AI & ML Advance

Artificial Intelligence (AI) has a long history, but is still properly and actively growing and changing. In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI, such as Data Science, Machine Learning, Deep Learning, Statistics, Artificial Neural Networks, Restricted Boltzmann Machine (RBM) and TensorFlow with Python.

Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination.

This Artificial Intelligence course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is going to apply.

Course Pre-Requisites:

• Foremost, a motivation of learning
• Good Knowledge of Mathematics
• A Programming language knowledge
• A Strong Analytical and Logical skills
• Good understanding of complex algorithms
• Basic fundamental knowledge of statistics and modeling
• Comprehensive knowledge of ML and Data Science fundamentals
• Insight view of applied AI and ML models

Learning Path:

• Module 1: Introduction To Deep Learning & AI
• Module 2: Deep Learning: A Revolution In Artificial Intelligence
• Module 3: What Is Deep Learning?
• Module 4: What Is Machine Learning? Analytics Vs Data Science
• Module 5: Data
• Module 6: Big Data
• Module 7: Data Science Deep Dive
• Module 8: Python
• Module 9: Operators And Keywords For Sequences
• Module 10: NumPy & Pandas
• Module 11: Deep Dive–Functions & Classes & Oops
• Module 12: Statistics
• Module 13: Machine Learning, Deep Learning & AI Using Python, Introduction
• Module 14: Clustering
• Module 15: Implementing Association Rule Mining
• Module 16: Understanding Process Flow Of Supervised Learning Techniques, Decision Tree Classifier
• Module 17: Random Forest Classifier
• Module 18: Naive Bayes Classifier
• Module 19: Project Discussion, Problem Statement And Analysis
• Module 20: Linear Regression
• Module 21: Logistic Regression
• Module 22: Support Vector Machines
• Module 23: Time Series Analysis
• Module 24: Machine Learning Project, Machine Learning Algorithms Python
• Module 25: Feature Selection And Pre-Processing
• Module 26: Which Algorithms Perform Best
• Module 27: Model Selection Cross Validation Score
• Module 28: TextMining & NLP
• Module 29: PySpark And MLLib
• Module 30: Deep Learning & AI Using Python, Deep Learning & AI
• Module 31: Introduction To Artificial Neural Networks
• Module 32: Convolutional Neural Networks
• Module 33: What Are RNNs–Introduction To RNNs
• Module 34: Restricted Boltzmann Machine (RBM) And Auto Encoders
• Module 35: TensorFlow With Python
• Module 36: Building Neural Networks Using TensorFlow
• Module 37: Deep Learning Using TensorFlow
• Module 38: Transfer Learning Using Keras And TFLearn