1 year of e-learning access, 15 hrs of e-learning content designed by industry experts
Machine Learning is a form of artificial intelligence. It is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning
✔ 15 hrs of e-learning content
✔ Real life applications and use cases explained
✔ Real life examples and demo included
✔ 1 year Access
• Learn about major applications of Artificial Intelligence across various use cases in various fields like customer service, financial services, healthcare, etc
• Implement classical Artificial Intelligence techniques such as search algorithms, neural networks, tracking Ability to apply AI techniques for problem-solving and explain the limitations of current Artificial Intelligence techniques
• Formalise a given problem in the language/framework of different AI methods such as a search problem, as a constraint satisfaction problem, as a planning problem, etc
• Understand the various algorithms of Machine Learning including Clustering, Decision Tree Algorithm, Random Forest, Logistic Regression, Linear Regression, Support Vector Machine and Naïve Bayes Classifier
• Design intelligent agents to solve real-world problems which are search, games, machine learning, logic constraint satisfaction problems, knowledge-based systems, probabilistic models, agent decision making
• Master TensorFlow by understanding the concepts of TensorFlow, the main functions, operations and the execution pipeline
• Acquire a deep intuition of Machine Learning models by mastering the mathematical and heuristic aspects of Machine Learning
• Implement Deep Learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
• Comprehend and correlate between theoretical concepts and practical aspects of Machine Learning
• Master and comprehend advanced topics like convolutional neural networks, recurrent neural networks, training deep networks, high-level interfaces
1.1 Real life applications of Machine Learning
1.2 Challenges before Machine Learning
1.3 How did Machine Learning evolve?
1.4 Why Machine Learning / Machine Learning benefits
1.5 What is Machine Learning?
1.6 Types of Machine Learning ( Supervised, Unsupervised & Reinforcement Learning )
1.7 Machine Learning algorithms
1.8 Breakthroughs in Machine Learning
1.9 Machine Learning future
1.10 Machine Learning career
1.11 Machine Learning job trends
2.1 Life without Machine Learning
2.2 Life with Machine Learning
2.3 What is Machine Learning
2.4 Machine Learning Process
2.5 Types of Machine Learning
2.6 Supervised Vs Unsupervised
2.7 The right Machine Learning solutions
2.8 Machine Learning Algorithms
2.9 Use case - Predicting the price of a house using Linear Regression
3.1 Why Machine Learning?
3.2 Applications of Machine Learning
3.3 How does Machine Learning work?
3.4 Machine Learning Workflow
3.5 Steps to download Anaconda
3.6 Types of Machine Learning
3.7 Linear Regression Demo
3.8 K-Means Clustering Demo
3.9 Use Case - Weather Analysis
4.1 Virtual personal assistants
4.2 Traffic predictions
4.3 Social media personalization
4.4 Email spam filtering
4.6 Search engine result refining
4.7 Online fraud detection
4.8 Stock market trading
4.9 Assistive medical technology
4.10 Automatic translation
5.1 Artificial Intelligence example
5.2 Machine Learning example
5.3 Deep Learning example
5.4 Human vs Artificial Intelligence
5.5 How Machine Learning works
5.6 How Deep Learning works
5.7 AI vs Machine Learning vs Deep Learning
5.8 AI with Machine Learning and Deep Learning
5.9 Real-life examples
5.10 Types of Artificial Intelligence
5.11 Types of Machine Learning
5.12 Comparing Machine Learning and Deep Learning
5.13 A glimpse into the future
1.1 Real world applications of Machine Learning
1.2 What is Machine Learning?
1.3 Processes involved in Machine Learning
1.4 Type of Machine Learning Algorithms
1.5 Popular Algorithms with hands-on demo
1.6 Linear regression
1.7 Logistic regression
1.8 Decision tree and Random forest
1.9 N Nearest neighbor
2.1 Why do we need KNN?
2.2 What is KNN?
2.3 How do we choose the factor 'K'?
2.4 When do we use KNN?
2.5 How does KNN algorithm work?
2.6 Use case - Predict whether a person will have diabetes or not
3.1 Types of Machine Learning?
3.2 What is K Means Clustering?
3.3 Applications of K Means Clustering
3.4 Common distance measure
3.5 How does K Means Clustering work?
3.6 K Means Clustering Algorithm
3.7 Demo In Python: K Means Clustering
3.8 Use case: Color compression In Python
4.1 What is Machine Learning?
4.2 Types of Machine Learning?
4.3 Problems in Machine Learning
4.4 What is Decision Tree?
4.5 What are the problems a Decision Tree Solves?
4.6 Advantages of Decision Tree
4.7 How does Decision Tree Work?
4.8 Use Case - Loan Repayment Prediction
5.1 What is Machine Learning?
5.2 Applications of Random Forest
5.3 What is Classification?
5.4 Why Random Forest?
5.5 Random Forest and Decision Tree
5.6 Use case - Iris Flower Analysis
6.1 What is Supervised Learning?
6.2 What is Classification? What are some of its solutions?
6.3 What is Logistic Regression?
6.4 Comparing Linear and Logistic regression
6.5 Logistic regression applications
6.6 Use case - Predicting the number in an image
7.1 Introduction to Machine Learning
7.2 Machine Learning Algorithms
7.3 Applications of Linear Regression
7.4 Understanding Linear Regression
7.5 Multiple Linear Regression
7.6 Use Case - Profit estimation of companies
8.1 What is Machine Learning?
8.2 Why support vector machine?
8.3 What is support vector machine?
8.4 Understanding support vector machine
8.5 Advantages of support vector machine
8.6 Use case in Python
9.1 What is Naive Bayes?
9.2 Naive Bayes and Machine Learning
9.3 Why do we need Naive Bayes?
9.4 Understanding Naive Bayes Classifier
9.5 Advantages of Naive Bayes Classifier
9.6 Demo - Text Classification using Naive Bayes