Go beyond the buzzwords and learn to build intelligent systems. This Program provides a practical, hands-on introduction to the core concepts of Artificial Intelligence and Machine Learning. You will learn to build models that can recognize patterns, make predictions, and solve real-world problems.

Understand the fundamental difference between AI, Machine Learning, and Deep Learning.
Implement key ML algorithms like Linear Regression, Logistic Regression, and Decision Trees.
Learn about neural networks and the basics of deep learning with TensorFlow or PyTorch.
Work on practical projects in areas like image recognition and natural language processing (NLP).
Understand the ethics and societal impact of AI.

Course Content

Introduction to Python

Python Basics

20:52
Variables and Data types

05:59
Operators and Conditional Statements

19:26
Loops and Functions

12:49

Module 2: Oops

Module 3:Introduction to Numpy, Pandas for Data Handling

Module 4:Data Visualization using Matplotlib & Seaborn

Module 5: Mathematics for AI & ML

Module 6:Linear Algebra: Matrix Operations, Eigenvalues & Eigenvectors

Module 7: Core Machine Learning Introduction

Lesson 1: Introduction to Machine Learning

10:40
Lesson 2: Logistic Regression Classification

01:32
Lesson 3:Sigmoid Function

01:25
Lesson 4: Logistic Regression: Cost Function, Advantages, Disadvantages, and Implementation

02:45
Lesson 5: Decision Tree

02:52
Lesson 6: Decision Trees: Fundamentals, Overfitting, Pruning, and Features

06:49
Lesson 7: Ensemble Methods

02:55
Lesson 8: Random Forest

21:44
Lesson 9: Time Series

15:40

Module 8: Regression Models

Module 9: Classification Algorithms

Module 10: Unsupervised Learning

Module 11: Neural Networks Foundations

Module 12: Deep Learning Architectures

Module 13: Natural Language Processing (NLP)

Module 14: Convolutional Neural Networks

Module 15: Transfer Learning with Pre-trained Models (VGG16)

MINOR PROJECT

A course by

SKILLsFLICK

Artificial Intelligence (AI) & Machine Learning (ML) TRAINING

What Will You Learn?

Material Includes

Requirements

Audience

Course Content

Introduction to Python

Python Basics

Variables and Data types

Operators and Conditional Statements

Loops and Functions

Module 2: Oops

Lesson 1:Introduction

Lesson 2:

Lesson 3:Encapsulation & Abstarction

Module 3:Introduction to Numpy, Pandas for Data Handling

Lesson 1: Introduction To Numpy

Lesson 2: Working with arrays using the NumPy library

Lesson 3: Introduction to Pandas

Lesson 4: Pandas

Module 4:Data Visualization using Matplotlib & Seaborn

Lesson 1: Introduction to Python Visualization

Lesson 2

Lesson 3

Lesson 4

Lesson 5: Google Colab

Lesson 6: Colab part 2

Module 5: Mathematics for AI & ML

Lesson 1: Introduction to Statistics

Lesson 2: Population and sampling

Lesson 3:Inferential Stats

Lesson 4

Module 6:Linear Algebra: Matrix Operations, Eigenvalues & Eigenvectors

Lesson 1: Introduction to Linear Algebra

Lesson 2

Lesson 3

Module 7: Core Machine Learning Introduction

Lesson 1: Introduction to Machine Learning

Lesson 2: Logistic Regression Classification

Lesson 3:Sigmoid Function

Lesson 4: Logistic Regression: Cost Function, Advantages, Disadvantages, and Implementation

Lesson 5: Decision Tree

Lesson 6: Decision Trees: Fundamentals, Overfitting, Pruning, and Features

Lesson 7: Ensemble Methods

Lesson 8: Random Forest

Lesson 9: Time Series

Module 8: Regression Models

Lesson 1

Lesson 2: Linear Regression 2

Lesson 3

Lesson 4

Lesson 5

Module 9: Classification Algorithms

Lesson 1

Lesson 2: Evaluation Metrics

Lesson 3: Random Forest Classifier Implementation and Evaluation

Lesson 4: Hyperparameter Tuning

Module 10: Unsupervised Learning

Lesson 1: K-Means Clustering Fundamentals

Lesson 2: Data Preprocessing and Transformation

Module 11: Neural Networks Foundations

Lesson 1

Lesson 2

Module 12: Deep Learning Architectures

Lesson 1: Neural Networks and Activation Functions in Deep Learning

Lesson 2: Activation Function

Lesson 3: Forward and backward Propagation

Lesson 4

Lesson 5: Popular Optimizers

Lesson 6

Lesson 7: Introduction to NLP

Module 13: Natural Language Processing (NLP)

Lesson 1

Lesson 2

Lesson 3

Lesson 4

Module 14: Convolutional Neural Networks

Lesson 1: Introduction to CNN

Lesson 2: CNN Prediction Accuracy and Verification

Module 15: Transfer Learning with Pre-trained Models (VGG16)