Industry Focused ML Engineer

  • High Demand In The It Industry: Machine learning is used by companies for automation, recommendation systems, fraud detection, customer analysis, sales forecasting, and intelligent applications.
  • Build Intelligent Prediction Systems: Learners understand how machines identify patterns from data and generate accurate predictions for real-world business problems.
  • Strong Career Foundation For Ai And Data Science: Machine learning is a core skill for learners who want to grow in artificial intelligence, data science, analytics, and predictive modeling careers.
3 Months ₹22,999 ₹16,999
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Industry Focused ML Engineer
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Course Overview

Machine Learning is a branch of Artificial Intelligence that enables systems to learn from data and make predictions or decisions without being directly programmed. This 3 months course helps learners build strong ML skills using Python, statistics, data preprocessing, supervised learning, unsupervised learning, feature engineering, model evaluation, hyperparameter tuning, data visualization, and real-world machine learning projects.

Course with Live Project

No Refund Available

Strong Machine Learning Model Building: Learners Build Practical Models For Regression, Classification, Clustering, Prediction, Customer Analysis, And Data-driven Decision-making.

Real Dataset And Algorithm Practice: Work With Python, Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn, Real-world Datasets, Feature Engineering, And Model Evaluation Techniques.

Portfolio-based Ml Project Development: Develop Practical Projects Like Smart Salary Prediction, Retail Demand Prediction, Customer Churn Prediction, Student Placement Prediction, And Forecasting Models.

Course Content

Once you submit your enquiry, our advisor will contact you within 24 hours to guide you through course selection, batch details, and enrollment steps.

  • • Live instructor-led training sessions
  • • Real-world project experience
  • • Certification guidance and support

To successfully complete the course and receive certification, learners must meet the following criteria:

  • • Minimum attendance requirement in live sessions
  • • Successful completion of assigned projects

Currently, there is no refund policy once the enrollment is completed. We recommend speaking with our advisors before enrolling to ensure the course fits your needs.

Skills Developed with Machine Learning Course

Python For Ml: Learn python programming, functions, modules, file handling, oop basics, data structures, and coding logic for machine learning tasks.
Statistics And Mathematics: Understand mean, median, mode, variance, standard deviation, probability, correlation, covariance, linear algebra basics, and data distributions.
Data Preprocessing: Work with missing values, duplicate records, categorical data, outliers, feature scaling, normalization, and dataset cleaning techniques.
Numpy And Pandas: Practice arrays, dataframes, csv handling, filtering, sorting, grouping, merging, transformation, and exploratory data analysis.
Data Visualization: Create charts, graphs, scatter plots, histograms, heatmaps, correlation visuals, and pattern-based reports using matplotlib and seaborn.
Supervised Learning: Learn regression, classification, linear regression, logistic regression, decision trees, random forest, knn, svm basics, and naive bayes.
Unsupervised Learning: Understand clustering, k-means, customer segmentation, dimensionality reduction basics, pca introduction, and hidden pattern discovery.
Feature Engineering: Practice feature selection, feature transformation, encoding techniques, scaling, input preparation, and improving dataset quality.
Model Evaluation And Optimization: Learn train-test split, accuracy, confusion matrix, precision, recall, f1-score, cross validation, overfitting, underfitting, and tuning basics.
Ml Project Development Skills: Practice dataset preparation, model training, testing, performance comparison, documentation, and presenting intermediate-level ml projects.

Career Opportunities after Machine Learning Course

This course opens doors to multiple high-demand career paths across industries.

Machine Learning Intern:

Support ml projects by cleaning datasets, training models, testing outputs, comparing performance, and preparing documentation.

Data Science Intern:

Work on data preprocessing, visualization, prediction models, feature engineering, and ml implementation tasks.

Ml Project Assistant:

Help teams with dataset preparation, feature selection, model testing, result analysis, documentation, and project coordination.

Python Ml Developer Beginner Role:

Build basic prediction systems, classification models, forecasting tools, and data-driven applications using python and scikit-learn.

Predictive Analytics Assistant:

Support forecasting, customer behavior prediction, risk analysis, trend analysis, and business decision-making using ml models.

Why Enroll in Machine Learning with Solitaire Learning?

Beginner-to-intermediate Ml Training: The course starts from python, statistics, and ml fundamentals, then moves toward algorithms, feature engineering, and model evaluation.
Practical Dataset-based Learning: Learners work with real-world datasets and understand machine learning through hands-on implementation and guided practice.
Industry-relevant Tools: The course covers python, numpy, pandas, matplotlib, seaborn, scikit-learn, jupyter notebook, google colab, and model evaluation tools.
Mentor-guided Project Support: Learners receive mentor guidance for concept clarity, coding practice, dataset handling, model building, debugging, and portfolio preparation.
Strong Foundation For Advanced Ml Learning: The course prepares learners for 4 months and 6 months advanced machine learning programs with optimization, deployment, and industry-level projects.

Ready to Take the Next Step in Your Career?

Join our expert-led training program, gain industry-recognized skills, and move closer to your professional goals. Seats are limited — enroll today!

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