Machine Learning

Master Python, Data Handling, Machine Learning, Deep Learning, and real-time AI applications through hands-on projects and expert mentorship.

Learning Mode

Online

Real-Time Projects

Hands-On Practical Learning

Expert Mentorship

Guidance from Industry Experts

Career Support

Placement Assistance & Certification

About the Course

The Machine Learning Course by SkillDzire covers Python programming, data handling, exploratory data analysis, statistics, supervised & unsupervised learning, model evaluation, and deployment. Students gain strong hands-on experience through real-world ML and Deep Learning projects, preparing them for careers in AI, Data Science, and Analytics.

Comprehensive Python and ML programming foundation

Hands-on projects using real datasets

Covers ML, DL, AI, and Generative AI concepts

Industry certification and placement guidance

Curriculum

Python Programming for Machine Learning

  • Python syntax, variables, data types
  • Conditional statements and loops
  • Functions, lambda expressions
  • Data structures: Lists, tuples, dictionaries, sets
  • File handling (open, read/write CSV)
  • Object-Oriented Programming basics
  • Working with Jupyter Notebook & Google Colab
  • CSV Reader and Analyzer
  • Contact Book using Dictionary and OOP

SQL for Data Handling in ML

  • SQL and relational database basics
  • SELECT, WHERE, ORDER BY, LIMIT
  • GROUP BY, COUNT, AVG, SUM
  • Filtering with HAVING, DISTINCT
  • JOINs: INNER, LEFT, RIGHT, FULL OUTER
  • Subqueries and Nested Queries
  • Using SQL in Python (SQLite / SQLAlchemy)

Introduction to Machine Learning

  • What is ML? AI vs ML vs DL
  • Types: Supervised, Unsupervised, Reinforcement
  • ML pipeline overview

Introduction to Deep Learning

  • Handwritten Digit Classification
  • CNN and architecture
  • Advanced DL & Computer Vision
  • Object and Face Recognition
  • Optical Character Recognition (OCR)

Exploratory Data Analysis (EDA)

  • NumPy, Pandas, Matplotlib, Seaborn
  • Data cleaning: missing values, duplicates
  • Outlier detection & treatment
  • Feature engineering (intro)
  • Project: Titanic Dataset Analysis
  • Hyperparameter Optimization

Statistics and Probability for ML

  • Descriptive statistics (mean, median, std dev)
  • Probability theory basics
  • Distributions: Normal, Binomial, Poisson
  • Bayes Theorem
  • Hypothesis testing (t-test, chi-square)
  • Central Limit Theorem

Supervised Learning – Regression

  • Simple, Multiple & Polynomial Regression
  • Regularization: Lasso, Ridge
  • Evaluation Metrics: MAE, MSE, RMSE, R²

Supervised Learning – Classification

  • Logistic Regression, KNN
  • Decision Trees, Random Forest
  • Naive Bayes, SVM
  • Model evaluation: Confusion Matrix, Precision, Recall, F1, ROC-AUC

Unsupervised Learning

  • K-Means, Hierarchical, DBSCAN
  • PCA, t-SNE
  • Association Rule Mining (Apriori)

Model Evaluation & Optimization

  • Train/Test Split, Cross-validation
  • Bias-Variance tradeoff
  • GridSearchCV, RandomizedSearchCV
  • Feature scaling: StandardScaler, MinMaxScaler
  • Feature selection: Correlation, RFE
  • Sklearn Pipelines

Ensemble Learning

  • Bagging: Random Forest
  • Boosting: AdaBoost, Gradient, XGBoost
  • LightGBM, CatBoost (Intro)
  • Voting & Stacking

Time Series Forecasting

  • Date-time features & indexing
  • Moving averages, seasonality, decomposition
  • ARIMA, SARIMA models
  • Evaluation metrics: MAPE, SMAPE

Generative AI & LLMs

  • Model Deployment: Pickle, Joblib
  • APIs with Flask / FastAPI
  • Streamlit or Gradio interfaces
  • Deployment platforms: Render, HuggingFace, Heroku
  • Cloud Deployment

Hands-On Real-Time Exposure Tasks

  • Real-Time Object Detection with YOLO
  • Custom Object Detection with YOLOv3/YOLOv8
  • Heart Disease Prediction using ML
  • Kidney Stone Detection using XResNet50
  • Transfer Learning for Kidney Disease Prediction
  • Pothole Detection using ResNet50
  • Movie Recommendation System
  • Image Classification using ResNet50
  • Sentiment Analysis with ML & DL
  • Face Mask Detection using YOLO/ResNet
Download Full Curriculum (PDF)

Projects You Will Work On

Object Detection using YOLO

Implement YOLO models to detect real-time objects in video streams for traffic or retail analysis.

Heart Disease Prediction

Predict heart disease using ML models like Logistic Regression and Random Forest based on health data.

Face Mask Detection

Build a real-time mask detection system using YOLO/ResNet models for safety monitoring.

Student Testimonials

"The Machine Learning course gave me a solid foundation and helped me build real projects."

- Rohan Mehta

"Excellent coverage of both ML and Deep Learning with real-world datasets."

- Priya Nair

"SkillDzire's course helped me transition into a Data Science career with confidence."

- Aditya Sharma