Machine Learning Fundamentals Training

Learn machine learning principles with in-depth practical exposure to how projects are implemented at organizations in this machine learning course. You learn all about real-world applications of ML using Python & the essentials of statistics and ML models with expert guidance from experienced industry mentors.

Our ML training includes Cloudlabs integration so you gain hands-on experience working with Python libraries. Create supervised learning models using regression, random forest classification, SVM and Naïve Bayes classifiers. Develop unsupervised learning models using k-means clustering and association rule learning. Equip yourself with the skills needed to build ML models from Day 1.

• Applications of Machine Learning
  • Web Search Ranking
  • Ecommerce
  • Weather forecast
  • Malware stop/Anti-virus
  • Anti-spam
  • Natural Language Processing in search engine
  • Face detection
  • Speech Recognition
• Jumpstart Python Programming
  • Install Anaconda Bundle
  • Starting Jypyter Notebook
  • Data Types in Python - Integer, Float, String
  • Data Structures in Python
  • Control Flow in Python
  • Functions in Python
  • Importing libraries
  • Quickstart NumPy
  • Quickstart Pandas
  • Quickstart Matplotlib
  • Quickstart Scikit-Learn
  • Quickstart Seaborn
• Quick Stats for Machine Learning
  • Descriptive Statistics
  • Distributions
  • Hypothesis Testing
  • Maximum Likelihood
• Supervised Learning
  • Usecases of Supervised Learning
  • Classification
  • Linear Regression
  • Random Forest Classification
  • Support Vector Machines (SVM) Classification
  • Naive Bayes
• Unsupervised Learning
  • Usecases of Unsupervised Learning
  • k-Means Clustering
  • Apriori - Association Rule Learning
• Reinforcement Learning
  • Use cases of Reinforcement Learning
• Key Elements of Machine Learning
  • Representation
  • Evaluation
  • Optimization
• Types of Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning