AI & Machine Learning with Python
Free
Learn the essential foundations of AI: the programming tools (Python, NumPy, PyTorch), the math (calculus and linear algebra), and the key techniques of neural networks (gradient descent and backpropagation).
Training Duration – 6 Days
Every Day Two Module will be covered. Module 1 before Break and Module 2 After Lunch Break.
All Module is having Practical Expects.
Pre-Requisite:
- Knowledge of mathematics and statistics
- Knowledge of any programming language like Python
REGISTER
GROUP OF 3 OR MORE [USD 800 PER PARTICIPANT]
INDIVIDUAL STANDARD PRICE [USD 1000 PER PARTICIPANT]
Course Features
- Lectures 168
- Quizzes 0
- Duration 6 Days
- Skill level All levels
- Language Machine Learning
- Students 15
- Certificate No
- Assessments Yes
You have 6 days remaining for the course
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Day 1 : Module 1
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1. Pre training test
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2. Introduction to Machine Learning
Machine Learning vs Statistics vs Data Science vs Data Engineering vs Data Analysis. Real world scenarios to understand the perspective History, Pioneers and Modern Trends
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3. Introduction to probability and statistics - Very brief overview
Probability
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Statistics
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Day 1 : Module 2
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4. Introduction to Python – Very brief overview
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Day 2 : Module 1
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• Random Variables
- • Defnition
- • Discrete random variables
- • Continuous random variables
- • Conditioning on an event
- • Functions of random variables
- • Generating random variables
- • Multivariate Random Variables
- • Discrete random variables
- • Continuous random variables
- • Joint distributions of discrete and continuous variables
- • Independence
- • Functions of several random variables
- • Generating multivariate random variables
- • Rejection sampling
- • Expectation
- • Expectation operator
- • Mean and variance
- • Covariance
- • Conditional expectation
- • Proofs
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Day 2 : Module 2
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Day 3 : Module 1
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Day 3 : Module 2
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Day 4 : Module 1
Part 1: Data Preprocessing
- • Importing the Dataset
- • Missing Data
- • Categorical Data
- • WARNING – Update
- • Splitting the Dataset into the Training set and Test set
- • Feature Scaling
- • Linear Regression • Assumptions of Linear Regression
- • Simple Linear Regression
- • Dataset + Business Problem Description
- • Simple Linear Regression Intuition
- • Simple Linear Regression in Python
- • Multiple Linear Regression
- • Multiple Linear Regression Intuition
- • Prerequisites: What is the P-Value?
- • Multiple Linear Regression in Python –
- • Multiple Linear Regression in Python – Backward Elimination – Preparation
- • Multiple Linear Regression in Python – Automatic Backward Elimination
- • Polynomial Regression
- • Polynomial Regression Intuition
- • Polynomial Regression in Python
- • Python Regression Template
- • Multiple Linear Regression Cross validation
- • Tuning and hyper parameter selection
- • Problems with Linear Regression – multi-collinearity
- • Regularization – Ridge, Lasso, Elastic Net for robustness Explore a few dataset from UCI ML library using python
- Apply Linear Regression to predict the demand of Electricity Power Demand
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Day 4 : Module 2
- • Support Vector Regression (SVR)
- • SVR Intuition
- • SVR in Python
- • Decision Tree Regression
- • Decision Tree Regression Intuition
- • Decision Tree Regression in Python
- • Random Forest Regression
- • Random Forest Regression Intuition
- • How to get the dataset
- • Random Forest Regression in Python
- • Evaluating Regression Models Performance
- • R-Squared Intuition
- • Adjusted R-Squared Intuition
- • Evaluating Regression Models Performance
- • Interpreting Linear Regression Coefficients
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Day 5 : Module 1
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Part 3: Classification
What are classification problems and types of classification algorithms Decision Tree Classification
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Classification using Random Forrest
Logistic Regression
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Day 5 : Module 2
Support Vector Classifiers Compare algorithms
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6. Feature Engineering
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Day 6 : Module 1
- • K-Nearest Neighbors (K-NN)
- • K-Nearest Neighbor Intuition
- • K-NN in Python
- • Support Vector Machine (SVM)
- • SVM Intuition
- • SVM in Python
- • Kernel SVM
- • Kernel SVM Intuition
- • Mapping to a higher dimension
- • The Kernel Trick
- • Kernel SVM in Python
- • Naive Bayes
- • Bayes Theorem
- • Naive Bayes Intuition
- • Naive Bayes in Python
- • Decision Tree Classification
- • Decision Tree Classification Intuition
- • Decision Tree Classification in Python
- • Random Forest Classification
- • Random Forest Classification Intuition
- • Random Forest Classification in Python
- • Evaluating Classification Models Performance
- • False Positives & False Negatives
- • Confusion Matrix
- • Accuracy Paradox
- • CAP Curve
- • CAP Curve Analysis
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Part 4: Clustering
- • K-Means Clustering
- • K-Means Clustering Intuition
- • K-Means Random Initialization Trap
- • K-Means Selecting The Number Of Clusters
- • K-Means Clustering in Python
- • Hierarchical Clustering
- • Hierarchical Clustering Intuition
- • Hierarchical Clustering How Dendrograms Work
- • Hierarchical Clustering Using Dendrograms
- • HC in Python
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Day 6 : Module 2
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Part 5: Association Rule Learning
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Part 6: Reinforcement Learning
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Part 7: Natural Language Processing
