS KHAN
UI & UX Designer
Mr. S. SABARINATHAN
AI AnalystFrequently Asked Questions
Everything You Need to Know Before Enrolling
Who can join the Machine Learning with Python course?
Students, graduates, working professionals, and aspiring AI entrepreneurs interested in practical Python and machine learning can enquire. Ask admissions about suitability for your background and the entry level of the current batch.
Do I need prior Python programming experience?
Python fundamentals are included in the curriculum. Prior experience can help with later modules; confirm whether the selected track supports complete beginners and what preparation is recommended.
What are the eligibility requirements?
Confirm the approved eligibility requirements with E Careerpluz. Basic computer familiarity and a willingness to practise programming and mathematical concepts are useful; any formal qualifications or prerequisites depend on the selected track.
What is the duration of the course?
The course duration is 90 hours. Contact admissions for batch dates and the detailed timetable.
Will I work on real-world projects?
These practice projects cover house prices, customer segmentation, spam classification, movie recommendations, and customer churn using suitable real or realistic datasets. Confirm project scope, data access, and assessment arrangements for the current batch. Real business data requires permission and privacy safeguards.
Which tools and programming languages will I learn?
Python is the main language. The tools include NumPy, Pandas, Scikit-learn, Jupyter or Colab, Matplotlib and Seaborn, Git and GitHub, and introductory TensorFlow and Keras where appropriate. Additional tools such as SQL, XGBoost, Streamlit, Flask, FastAPI, and OpenCV depend on the selected track.
Will I receive a certificate after completing the course?
Confirm the certificate issuer, attendance requirements, assessment criteria, and completion terms with E Careerpluz before enrolling. No third-party accreditation or external awarding-body recognition is implied by the reference images.
Does E Careerpluz provide placement assistance?
Career guidance includes resume and portfolio support, mock interviews, project presentation, application guidance, and counselling. Employment, interviews, internships, and referrals depend on eligibility, performance, and available opportunities.
Can I take this course online or offline?
Ask admissions which online or offline formats are available for the current batch, including practical support, session timings, equipment requirements, and access to learning resources.
What career opportunities are available after this course?
Relevant paths may include machine learning engineering, data science, data or BI analysis, and AI solutions work. Suitability depends on qualifications, experience, technical depth, portfolio quality, and employer requirements.
Can I build my own AI or analytics business?
These skills can support analytics, forecasting, segmentation, recommendation systems, consulting, and custom ML services. Business success depends on practical experience, client needs, implementation quality, data permissions, costs, and applicable regulations.
How do I enrol in the course?
Use the course enquiry form or talk to an E Careerpluz advisor about your background and goals. Confirm eligibility, duration, fees, delivery, software access, certification terms, and admission steps before making a payment.
What is the course fee, including GST?
Course fee: INR 18,000 + 18% GST (INR 3,240). Total including GST: INR 21,240. Confirm exclusions, materials, assessments and payment options with admissions.
Build your next chapter in machine learning.
Confirm course duration, fees, upcoming batch, learning mode, and final curriculum with our team.
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