Machine Learning (ML): Hands-on Python Course
- 7.5 hours on-demand video
- 15 articles
- 2 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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- Hands-on explanation of every major ML techniques in detail: Supervised, Unsupervised, Reinforcement Learning
- Model Development, Deployment and Monitoring.
- All the source codes are made available to you for your use.
- Regression (Simple, Polynomial, and Multinomial)
- Classification (Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Naive Bayes)
- Ensemble Modeling (Voting Classifier, Bagging, Boosting, Stacking, Random Forest)
- Data Visualization with MatPlotLib and Seaborn
- Use train, test and Cross Validation to choose and tune data
- Feature Engineering (Reduce Noise, Outliers) and Data Preprocessing
- Practical examples of How to trade-off between Bias, Variance, Irreducible errors using Ensemble Learning model and Bagging, Boosting
- Understand how to implement Machine Learning at massive scale
- Understand Tensor Flow and Keras
- Understand math and statistics behind Machine Learning models
Welcome to this course. Let's understand what is Machine learning.
You will learn about relations between Algorithm, Features, Data and Classical Programs, Data Science, Data Mining, and Machine Learning.
In this lecture you will learn about some of the important terms and keywords used in machine learning i.e. Observations, Labels, Features, Predictors, Independent Variable, Target Variable, Predictions, Categorical Variable, Numerical Variables, Feature Matrix, Target Vector.
In this lecture you will learn about different types of machine learning algorithm.
Simple Linear Regression
Multiple Linear Regression
Polynomial Linear Regression
Support Vector Machine (SVM)
Naive Bayes (NB)
K-Nearest Neighbors (KNN)
Trial & Error
Markov Decision Process
In this lecture your will learn about data exploration using Python library Seaborn. You will understand
Data correlation and heat map
Scatter plots and linear regression plots
and other visualizations
In this lecture you are going to do a predictive analysis on building height and number of stories data set using machine learning simple linear algorithm. You will use python provided libraries Numpy, Pandas, Matplot and Sklearn.
In this lecture you will import the mpg data set into pandas dataframe, provide column names, use to_numeric function to change one of the feature values to numeric value and check multcollinearity.
This lecture is continuation of multiple linear regression you are building from previous lecture. In this lecture you are going to use Variance Inflation Factor to get rid of multicollinearity. Then you will draw scatter plot of remaining independent variables using sea-born pair plot.
This lecture is continuation of multiple linear regression you are building from previous lecture. In this lecture you are going to compare p value with t, split the data set in training and test data and run multiple linear regression model. Then you will draw distribution plot of y pred and y test.
- To be able to operate computer
- A lot of curiosity!
- Some knowledge of Python programming and high school level math will be an asset
Join the most comprehensive Machine Learning Hands-on Course, because now is the time to get started!
From basic concepts about Python Programming, Supervised Machine Learning, Unsupervised Machine Learning to Reinforcement Machine Learning, Natural Language Processing (NLP), this course covers it all you need to know to become a successful Machine Learning Professional!
But that's not all! Along with covering all the steps of Machine Learning functions, this course also has quizzes and projects, which allow you to practice the things learned throughout the course!
You'll not only learn about the concepts, but also practice each of those concepts through hands-on and real life Projects.
And if you do get stuck, you benefit from an extremely fast and friendly support - both via direct messaging or discussion. You have my word!
With more than two decades of IT experience, I have designed this course for students and professionals who wish to master how to develop and support industry standard Machine learning projects.
This course will be kept up-to-date to ensure you don't miss out on any changes once Machine Learning is required in your project!
Why Machine Learning?
In modern times, Machine Learning is one of the most popular (if not the most!) career choices. According available data, Machine Learning Engineer Is The Best Job of 2019 with a 344% growth and an average base salary of $146,085 per year.
If you are looking for a thriving career in Data Analytics, Artificial Intelligence, Robotics, this is the right time to learn Machine Learning.
Get a very deep understanding of Machine Learning!
- Students and professionals who want to become Machine Learning Expert or Data Scientist.
- IT Professionals, Mathematicians, Statisticians.
- Machine learning enthusiasts.
- Project Managers, Data Analytics, and Business Intelligence Professionals.
- Python developers.