Whether you are a beginner or looking to level up your Data Science / AI / ML skills, I’ve put together a structured guide to navigate your AI journey with the Complete Data Science Pathway by ML+
Irrespective of what field / background you come from, the Complete Data Science Pathway courses will help you to learn and master Data Science from scratch.
Step 1: Foundations of Data Science: Orientation Course
Before you jump right in, it is a good idea to know the entire landscape of AI / Data Science. This orientation course will teach you what Data Science and ML is all about, what you can and cannot do with it, how Data Science projects work, different use cases in various domains etc.
I highly recommend to take this if you are a beginner, so would not feel lost later on. If you are already familiar with Data Science, feel free to skip.
Pre-requisites: Nil. No Prior coding knowledge or experience required
Course Work: 1 Course – 2.5 hours
Key Skills Gained: Data Science literacy, knowledge of what AI / ML is all about and ability to hold conversations.
Who is this for?: Beginners, DS Product Managers, Executives
Please click this link for the Foundations of Data Science Course
If you have decided to learn and master Data Science, please proceed to the next step.
Step 2: Programming for Data Science
Programming is essential for Data Science, to be able to manipulate and analyze data, to build ML models and build your own logics.
What to learn?
Python, SQL and optionally R programming language. Especially we focus on Python for Data Analysis with NumPy, Pandas and Matplotlib packages, advanced and high performance computing with Python followed by SQL in 4 levels.
These are all essential and must.
R programming is great for statistical computing, you can pick up later at own pace from the supplementary courses section.
Why learn programming first? Because the next step is Maths for ML, which we will learn by applying the ideas in code. So, good to learn the programming for DS first.
Pre-requisites: Foundations of Data Science course (can skip if already familiar with Data Science)
Course work: 8 courses on Python, SQL
Key Skills Gained: Beginner to advanced Python and SQL, focused on Data Science.
Please click this link for the Programming for Data Science Course
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Step 3: Maths for Machine Learning
Mathematics is the backbone of machine learning. It enables ML practitioners to understand the principles behind the algorithms, optimize models and interpret ML.
Primarily, there are 3 parts to this: Linear Algebra, Calculus and Probability & Statistics.
Statistics is essential for both deep-dive data analysis and predictive modeling.
Take note of some of the key concepts. Linear algebra make sure to understand Vectors and matrices, determinants, eigenvalues and eigenvectors, and Singular Value Decomposition (SVD).
Key concepts in Calculus includes: Derivatives and partial derivatives for Gradient descent which is used to minimize loss functions, integrals for area under a curve and the chain rule for backpropagation.
In Statistics, some of the key concept are understanding distributions, univariate and bivariate analysis techniques, hypothesis testing and statistical tests, confidence intervals, bayesian statistics etc.
Covered in detail in the Statistics course (actively updated) and the EDA course, in the form of simple video lessons and exercises.
Pre-requisites: Programming for Data Science courses
Course work: 3 courses
Key Skills Gained: Mathematical foundations for machine learning – Linear Algebra, Calculus, Statistics and Exploratory Data Analysis techniques.
Please click this link for the Maths for Machine Learning Courses
Step 4: Machine Learning Algorithms
This is the core step, knowing the ML algorithms and the math behind it allows ML practitioners to understand how and why these algorithms work, rather than treating them as black boxes.
Why learn the math behind ML? Better model selection, better problem solving skills, better inferencing and eventually better business results.
You will be better equipped to learn new algorithms and come up with custom ones yourselves, empowered to crack Data Science interviews confidently.
We will treat each algorithm in-depth, starting with Linear Regression which is the foundational prediction modeling technique for predicting continuous variables (like revenue, housing price etc). The course includes regularization and advanced regression techniques. Following this we will learn Logistic regression which is the foundational model for classification problems such as detecting fraud.
That covers the foundations of both regression and classification. However, within classification problems handling imbalanced classes requires special attention since many real world problems such as the Credit card Fraud has this issue.
Then we step into the Core ML algorithms, followed by Ensemble algorithms such as bagging and boosting based models, which are techniques that use multiple ML models to get better model performance.
In ML, Fixed and Mixed effects is a special type of statistical modeling, where we categorize and model predictors as either fixed or random effects, quite useful in business problems such as marketing spend optimization, then a very popular type of problem is the Recommendation systems basic to advanced and finally we look at how to explain ML models using both model specific and model agnostic techniques.
At ML+, we will not just stop with explaining how the algorithm works, you will learn the related underground concepts that only seasoned world-class Data Scientists know and apply. Checkout the logistic regression course for example.
Pre-requisites: Mathematics for ML and Programming for Data Science.
Course work: 9 courses on Machine Learning Algorithms
Key Skills Gained: ML Concepts, Math behind ML, How ML algorithms work basic to advanced.
Please click this link for the ML Algorithms Courses
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Step 5: ML OPs
ML Ops focusses on bridging the gap between ML model development and deploying them in production environments. This involves making sure the models are reliable by monitoring performance and periodically updating.
This is an area of focus particularly for ML Engineers who are equipped with knowledge of building ML, deploying and maintaining them.
At ML+, we follow the path of deployment mainly using AWS based services such as AWS EC2 for hosting your model in a dedicated server, quick serverless model deployment using AWS Lambda or a more comprehensive approach is to develop and deploy using AWS Sagemaker.
Then, we will learn MLflow to streamline and manage the machine learning lifecycle.
When dealing with Big Data, we use PySpark which is most popular distributed computing framework that Data scientists and engineers use. We will learn this exhaustively in a 5-part series.
Pre-requisites: Programming for Data Science, First 3 courses in ML Algorithms.
Course work: 9 courses on ML Ops topics.
Key Skills Gained: AWS basics, AWS EC2, AWS Lambda, AWS Sagemaker, ML flow and PySpark.
Please click this link for MLOps specialization
Step 6: Deep Learning
Deep Learning is a sub-set of machine learning, based in a specific type of ML algorithm named Neural networks. When neural networks have many layers, it is often referred to as ‘Deep’ Learning.
ML practitioners use deep learning to work on cutting-edge AI applications like image processing and NLP, opening up lucrative career opportunities.
Start with the Foundations of Deep Learning 1 and 2 and move to applied deep learning with PyTorch. This will get the fundamentals with understanding back propagation, forward and backward pass, optimizers, dropout regularization building your own architectures.
Then, we move into computer vision based applications using CNN in a project based learning based on Detecting defects in steel sheets. Then we see how to solve sequential problems like time series and text generation using RNNs and LSTMs.
Post that we’ll learn building blocks like encoder-decoder, self attention and transformers using BERT by classifying sentiment of reviews.
Pre-requisites: Programming for Data Science, ML Algorithms
Course work: 6 courses on Deep learning using PyTorch.
Key Skills: Deep Learning Foundations, PyTorch, BackProp, Self-attention, ANN, CNN, RNNs, LSTM, Transformers, BERT.
Please click this link for Industry Data Science Projects specialization
Step 7: Time Series Forecasting
Predicting future values is forecasting, such as predicting future demand of products from inherent past patterns, seasonality and external factors. Forecasting projects often have a strong business impact, high visibility and simple to quantify the benefits.
ML+ provides comprehensive curriculum for mastering basic to advanced Time series analysis and forecasting, building clear intuition with business understanding. You will gain from years of experience of industrial experience, practical implications on real world projects.
Start with basic time series analysis, followed by statistical models for time series will give you the introduction to foundational forecasting models starting with exponential smoothing, Holt-Winters, and filters for time series data.
Then we move to ARIMA, SARIMAX based models with statistical tests, followed by Vector autoregression (VAR) based models which are useful when you have a system of series data that influence each other.
We will also look at advanced analysis methods such as Singular spectrum analysis (SSA) and Time series feature engineering techniques which are crucial for high quality results.
Pre-requisites: Programming for Data Science, ML Algorithms
Course work: 8 courses on Time Series Forecasting
Key Skills Gained: Basic to advanced Time series Analysis and Forecasting
Please click this link for Time series forecasting specialization
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Step 8: Industry Data Science Projects
It is important to move on to build Data Science projects for two reasons: (i) For practice and get good at it so you can can prove to yourself. (ii) For a strong resume which is key for getting interviews and cracking it as well.
In ML+, we will work on projects that are typically executed by Data Scientists. Most of these projects will be applicable across industries.
For example, Customer Lifetime value project (CLTV) is used by businesses to justify and control the cost of acquisition of customers, likewise, Market Mix Modeling (MMM) is used by marketing teams for optimizing ad spends, Attribution models helps better decisioning on marketing strategies, Uplift modeling to help estimate if and how much marketing campaigns help with customer acquisitions.
In a similar fashion, the 19 projects in ML+ Industry Data Science Projects will give the practice, multiple strategies to solve given problem, how to think about solving Data Science projects, what other approaches to take when hitting a roadblock and how to present the results to stakeholders.
Pre-requisites: Programming for Data Science, ML Algorithms, Deep Learning and Time Series Forecasting
Course work: 19 Project courses on Industry Data Science projects.
Key Skills Gained: How to approach, solve and deliver Data science projects.
Please click this link for Industry Data Science Projects specialization
Step 9: Generative AI (tbu)
Generative AI and Large Language models (LLM) are subsets of Deep learning space, which are the most popular and evolving area. Using Gen AI you can build interesting use-cases, many of them revolving around customized chatbots and methods to improve the quality of the responses from the llms.
The main library in the forefront of the GenAI space is LangChain, we we will learn in detail in the Complete LangChain Course, where we will learn the basic to advaned functionalities of the library as well as several concepts related to Gen AI app development.
Then we will learn about advanced retrieval augmented generation (RAG) techniques, designed to enhance the quality of the responses for various use-cases.
How to deploy LLMs in cloud services such as AWS Bedrock, finally we will have a dedicated look at 12 Gen AI projects which you can nearly use as templates to implement these in a production environment.
Pre-requisites: Programming for Data Science, Knowledge of ML and Deep Learning will be helpful.
Course work: 5 courses on Gen AI.
Key Skills Gained: LangChain, AWS Bedrock, basic to advanced RAGs, Building Chatbots, various Gen AI usecases.
Please click this link for GenAI specialization
Hope this information was helpful. If you wish to enroll, please use the COUPON CODE: ROADMAP30 to get a special discount during checkout.
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