Machine Learning Fundamentals

  • High Demand In The It Industry: Machine learning is used by companies for prediction, automation, recommendation systems, fraud detection, customer analysis, and forecasting.
  • Build Prediction-based Applications: Learners understand how machines identify data patterns and generate useful predictions for real-world problems.
  • Strong Foundation For Ai And Data Science: Machine learning is an important skill for learners who want to grow in artificial intelligence, data science, and analytics careers.
1.5 Months ₹11,999 ₹8,999
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Machine Learning Fundamentals
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Course Overview

Machine Learning is a branch of Artificial Intelligence that helps systems learn from data and make predictions or decisions without being directly programmed. This 45 days course helps learners build a strong foundation in Python, statistics, data preprocessing, supervised learning, unsupervised learning, model evaluation, data visualization, and practical ML project development.

Course with Live Project

No Refund Available

Machine Learning Foundation: Learners Understand Ml Concepts, Types Of Machine Learning, Features, Labels, Training Data, Testing Data, And Prediction Workflow.

Python And Dataset Practice: Work With Python, Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn Basics, Data Cleaning, And Real-world Datasets.

Practical Ml Project Development: Develop Projects Like Student Placement Prediction, Employee Attrition Prediction, House Price Prediction, And Basic Classification 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 fundamentals, data types, conditions, loops, functions, file handling, and basic coding logic for ml tasks.
Statistics And Mathematics Basics: Understand mean, median, mode, variance, standard deviation, probability, correlation, and data distribution concepts.
Data Preprocessing: Work with missing values, duplicate records, categorical data, feature scaling basics, and dataset cleaning techniques.
Numpy And Pandas: Practice arrays, dataframes, csv file handling, filtering, sorting, transformation, and basic exploratory data analysis.
Data Visualization: Create charts, graphs, scatter plots, histograms, correlation visuals, and pattern-based data reports.
Supervised Learning: Learn regression, classification, model training, prediction, and accuracy checking using beginner-friendly algorithms.
Unsupervised Learning: Understand clustering concepts, k-means basics, customer grouping, and hidden pattern discovery in datasets.
Model Evaluation: Learn train-test split, accuracy score, confusion matrix basics, precision, recall, overfitting, and underfitting concepts.
Ml Tools Usage: Work with python, jupyter notebook, google colab, numpy, pandas, matplotlib, seaborn, and scikit-learn basics.
Ml Project Development Skills: Practice dataset preparation, model training, testing results, documenting workflow, and presenting beginner-to-intermediate 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 basic models, testing outputs, and preparing project documentation.

Data Science Intern:

Work on data analysis, preprocessing, visualization, basic prediction models, and ml implementation tasks.

Ml Project Assistant:

Help teams with dataset preparation, feature understanding, model testing, result comparison, and documentation.

Python Ml Beginner Role:

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

Predictive Analytics Assistant:

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

Why Enroll in Machine Learning with Solitaire Learning?

Beginner-friendly Ml Training: The course starts from python, statistics, and ml basics, making it suitable for learners starting their ml journey.
Practical Dataset-based Learning: Learners work with real-world datasets and understand ml through hands-on implementation instead of only theory.
Industry-relevant Tools: The course covers python, numpy, pandas, matplotlib, seaborn, scikit-learn, jupyter notebook, and google colab basics.
Mentor-guided Learning: Learners receive mentor support for concept clarity, coding practice, dataset handling, model building, and project development.
Strong Foundation For Advanced Ml Courses: The course builds a solid base before moving into 2 months, 3 months, 4 months, or 6 months machine learning programs.

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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