Best Machine Learning Training

Python Refresher

• Basic Syntax
• Lists
• Tuples
  • Lambda Functions
  • Map, Filter Reduce

NumPy and Array Concepts:

• Introduction
• Pricing
• Supported Regions
• Machine Learning Editions
  • Standard Edition
  • Enterprise Edition
  • Business Critical Edition
  • Virtual Private Machine Learning (VPS)
  Pandas:
  • Introduction to Pandas
  • Pandas Series Vs Data Frames
  • Loading Data with Pandas (csv, xlsx, json etc.)
  • Creating series and data frames
  • Data pre-processing techniques with pandas
  4. Seaborn & Matplotlib:
  • Introduction to Data Visualization
  • Line Plots
  • Dist plots
  • Join Plot
  • Scatter Plot
  • Count plot
  • Heatmap
  • 3d plotting
  • Label title and grid
  Web Scaping
  • What is web scraping
  • Accessing Web Data
  • Introduction to Beautiful Soup
  • Using Beautiful Soup, Requests etc to Scrape data
  • Converting Scraped Data to a Dataset
  EDA (Exploratory Data Analysis):
  • Introduction To EDA
  • Data collection
  • Understanding the Data
  • Filling Missing Values
  • Feature Engineering Techniques
  • Working with text data
  • Preparing Data to be fed into Machine Learning Algorithm
  • Shortlisting Algorithms to apply.
  Machine Learning
  • Supervised Learning
  • Regression (To predict recurring values)
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial and Non-Linear Regression
  • Random forest regressor/ Decision Tree Regressor/ SVM Regressor
  • Classification (To predict class labels)
  • Logistic Regression with Log loss
  • Naïve Bayes theorem and classifier
  • Support Vector Machines and Support Vector Classifiers
  • K-Nearest Neighbours Algorithm with Euclidean Distance
  • Decision Tree and Decision Tree Classifiers (Both in Gini and Entropy)
  • Bagging or Random Forest Classifiers
  • Boosting
  • AdaBoost
  • Gradient Boosting
  • XGBOOST
  • Unsupervised Learning
  • Clustering (To identify similar type of data)
  • K-means Clustering (With mean find the centroids)
  • K-medoid Clustering
  • Hierarchical/Agglomerative Clustering
  • Density Based Clustering (DBSCAN)
  • PCA (Principal Component Analysis)
  • Recommendation Systems
  • Collaborative Recommendation Systems
  • Content Based Recommendation Systems
  Optimizing Machine Learning Work Flow
  • Introduction to ML Workflow
  • Model Selection
  • Overfitting
  • Underfitting
  • Bias-Variance Trade-off
  • Optimization
  • Hyperparameter Optimization Using Randomized search CV
  Pipelines
  • Introduction To Pipelining
  • Setting Up Machine Learning Pipelines
  • Implementing Pipeline
  Evaluation Metrics
  • R Squared
  • RMSE
  • Accuracy Score
  • K Fold Cross Validation
  Projects
  • Basic
  • Breast Cancer Prediction
  • Spam Mail Detection
  • Diabetes Detection
  • Intermediate
  • Movie Recommendation System
  • Realtime Face Detection Using OpenCV
  • Customer Segmentation
  • Advanced
  • Churn Prediction
  • California Houses Price Detection
  • Emotion Recognition Using Open C