Prateek MishraFounder & Chief of TechSim+
AI & ML Courses
Data Analytics and Machine Learning with Julia
Dive into Julia’s Machine learning framework and build a Machine Learning Model. An in-depth exploration of Julia's growing ecosystem of packages. Learn about Machine learning and give speed and high performance to data analysis on large data sets Apply statistical models in Julia for data-driven decisions.
- Lectures 30
- Duration 30 Days
- MemberShip Yes
- Projects Yes
- Skill level Advanced
- Language English
- Assessments Yes
Julia is a fast and high performing language that's perfectly suited to data science with a mature package ecosystem and is now feature complete. It is a good tool for a data science practitioner.
This course will help you get familiarised with Julia's rich ecosystem, which is continuously evolving, allowing you to stay on top of your game.
This course contains the essentials of data science and gives a high-level overview of advanced statistics and techniques. You will dive in and will work on generating insights by performing inferential statistics, and will reveal hidden patterns and trends using data mining. This has the practical coverage of statistics and machine learning. You will learn Supervised and UnSupervised Machine Learning, Data Analytic, Image Processing and lot more.
Step - 1: The Groundwork – Julia's Environment
- Setting up the environment
- Using Jupyter Notebook
- Package management: Pkg.status(), Pkg.add()
- Working with unregistered packages
- Developing packages
- Parallel computation using Julia
- Julia's key feature – multiple dispatch
- Methods in multiple dispatch
- Functions in Julia
- Understanding Types and Dispatch
- Working with Control Flow
Step - 2: Data Munging or Data Analysis
- What is data munging?
- The data munging process
- What is a DataFrame?
- DataArray – a series-like data structure
- Installation and using DataFrames.jl
- Working with DataFrames
- The Split-Apply-Combine strategy
- Reshaping the data
- Sorting a dataset
- Pooling data
Step - 3: Making Sense of Data Using Visualization
- Difference between using and importall
- Pyplot for Julia
- Plot using sine and cosine
- Generating Unicode scatterplots
- Generating Unicode line plots
- Visualizing using Vega
- Heatmaps in Vega
Step - 4: Data visualization using Gadfly
- Installing Gadfly
- Interacting with Gadfly using plot function
- Using Gadfly to plot DataFrames
- Generating an image with multiple layers
- Generating plots with different aesthetics using statistics
- Generating plots with different aesthetics using Geometry
- Using Geometry to create density plots
- Using Geometry to create histograms
- Smooth line plot
- Subplot grid
- Horizontal and vertical lines
- Beeswarm plots
- Continuous color scale
Step - 5: Supervised Machine Learning
- What is machine learning?
- Different types of machine learning
- What is bias-variance trade-off?
- Effects of overfitting and underfitting on a model
- Building decision trees – divide and conquer
- Advantages of decision trees
- Supervised learning using Naïve Bayes
- Advantages of Naïve Bayes
- Uses of Naïve Bayes classification
- How Bayesian methods work
Step - 6: Unsupervised Machine Learning
- Understanding clustering
- How are clusters formed?
- Types of clustering
- Hierarchical clustering
- Overlapping, exclusive, and fuzzy clustering
- Differences between partial versus complete clustering
- K-means clustering
- Algorithm of K-means
- Issues with K-means
- Getting deep into hierarchical clustering
- Understanding the DBSCAN technique
Step - 7: Creating Ensemble Models
- What is ensemble learning?
- Understanding ensemble learning
- Subsampling training dataset
- Bagging, Boosting
- Manipulating the input features
- Random forests
- Applications of ensemble learning
Step - 8: Collaborative Filtering and Recommendation System
- What is a recommendation system?
- Association rule mining
- Content-based filtering
- Collaborative filtering
- Building a movie recommender system
Prateek MishraFounder & Chief of TechSim+
Prateek is an entrepreneur and thought Leader in Artificial Intelligence deep-tech industries. He is a leading trainer with expertise in AI, Machine Learning, Data Analytic, Deep Learning, Python, Embedded and IOT, Julia Programming, Blockchain and Tableau. Prateek has Successfully conducted 200+ workshops.