Installation of jupyter Notebook Setup with Python (AI Development
- What Is Artificial Intelligence
- Level Of product
- Introduction Of Machine Learning
- Types of Data Analytics
- Introduction To Statistics
- Application Of Machine Learning
- Machine Learning Cloud Platforms
- Objective Of Machine Learning Algorithm
- Introduction of Deep Learning
- Machine Learning VS Deep Learning
- Application of Deep Learning.
- Introduction to Data Science
- Introduction to Data Analytics
- Difference Between Data Science and Business Intelligence
- Turing Test
- History of Data Science
- Important Scientists to follow in the field of Machine Learning
- Why do we care about Data Science?
- Hands on practice on Python Programming Language
- History Python 2 vs python 3
- Data Types and Data Structure with Python
- Methods and Functions in Python
- Object Oriented Programming In python.
- Python fundamentals and required libraries for the course
- Functions from numpy, matplotlib, scikit-learn and various other libraries
- Graph and Images in python
- Data Analysis
- Hands on practice with Salaries Dataset
- Statistical Analysis
- Data Visualization with Seaborn and Pyplot
- Hands on Practice with Ariflight Dataset.
- What Is Linear Regression?
- Univariate Linear Regression
- Hands on practice with Salary Dataset
- Gradient Descent Algorithm
- Linear Regression Use Cases
- Objectives of Linear Regression
- Multivariate Linear Regression
- One Hot Encoding and Dummy Variable
- Gradient Descent with Multiple Variable
- Hands On practice with Startup Funding Dataset
- Polynomial Regression
- Hands On practice with 50 Startup Dataset
- Linear Regression Assumptions
- What Is Logistic Regression?
- Logistic Regression Features
- Objective of Logistic Regression
- Activation Functions:
1. Sigmoid Function
2. Softmax Function
- Model Evaluation
- Confusion Matrix
1. True Positive and True Negative
2. False Positive , False Negative
3. Accuracy, Precision
4. Recall, F1-Score
- Over Fitting & Generalisation
- Hands On practice with Bank Marketing Dataset
- Hands On practice with Social Network Advertisement Dataset
- Decision Tree Introduction
- Classification Tree
- ID3 Algorithm
2. Information Gain
1. Gini Index
2. Gini Impurity
- C4.5 Algorithm
- Chi-Square Algorithm
- Regression Tree
- CART Algorithm
- Advantages and Disadvantages of Decision Tree
- Hands on Practice of Decision Tree with Breast Cancer Dataset
Hands on Practice of Decision Tree with Social Network Advertise Dataset
Using the Decision Tree Algorithm for analysing a real-life dataset
- What is Bagging?
- How Random Forest Algorithm is a very major upgrade from Decision Tree?
- How does Random Forest handle Missing Data?
- Random Forest as Classifier
- Random Forest as Regressor.
- Random Forest Algorithm using SciKit Learn Library. Calculation of Accuracy and F1 Score
- Comparing Results of Decision Tree and Random Forest Algorithms for
- Breast-Cancer Dataset.
Artificial Neural Network
- What is an Artificial Neural Network?
- Introduction to Neurons
- Single Neuron Model
- Activations Functions:
4. Leaky Relu Functions
- Neural Network Architecture
- Types of Neural Network
- Application, Advantages and Limitations
- Hands on practice of Artificial Neural Network with Churn Modelling Dataset.
Support Vector Machines
- What are Convex Hulls?
- What is a hyperplane?
- What is weight and bias?
- Why is SVM is one of the best classification algorithms till date?
- Support Vector Machine Algorithm Derivation.
- A discussion on techniques to apply support vector classification on linearly
- inseparable datasets. (using Kernels or increasing the dimensionality of the dataset)
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