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Introduction to AI-ML

17999.00 INR 19999.00 INR

Introduction to AI-ML

You will understand how machines learn and how algorithms can be trained to make predictions, types of Machine Learning algorithms and decisions based on data.

Course Information

Welcome to the Introduction to AI/ML certification course! In this course, you will learn the fundamentals of Artificial Intelligence and Machine Learning. You will understand how machines learn and how algorithms can be trained to make predictions and decisions based on data. You will also learn about different types of Machine Learning algorithms, such as supervised, unsupervised, and reinforcement learning. The course is designed to benefit you in several ways. First, AI and ML are rapidly becoming critical fields for businesses to remain competitive. By taking this course, you will gain valuable knowledge and skills that can be applied in a variety of fields such as healthcare, finance, and manufacturing. Second, you will learn how to analyze data and make predictions, which is a highly sought-after skill in the current job market. Finally, the course will provide you with real-world learning opportunities through practical exercises and projects. You will learn how to apply your knowledge to solve real-world problems and build predictive models. This will help you develop your programming skills and give you the confidence to take on challenging projects. By the end of this course, you will have a solid foundation in AI/ML and be well-equipped to apply your skills to real-world problems.

    Course Content

  • Module 1

    Probability Introduction Part1 (00:16:42)

    Probability Introduction Part 2 (00:08:16)

    Probability Introduction Part 3 (00:13:15)

    Sample Space (00:17:51)

    Distributions 1 (00:03:58)

    Distributions 2 (00:04:50)

    Total probability rule Concept (00:01:44)

    conditional probability Concept (00:02:16)

    Bernoulli process Concept (00:02:02)

    Test (00:00:00)

  • Module 2

    General Random variable (00:08:38)

    Discrete Random Variable (00:17:58)

    Variance (00:12:38)

    Covariance and Correlation part 1 (00:08:22)

    Covariance and Correlation part 2 (00:05:43)

    Random variable Concept (00:01:39)

    covariance and correlation Concept (00:02:16)

    Module 2 Test (00:00:00)

  • Module 3

    Inequalities and Law of Large Numbers (00:14:33)

    The Central Limit theorem part1 (00:08:44)

    The Central Limit theorem part2 (00:05:46)

    Module 3 Test (00:00:00)

  • Module 4

    Point Estimate part1 (00:09:39)

    Point Estimate part2 (00:03:11)

    Maximum likelihood Estimation (00:05:32)

    Interval Estimate part1 (00:39:16)

    Interval Estimate part2 (00:08:41)

    Hypothesis testing Concept (00:01:47)

    Hypothesis_Testing_1 (00:12:23)

    Interval Estimation_AI_ML Concept (00:02:29)

    Hypothesis_Testing_2 (00:16:20)

    Hypothesis_Testing_3 (00:08:23)

    Hypothesis_Testing_4 (00:13:28)

    Hypothesis_Testing_5 (00:17:02)

    Module 4 Test (00:00:00)

  • Projects

    Projects_1 (00:16:41)

    Projects_2 (00:13:02)

    Projects_3 (00:13:45)

  • Module 5

    Introduction (00:10:33)

    What is Machine Learning (00:12:56)

    Supervised Learning (00:20:17)

    Unsupervised Learning (00:13:06)

    Introduction (00:07:15)

    linear Regression Hypothesis Function (00:16:19)

    Linear Regression Cost Function (00:09:56)

    Linear Regression Gradient Descent (00:12:56)

    Linear Regression Gradient Descent part 2 (00:09:28)

    Recap Linear Regression (00:09:28)

    Linear regression Practical Code (00:20:17)

    Linear regression Practical Code 2 (00:10:13)

    Linear Algebra Review (00:17:59)

    Matrix-Vector Mulitplication (00:18:59)

    Linear regression with Multiple Variables (00:15:22)

    Linear regression with Multiple Variables (Gradient Descent) (00:11:31)

    Feature Normalization (00:13:08)

    Linear regression with Multiple Variables Code (00:16:01)

    Polynomial Regression (00:15:52)

    Normal Equation (00:13:56)

    Linear Regression (concept) (00:02:00)

    Linear Algebra Concept (00:02:00)

    Module 5 Test (00:00:00)

    Polynomial Regression-2 (00:08:11)

    Supervised and Unsupervised learning Concept (00:02:14)

    Assignment Module5 (00:00:00)

  • Module 6

    Logistic Regression and Binary classification (00:20:59)

    Decision Boundary (00:21:47)

    Multiclass Classification (00:14:33)

    Multiclass Classification Problems Code (00:10:57)

    Binary Classification Problem Steps (00:19:59)

    Regularization (00:17:53)

    Regularized Logistic Regression (00:03:05)

    Cost Function (00:11:36)

    Housing price Example (00:07:27)

    Non-Linear Hypotheses (00:10:30)

    Neurons and the brain (00:22:27)

    Neural Networks-Model Representation-1 (00:03:08)

    Logistic Regression Concept (00:02:00)

    Multiclass Classification Concept (00:02:00)

    Binary Classification Concept (00:02:00)

    Module 6 TEST (00:00:00)

    Multiclass Classification (00:10:23)

    Assignment Module6 (00:00:00)

  • Module 7

    Neural Networks Model representation-1 (00:12:09)

    Neural Networks-Model Representation-1 (00:07:55)

    Neural Networks-Model Representation 2 (00:26:25)

    Neural Networks Example (00:08:21)

    Example for Neural Network (00:17:41)

    Keras Model (00:12:58)

    Kearas Model Example (00:16:22)

    Keras Model Example2 (00:11:04)

    Neural network Code (00:03:10)

    Example (00:09:53)

    Sigmoid Function (00:06:26)

    Derivative (00:12:45)

    Sigmoid Function Example (00:21:31)

    Train Function (00:11:07)

    Prediction Function (00:13:00)

    Multiclass Classification in Neural Network (00:09:17)

    Multiclass Classification (00:12:11)

    Cost function Changes (00:15:19)

    Advice for Applying Machine Learning (00:16:07)

    Model Selection (00:15:18)

    Diagnosing Bias vs Variance (00:14:58)

    Regularization in multiclassification (00:17:52)

    Practical coding (00:13:52)

    practical Coding 2 (00:15:22)

    Practical Coding 3 (00:12:22)

    Practical Coding 4 (00:16:11)

    K-Means Algorithm (00:14:10)

    K-Means algorithm (Contd) (00:16:57)

    K-Means algorithm Features (00:08:43)

    Optimization of K-Means Algorithm (00:16:41)

    K-Means steps (00:18:32)

    K-Means Practical Code (00:24:02)

    K-Means Practical Code (Contd) (00:15:06)

    K Means Algorithm Concept (00:02:00)

    Module 7 test (00:00:00)

    Debugging Learning Algorithm (00:10:45)

    Error Analysis (00:16:26)

    Feed Forward (00:21:34)

    Assignment Module7 (00:00:00)

  • Module 8

    Example 1 (00:10:56)

    Example 2 (00:15:37)

    Anomaly Detection (00:18:21)

    Gaussian Distribution (00:12:17)

    Developing & Evaluating Anomaly Detection System (00:14:48)

    Developing & Evaluating Anomaly Detection System-2 (00:22:27)

    Multivariate Gaussian Distribution (00:07:38)

    Multivariate Gaussian Distribution Example (00:09:52)

    Learning With Large Datasets (00:13:52)

    Mini batch Gradient Discent (00:17:54)

    Online Learning (00:21:31)

    TEST (00:00:00)

    Assignment Module8 (00:00:00)

  • Final Certification Examination

    Final Test (00:00:00)

Authors

Rajesh Mayya Profile

Rajesh Mayya

Rajesh Mayya been driven by an insatiable curiosity and a passion for innovation and journey into the world of technology . he is into tech stuff. He got Master's in Engineering, focusing on Artificial Intelligence and Machine Learning. he loves figuring out how things work in the tech world. During his studies, he explored image classification using deep learning . It's not just about grades for him; he wanted to use what he has learned to make a difference in the real world. In the fast-paced world of tech, he believed in making things simple and user-friendly. It's not just about the code; it's about creating solutions that people can actually use and enjoy. He believes that learning is a lifelong adventure. Whether it's staying updated with the latest tech trends or picking up a new skill, he is always eager to expand his knowledge and stay ahead of the curve. In the fast-paced world of tech, he believed in making things simple and user-friendly.
Agrim Jain Profile

Agrim Jain

Agrim Jain, from the Indian Institute of Technology (IIT) Dharwad has taken up the challenge of teaching AI & ML at Bit Byte Binary. His passion for exploring cutting-edge technology and contributing to the field in innovative ways has led him to become a budding machine learning enthusiast. Agrim firmly believes that machine learning is a significant field that has the potential to revolutionise the world we live in today. This area of artificial intelligence (AI) focuses on training machines to learn from data and make predictions or decisions, making a significant impact across various sectors from healthcare to finance to education. Through his videos, Agrim aims to spread awareness and educate people about the vast possibilities that exist in this field. He plans to take his viewers on a journey of discovery, exploring the latest advancements and real-world applications of machine learning. By demonstrating key concepts and techniques in a simple and understandable way, Agrim hopes to inspire more people to become interested in this field and help them understand its significance.