A break through in your Career!
What is Machine Learning?
We already have lots of data, but very little time to analyze it because the world is moving at a great pace. Everyday there is some new trend in the market. Having the ability to continuously learn from previous data is a super-power. Machine learning is all about giving computers the ability to learn from previous examples, rather than being explicitly programmed.
What you will learn in the course?
We will briefly discuss machine learning and it’s sub-types. Then we will quickly move algorithms. We will be discussing 12-15 different algorithms. But machine learning is much more than just building models. So, in the second half of the course, we will discuss everything else that goes into developing good ML systems. Finally, we will learn how to make web-apps and deploy ML models on cloud.
- Supervised and Unsupervised learning
- Classification and Regression
- K Nearest Neighbors (KNN)
- Support Vector Machines (SVM)
- Naïve Bayes (NB)
- Decision Tree (DT)
- Bagging models
- Random Forest
- Gradient Boosting
- Linear Regression
- Ridge, Lasso and ElasticNet
- Evaluation Metrics
- Feature Engineering
- Time Stamps
- Feature Selection
- Hyper-parameter optimization
- overfitting & underfitting
- dimensionality reduction
- Natural Language Processing (NLP)
- Cleaning and tokenizing
- Bag of words
- Word Embeddings
- Other libraries
- Making Web-apps
- Organizing machine learning projects and best practices in industry
- Project work
- 5 end-to-end machine learning workflows
- Participate in a Kaggle competition.
- Deploying machine learning model.
By the course end . . .
You will have a pretty good idea of everything that goes into building ML systems. You will be able to develop basic and intermediate level ML systems. You will know how to deploy your system on cloud. Participating in online Data Science competitions, reading research papers, and blogs are some more things that you will feel comfortable with.