• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-Learn (Sklearn)

Data Loading and Preparation

• How Pandas works in EDA
• Descriptive Statistics, Quartile Analysis
• Sort, Merge, Join (Preparing data at the lowest granularity)
• Indexing and Slicing
• Pivot Table, Aggregate, and Cross Tab

Data Cleaning

• Handling missing values, Converting Categorical Features
• Creating Dummy variables, Feature Engineering/Selection
• Adding New features, Variable transformations

Data Visualization

• Insights from Data Summary

Fundamentals

• Statistical Modeling vs. Machine Learning
• Types of Algorithms
• Scope, Optimal Fit vs. Under/Over-Fitting
• Variance and Bias
• Cost and Loss Functions
• Ensemble Algorithms / Methods

Machine Learning Algorithms – I

• Linear Regression (Simple and Multiple)
• Regularization Techniques
• Non-Linear Regression
• Logistic Regression
• Time Series Analysis – Forecasting
• Model Validation and Deployment

Machine Learning Algorithms – II

– Decision Trees

• Types, Regression vs. Classification Tree
• Avoiding Over-fitting, Constraints, Pruning
• Entropy, Gini, Chi-Square, Information Gain
• Advantages
• Bagging Techniques
• Ensemble Methods, Need for Bagging
• Random Forest Algorithm
• Implementation, Fine-Tuning, Hyperparameter Tuning
• Variable Importance, Visualization

Machine Learning Algorithms – III

• Boosting Techniques
• Types: Gradient Boosting Machine (GBM), LightGBM, Extreme Gradient Boosting Machine (XGBM)
• Implementation in Classification and Regression

 Machine Learning Algorithms – IV

• Support Vector Machines
• Naïve Bayes
• KNN
• Unsupervised Algorithms
• Clustering (K-Means)
• Association Rules (Apriori Algorithm)
  
  
  

Overview

• Use and Application Areas
• Defining Neural Networks
• Types of Artificial Neural Networks
• Activation Functions: ReLU, Sigmoid, Tanh

Tools

• Tensorflow, Keras

  Deep Learning Algorithms

• Recurrent Neural Networks (RNN)
• Convolutional Neural Networks (CNN)
• Convolutional, Pooling, and Fully-Connected Layers