Rating 4.33 out of 5 (3 ratings in Udemy)
What you'll learn- Build effective machine learning models using python and scikit-learn
- Analyse data using Exploratory Data Analysis techniques from Data Science
- Cut through all the jargon to fully grasp the important machine learning and artificial intelligence concepts
- Explore datasets from a wide variety of fields including: sales, medical, sociological and other sciences
- Make Predictions using both Regression and classification models
- Use …
Rating 4.33 out of 5 (3 ratings in Udemy)
What you'll learn- Build effective machine learning models using python and scikit-learn
- Analyse data using Exploratory Data Analysis techniques from Data Science
- Cut through all the jargon to fully grasp the important machine learning and artificial intelligence concepts
- Explore datasets from a wide variety of fields including: sales, medical, sociological and other sciences
- Make Predictions using both Regression and classification models
- Use advanced techniques such as Approximate KNN and LSH
- Create data visualisations with seaborn and matplotlib
- Make Predictions using Regression models: SVM, Decision Trees, Random Forest
- Classify Data using - K-Nearest Neighbour, Naive Bayes, Tree based models
- Cluster data using - K-means, Gaussian Mixture Models, Agglomerative Clustering, DBSCAN
- Generate summary statistics with pandas, numpy and scipy
- Learn best practices for training and evaluating your models
- Identify outliers and be able to clean and handle missing data
- Work with data resampling techniques to balance your data
- Build Pipelines with training data
- Evaluate and compare models using k-fold validation
- Deploy encoding techniques - One-hot Encoding, Target Encoding, Binary Encoding
- Data preprocessing
DescriptionWhy choose this course ?
Other courses are either too theoretical or too superficial in explaining how things work. This course was created to strike a balance between the practical and the theory.
In real-life you’ll need to be able build models and troubleshoot them when they don’t work. You don’t need to be a mathematician to use ML, but you need to have an intuition about how and why something works. This course gives you just that.
Hiring companies look for professionals who can problem solve and think in a particular way, without only relying on a specific technology stack. Because technology always changes!
This course helps you to develop a data scientist mindset.
How will this help my career ?
In this course we go into many of the core technologies that are used in the tech industry (eg. Spotify, Amazon, Google, Netflix, Zalando, Tencent )
As you build different models during the course, you’ll be adding to your portfolio and to your confidence as a data scientist and ML practitioner.
People who master Machine Learning are in high demand in the job market, as there is a lack of qualified professionals.
Machine Learning is the future! Now is the time to get on board and benefit from an interesting career and well paid job. Start learning today!
What will I learn in this course ?
Machine Learning Algorithms
Exploratory Data Analysis
Use Python with Jupyter
Data science libraries: numpy, scipy, pandas
Data Visualisation with Matplotlib, Seaborn
Feature Engineering
Regression
Clustering
Classification
PCA (Principal Component Analysis)
k-fold validation & train/test splits
Model fit & evaluation
SVR (Support Vector Regressors)
SVM (Support Vector Machines)
K-Nearest Neighbour
Gaussian Mixture Models
Agglomerative Clustering
DBSCAN
Pipelines
One-hot encoding, target encoding, binary encoding
Business use Machine Learning models
Random Forest
Decision Trees
K-means
Bias/Variance Tradeoff
This course includes
12 hours of video content
17 downloadable resources
17 practical assignments in jupyter notebooks
Reference Materials & further reading
