Applied Machine Learning Online Course
How to utilise Appliedaicourse
Python for Data Science Introduction
- Python, Anaconda and relevant packages installations
- Why learn Python?
- Keywords and identifiers
- comments, indentation and statements
- Variables and data types in Python
- Standard Input and Output
- Control flow: if else
- Control flow: while loop
- Control flow: for loop
- Control flow: break and continue
Python for Data Science: Data Structures
Python for Data Science: Functions
Python for Data Science: Numpy
Python for Data Science: Matplotlib
Python for Data Science: Pandas
Python for Data Science: Computational Complexity
Plotting for exploratory data analysis (EDA)
exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task.
- Introduction to IRIS dataset and 2D scatter plot
- 3D scatter plot
- Pair plots
- Limitations of Pair Plots
- Histogram and Introduction to PDF(Probability Density Function)
- Univariate Analysis using PDF
- CDF(Cumulative Distribution Function)
- Mean, Variance and Standard Deviation
- Percentiles and Quantiles
- IQR(Inter Quartile Range) and MAD(Median Absolute Deviation)
- Box-plot with Whiskers
- Violin Plots
- Summarizing Plots, Univariate, Bivariate and Multivariate analysis
- Multivariate Probability Density, Contour Plot
- Exercise: Perform EDA on Haberman dataset
It will give you the tools to help you with the other areas of mathematics required to understand and build better intuitions for machine learning algorithms.
- Why learn it ?
- Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector
- Dot Product and Angle between 2 Vectors
- Projection and Unit Vector
- Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane
- Distance of a point from a Plane/Hyperplane, Half-Spaces
- Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D)
- Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D)
- Square ,Rectangle
- Hyper Cube,Hyper Cuboid
Interview Questions on Linear Algebra
Probability and Statistics
Interview Questions on Probability and statistics
Dimensionality reduction and Visualization:
In machine learning and statistics, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration, via obtaining a set of principal variables. It can be divided into feature selection and feature extraction.
- What is Dimensionality reduction?
- Row Vector and Column Vector
- How to represent a data set?
- How to represent a dataset as a Matrix.
- Data Preprocessing: Column Normalization
- Mean of a data matrix
- Data Preprocessing: Column Standardization
- Co-variance of a Data Matrix
- MNIST dataset (784 dimensional)
- Code to Load MNIST Data Set
Interview Questions on Dimensionality Reduction
PCA(principal component analysis)
- Why learn PCA?
- Geometric intuition of PCA
- Mathematical objective function of PCA
- Alternative formulation of PCA: Distance minimization
- Eigen values and Eigen vectors
- PCA for Dimensionality Reduction and Visualization
- Visualize MNIST dataset
- Limitations of PCA
- PCA Code example using Visualization
- PCA Code example using non-Visualization
(t-SNE)T-distributed Stochastic Neighbourhood Embedding
Real world problem: Predict rating given product reviews on Amazon
Classification And Regression Models: K-Nearest Neighbors
Interview Questions on K-NN(K Nearest Neighbour)
Classification algorithms in various situations
Interview Questions on Classification algorithms in various situations
Performance measurement of models
Interview Questions on Performance Measurement Models
Interview Questions on Naive Bayes Algorithm
Solving Optimization Problems
Interview Questions on Logistic Regression and Linear Regression
Support Vector Machines (SVM)
Interview Questions on Support Vector Machine
Interview Questions on decision Trees
Interview Questions on Ensemble Models
Featurization and Feature engineering.
Hierarchical clustering Technique
DBSCAN (Density based clustering) Technique
Interview Questions on Clustering:
Recommender Systems and Matrix Factorization
Interview Questions on Recommender Systems and Matrix Factorization.
Case Study 2: Personalized Cancer Diagnosis
Case study 3:Taxi demand prediction in New York City
Case Study 4: Microsoft Malware Detection
Case study 5:Netflix Movie Recommendation System
Case study 6: Stackoverflow tag predictor
Case Study 7: Quora question Pair Similarity Problem
Deep Learning:Neural Networks.
Deep Learning: Deep Multi-layer perceptrons
Deep Learning: Tensorflow and Keras.
Deep Learning: Convolutional Neural Nets.
Deep Learning: Long Short-term memory (LSTMs)
Case Study 8: Amazon fashion discovery engine
Case study 9:Self Driving Car
Interview Questions on Deep Learning
Obective of Applied AI/ Machine Learning Online Course:
The AppliedAICourse attempts to teach students/course-participants some of the core ideas in machine learning, data-science and AI that would help the participants go from a real world business problem to a first cut, working and deployable AI solution to the problem. Our primary focus is to help participants build real world AI solutions using the skills they learn in this course.
This course will focus on practical knowledge more than mathematical or theoretical rigor. That doesn’t mean that we would water down the content. We will try and balance the theory and practice while giving more preference to the practical and applied aspects of AI as the course name suggests. Through the course, we will work on 20+ case studies of real world AI problems and datasets to help students grasp the practical details of building AI solutions. For each idea/algorithm in AI, we would provide examples to provide the intuition and show how the idea to used in the real world.
- Validity of this course is 365 days( i.e Starts from the date of your registration to this course)
- Expert Guidance, we will try to answer your queries in atmost 24hours
- 10+ real world case studies and 5 case studies will be given as assignments to build your portfolio. please click here to view the sample portfolio
- 30+ machine learning and Deep learning algorithms will be taught in this course.
- No prerequisites– we will teach every thing from basics ( we just expect you to know basic programming)
- Python for Data science is part of the course curriculum.
- The content of this course will be dynamic(i.e lessons will be added if there is an exceptional paper published)
We are building our course content and teaching methodology to cater to the needs to students at various levels of expertise and varying background skills. This course can be taken by anyone with a working knowledge of a modern programming language like C/C++/Java/Python. We expect the average student to spend at least 5 hours a week over a 6 month period amounting to a 145+ hours of effort. More the effort, better the results. Here is a list of customers who would benefit from our course:
Undergrad (BS/BTech/BE) students in engineering and science.
- Grad(MS/MTech/ME/MCA) students in engineering and science.
- Working professionals: Software engineers, Business analysts, Product managers, Program managers, Managers, Startup teams building ML products/services.
- ML Scientists and ML engineers.
- Lectures 654
- Quizzes 0
- Duration 140+ hours
- Skill level All levels
- Language English
- Students 963
- Assessments Yes