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Machine Learning with Python

Course Description

Step into one of the most in-demand tech careers with the Machine Learning with Python – Certification Training Program, a 22+ hour self-paced, immersive course designed for aspiring data scientists, analysts, and AI developers.

This industry-aligned program helps you build, optimize, and evaluate machine learning models from scratch using Python’s most powerful libraries: Scikit-learn, Pandas, TensorFlow, Keras, PyTorch, and more. You’ll master everything from regression and classification to clustering, deep learning, model evaluation, and real-world deployment techniques.

  What You Will Learn

·       Core ML algorithms: Linear/Logistic Regression, KNN, Decision Trees, SVM, Naive Bayes, Random Forest

·       Advanced techniques: Clustering, PCA, Feature Engineering, Ensemble Models

·       Evaluation metrics: Confusion Matrix, ROC-AUC, Precision/Recall, RMSE, F1 Score

·       Deep learning with Python: ANN, CNN, RNN, GANs, Autoencoders

·       Hands-on implementation using Scikit-learn, TensorFlow, Keras, and PyTorch

·       Real-world ML applications: E-commerce, finance, healthcare, fraud detection, NLP

·       Model deployment basics using Flask + saving/loading models

·       Data preprocessing and advanced feature engineering

·       Concepts in statistics, hypothesis testing, overfitting prevention, and regularization

·       Version control for ML projects (Git basics)

Course Curriculum

  • Foundations of ML & Python Ecosystem

    ·        What is ML? Key concepts & real-world use cases

    ·        Python tools: NumPy, Pandas, Matplotlib, Scikit-learn

    ·        Understanding datasets: Kaggle, UCI, Google, OpenML

    ·        Hands-on: Data exploration & preprocessing

  • Regression Models & Evaluation

    ·        Linear, Polynomial & Ridge Regression

    ·        Evaluation: MSE, RMSE, R², AUC-ROC, Cross-validation

    ·        Regularization (Lasso & Ridge)

    ·        Hands-on: Predict housing prices with Linear Regression

  • Classification Techniques

    ·        Logistic Regression, KNN, Decision Trees, Naive Bayes, SVM, Random Forest

    ·        Model tuning & hyperparameter optimization

    ·        Use-cases: Email spam filtering, loan default prediction

    ·        Hands-on: Customer churn prediction using classification

  • Unsupervised Learning & Pattern Discovery

    ·        Clustering: K-Means, DBSCAN, Hierarchical

    ·        Association rules & Market Basket Analysis (Apriori)

    ·        Hands-on: Market segmentation using clustering

  • Dimensionality Reduction + Feature Engineering

    ·        PCA, LDA, t-SNE (for visualization)

    ·        Handling missing data: Imputation strategies

    ·        Categorical encoding: Label, One-hot, Target

    ·        Outlier detection & normalization

    ·        Hands-on: PCA on credit risk dataset

  • Deep Learning & Neural Networks

    ·        ANN structure: input/output layers, backpropagation

    ·        CNNs: Image classification using TensorFlow & Keras

    ·        RNNs & LSTMs: Time-series + basic NLP

    ·        Intro to GANs, Autoencoders (with examples)

    ·        Hands-on: MNIST digit recognition (CNN)

    ·        Capstone: Build and deploy an LSTM-based sentiment analyzer

  • Model Deployment & Production Basics

    ·        Saving and loading models (Pickle, Joblib, SavedModel)

    ·        Deploying models using Flask APIs

    ·        Overview of MLOps tools: Docker, GitHub, CI/CD basics

    ·        Hands-on: Deploy ML model as REST API locally

  • Metrics, Validation, and Real-World Strategies

    ·        Precision, Recall, F1-Score, Confusion Matrix

    ·        Preventing overfitting with cross-validation and regularization

    ·        A/B testing basics

    ·        Hands-on: Performance analysis with scikit-learn metrics

  • Final Capstone Project + Git Version Control

    ·        Full ML lifecycle: Data → Model → Deployment → GitHub

    ·        Case Study: Predict credit card fraud + deploy API

    ·        Git & GitHub essentials for ML teams

    ·        Submission and feedback tracker provided

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Chronolearn

Developer

I am a web developer with a vast array of knowledge in many different front end and back end languages, responsive frameworks, databases, and best code practices

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Title From Date To Date Cost
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This Course Fee:

₹39,999.00

₹60,000.00
Course includes:
  • img Level
      Beginner Intermediate Expert
  • img Duration 24h
  • img Passing Marks 80
  • img Exam Duration 1h 30m
  • img Certifications Yes
  • img Language
      English German Arabic French Spanish
  • img Access 90 Days
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