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
Best Machine Learning Training in Hyderabad

Preview this course

  • Duration 35 Hours
  • Students 21
  • Days 40 Days
  • Resume Preparation Yes
  • Interview Guidance Yes