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Duration: 250Hours
Category: Data Science Courses
Type: Application Development
Topics and modules covered in Machine Learning
Python Libraries
Applications
Life Cycle
Required Skills
Implementation
Challenges & Common Issues
Limitations
Data Structure
Artificial Intelligence
Neural Networks
Deep Learning
Getting Datasets
Categorical Data
Data Loading
Data Understanding
Data Preparation
Models
Supervised Learning
Unsupervised Learning
Semi-supervised Learning
Reinforcement Learning
Supervised vs. Unsupervised
Data Visualization
Histograms
Density Plots
Box and Whisker Plots
Correlation Matrix Plots
Scatter Matrix Plots
Statistics
Mean, Median, Mode
Standard Deviation
Percentiles
Data Distribution
Skewness and Kurtosis
Bias and Variance
Hypothesis
Regression Analysis
Linear Regression
Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Classification Algorithms
Logistic Regression
K-Nearest Neighbors (KNN)
Na?ve Bayes Algorithm
Decision Tree Algorithm
Support Vector Machine
Random Forest
Confusion Matrix
Stochastic Gradient Descent
Clustering Algorithms
Centroid-Based Clustering
K-Means Clustering
K-Medoids Clustering
Mean-Shift Clustering
Hierarchical Clustering
Density-Based Clustering
DBSCAN Clustering
OPTICS Clustering
HDBSCAN Clustering
BIRCH Clustering
Affinity Propagation
Distribution-Based Clustering
Agglomerative Clustering
Dimensionality Reduction
Feature Selection
Feature Extraction
Backward Elimination
Forward Feature Construction
High Correlation Filter
Low Variance Filter
Missing Values Ratio
Principal Component Analysis
Reinforcement Learning Algorithms
Exploitation & Exploration
REINFORCE Algorithm
SARSA Reinforcement Learning
Actor-critic Method
Monte Carlo Methods
Temporal Difference
Deep Reinforcement Learning
Deep Reinforcement Learning Algorithms
Deep Q-Networks
Deep Deterministic Policy Gradient
Trust Region Methods
