Step by Step Guide to Machine Learning
WHO CAN ACCESS THIS COURSE :-
- Beginners who want to become a data scientist
- Software developers who want to learn machine learning from scratch
- Python developers who are interested to learn machine learning
- Professionals who want to start their career in Machine Leaning
WHAT ARE YOUR REQUIREMENTS FOR THIS COURSE ?
- Basic knowledge of scripting and programming
- Basic knowledge of python programming
WHAT YOU ARE GOING TO LEARN FROM THIS COURSE ?
- Figure out how to utilize NumPy to do quick numerical counts in AI.
- Realize what is AI and Information Fighting in AI.
- Figure out how to utilize scikit-learn for information preprocessing in AI.
- Learn diverse model determination and highlight choices methods in AI.
- Find out about group examination and abnormality location in AI.
- Find out about SVMs for order, relapse and exceptions identification in AI.
If you are hoping to begin your vocation in AI then this is the course for you.
This is a course planned so that you will get familiar with all the ideas of AI directly from fundamental to cutting edge levels.
This course has 5 sections as given below:
- Introduction & Data Wrangling in machine learning
- Linear Models, Trees & Preprocessing in machine learning
- Model Evaluation, Feature Selection & Pipelining in machine learning
- Bayes, Nearest Neighbours & Clustering in machine learning
- SVM, Anomalies, Imbalanced Classes, Ensemble Methods in machine learning
For the code clarified in each talk, you can discover a GitHub interface in the assets segment.
- 5 sections • 16 lectures • 6h 49m total length
Introduction to Machine Learning & Data Wrangling3 lectures • 1hr 22min
- Black Box Introduction to Machine LearningPreview18:30
- Essential NumPy22:25
- Essential Pandas for Machine Learning41:05
Linear Models, Trees & Preprocessing3 lectures • 2hr 14min
- Direct Models for Relapse and Classification51:35
- Pre-Preparing Strategies utilizing scikit48:11
- Choice Trees34:38
Model Evaluation, Pipelining 3 & Feature Selection lectures • 1hr 7min
- Model Selection & Evaluation29:55
- Feature Selection Techniques14:49
- Composite Estimators using Pipelines & FeatureUnions22:58
Bayes, Nearest Neighbours & Clustering3 lectures • 1hr 1min
- Naive Bayes20:24
- Nearest Neighbors19:55
- Cluster Analysis20:40
SVM, Anomalies, Imbalanced Classes, Ensemble Methods4 lectures • 1hr 4min
- Anomaly Detection17:41
- Handling Imbalanced Classes13:17
- Support Vector Machine16:42
- Ensemble Methods16:39