About this Course

Unlock the full potential of data with our comprehensive Machine Learning using Python course. Learn from 40+ hours of applied learning, interactive labs, and real-world projects. Gain in-demand skills in supervised/unsupervised learning, regression, classification, time series modeling, and more using Python. This course is ideal for professionals seeking to upskill and transition into roles like machine learning engineer or data scientist. With dedicated mentorship, lifetime access to learning materials, and a focus on experiential learning, you’ll be well-prepared for a successful career in machine learning.

Outline

Lesson 01: Course Introduction

  • Overview and learning objectives
  • Introduction to Python packages for machine learning

Lesson 02: Introduction to Machine Learning

  • Types of Machine Learning: Supervised, Unsupervised
  • Machine Learning pipeline, MLOps

Lesson 03: Supervised Learning

  • Supervised Learning algorithms
  • Data preparation, detecting overfitting/underfitting

Lesson 04: Regression and Applications

  • Linear, Logistic, Polynomial, Ridge, LASSO regression
  • Model building and performance evaluation

Lesson 05: Classification and Applications

  • Classification algorithms: Naive Bayes, KNN, Decision Trees
  • Random Forest, Support Vector Machines (SVM)

Lesson 06: Unsupervised Algorithms

  • Clustering techniques: K-means, Hierarchical Clustering
  • Outlier detection, Principal Component Analysis (PCA)

Lesson 07: Ensemble Learning

  • Bagging, Boosting, Stacking methods
  • Hands-on with TensorFlow and Keras

Lesson 08: Recommender System

  • Collaborative Filtering: User-based, Item-based
  • PyTorch for building recommendation engines

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