Data Science And Machine Learning Developer Certification

Data Science | Machine Learning | Deep Learning | Keras | TensorFlow | Scikit

What you'll learn

Evaluate deep learning models using TensorFlow and Keras across various applications.

Identify and prepare raw data for analysis, modeling, and deployment in scalable ML workflows.

Apply supervised and unsupervised learning techniques to solve real-world prediction and classification tasks.

Develop end-to-end machine learning models using Python and open-source ML libraries.

Requirements

Exposure to coding (Python is helpful but not an absolute must).

Exposure to basic math (linear algebra is a plus but not required).

Description

Course Description:Are You Ready to Build Machine Learning Models That Work in the Real World?You’ve probably heard of machine learning, seen flashy headlines about AI beating humans at games or diagnosing diseases, and maybe even tried a few Python tutorials. But when it comes to actually building and deploying ML models that solve real business problems — it’s easy to get stuck. That’s where this course comes in.This is more than a course. It’s a complete, hands-on journey through the machine learning lifecycle — built for developers, analysts, and professionals who want to move from understanding theory to applying it with confidence.Whether you're looking to transition into a machine learning role, collaborate more effectively with data scientists, or lead a data-driven team, this course equips you with the tools, intuition, and experience to make an impact.Course OverviewThis course takes a practical approach to learning machine learning and deep learning. Rather than diving straight into math-heavy formulas or overly simplified toy problems, we focus on what you actually need to know to build intelligent systems — and how to do it using modern, open-source tools.Starting with the fundamentals of machine learning, you’ll explore how models learn, what makes them perform well (or poorly), and how to train and evaluate them using real-world data. You’ll work with classification algorithms like support vector machines and naive Bayes, and explore practical use cases such as admissions, forecasting, and outlier detection.As you advance, you’ll build deep learning models using TensorFlow and Keras — experimenting with architectures, layers, activation functions, and learning rates. You'll get hands-on experience with convolutional operations for image recognition, as well as transfer learning using pretrained models to boost performance on smaller datasets.You’ll also explore how to scale your models using pipelines and distributed systems, preparing you for real-world deployment challenges.What You Will LearnBy the end of this course, you will be able to:Develop end-to-end machine learning models using Python and open-source libraries like Scikit-learn, TensorFlow, and Keras.Apply supervised and unsupervised learning techniques to real-world datasets.Evaluate and fine-tune deep learning architectures including CNNs and pretrained models.Identify and prepare raw data for modeling, from feature engineering to training workflows.Scale your machine learning pipelines using cloud-based and distributed systems.What Makes This Course DifferentUnlike many theoretical or overly simplified machine learning courses, this one is designed around real-world use cases, industry-standard tools, and a strong emphasis on intuitive understanding. Every topic is built to be immediately applicable—not just academic. By the end of this course, you'll have written working code, developed practical workflows, and built a toolkit of reusable techniques.Here’s how this course stands apart:Hands-on from the start: You won’t just watch lectures—you actively write code, run experiments, and troubleshoot models.Focused on practical fundamentals: You learn topics like support vector machines, neural networks, and transfer learning through real-world examples, not abstract formulas.Built for modern ML roles: Whether you’re aiming to become a machine learning engineer, data scientist, or technical lead, this course prepares you with job-relevant skills.Uses professional-grade tools: You’ll work with TensorFlow, Keras, Scikit-learn, and other frameworks widely used in the machine learning industry.Ready to Get Started?If you’re looking for a course that goes beyond theory, teaches you how machine learning really works, and gets you building useful models right away — this is the course for you.Join now and start your journey toward becoming a skilled, job-ready machine learning practitioner. The future of AI isn’t just for PhDs — it’s for builders. Let’s get started.

Overview

Section 1: Introduction to Machine Learning

Lecture 1 Welcome and Course Goals

Lecture 2 Introduction to Machine Learning

Lecture 3 Getting Started with Your First Python Lab

Lecture 4 Analyzing Rainfall Data Using Pandas

Lecture 5 Navigating Data Structures with Pandas

Lecture 6 Loading and Preparing Data in Python

Lecture 7 Analyzing Flight Data with Pandas

Lecture 8 Visualizing Car Data with Matplotlib and Pandas

Lecture 9 Mastering Data Visualization in Python

Lecture 10 Comparing Seaborn and Matplotlib for Visualization

Lecture 11 Data Visualization with Matplotlib and Seaborn

Lecture 12 Understanding Statistical Measures for Exploratory Data Analysis

Lecture 13 Exploratory Data Analysis Overview

Lecture 14 Preparing Data with Scikit-Learn and PCA

Lecture 15 Applying Linear Regression with Scikit-Learn

Lecture 16 Modelling Tips with Linear Regression in Python

Lecture 17 Building Predictive Models Using Linear Regression and Gradient Descent

Lecture 18 Solving Real-World Problems with Regularized Linear Regression

Lecture 19 Implementing L1 and L2 Regularization Using Scikit-Learn

Lecture 20 Predicting Binary Outcomes with Logistic Regression

Lecture 21 Labs: Section 1

Section 2: Working with Real-World Data and Classifiers

Lecture 22 Understanding Support Vector Machines in Practice

Lecture 23 Evaluation Metrics, ROC Curves, and Naive Bayes Classification

Lecture 24 Classification: Accuracy, Precision, Recall, and Related Metrics

Lecture 25 Classifying College Admissions with Support Vector Machines (SVMs)

Lecture 26 Predicting College Admissions with Support Vector Machines

Lecture 27 Understanding and Applying Decision Trees for Classification and Regression

Lecture 28 Enhancing Model Performance with Random Forests

Lecture 29 Decision Trees and Random Forests in Practice

Lecture 30 Building a Decision Tree on the Prosper Loan Dataset

Lecture 31 Classifying Income Levels with Naïve Bayes

Lecture 32 Introduction to Unsupervised Learning and K-Means Clustering

Lecture 33 Principal Component Analysis (PCA) for Dimensionality Reduction

Lecture 34 Introduction to Principal Component Analysis

Lecture 35 Clustering Car Data with K-Means

Lecture 36 Exploring Wine Data Using PCA

Lecture 37 Labs: Section 2

Section 3: Exploring Deep Learning Concepts and Tools

Lecture 38 Introduction to Deep Learning and the Modern AI Landscape

Lecture 39 Exploring Linear Models in TensorFlow Playground

Lecture 40 Visualizing Neural Networks and Understanding Hyperparameters

Lecture 41 Introduction to TensorFlow

Lecture 42 Understanding TensorFlow Sessions

Lecture 43 Exploring TensorFlow’s Low-Level API

Lecture 44 TensorFlow Tensors and Sessions

Lecture 45 Linear Models in TensorFlow

Lecture 46 Implementing Linear Models with TensorFlow and Gradient Descent

Lecture 47 Implementing Linear Regression Using Low-Level TensorFlow APIs

Lecture 48 TensorFlow High-Level API and Estimators

Lecture 49 Understanding TensorFlow Estimators

Lecture 50 Applying Estimator and Keras APIs to Linear Models

Lecture 51 Keras API Documentation

Lecture 52 Exploring Hidden Layers with Complex Datasets

Lecture 53 Building and Training Deep Neural Networks with Low-Level and Keras APIs

Lecture 54 Modeling Iris Flower Classification with Estimator and Keras APIs

Lecture 55 Understanding Multilayer Perceptrons, Optimization, and Activation in Neural Net

Lecture 56 Labs: Section 3

Section 4: Section 4: Learning Image Processing with Convolutions

Lecture 57 Understanding Convolutional Neural Networks (CNNs)

Lecture 58 Building CNNs with TensorFlow

Lecture 59 Pooling Layers and Padding in CNNs

Lecture 60 Visualizing Training with TensorBoard

Lecture 61 Labs: Section 4

Section 5: Leveraging Pretrained Models with Transfer Learning

Lecture 62 Transfer Learning and Pretrained Models

Lecture 63 Recurrent Neural Networks in TensorFlow

Lecture 64 Understanding LSTM Networks

Lecture 65 Architecting Deep Learning Workflows with Keras and TensorFlow

Lecture 66 Labs: Section 5

Section 6: Building a Machine Learning Pipeline

Lecture 67 Scaling Machine Learning with TensorFlow and Distributed Systems

Lecture 68 Mastering Feature Engineering for Machine Learning

Lecture 69 Building Full ML Pipelines: From Data Exploration to Prediction

Lecture 70 A Guide to Monitoring Machine Learning Models in Production

Lecture 71 Labs: Section 6

Section 7: Closing Remarks

Lecture 72 Course Reflection and Continued Learning

Aspiring Data Scientists & ML Engineers: Developers and recent graduates looking to transition into machine learning roles or launch a career in data science.,Technical Professionals Enhancing ML Knowledge: Software engineers, information architects, and developers who want to deepen their understanding of ML/DL to better collaborate with data teams.,Analytics & BI Professionals: Business analysts and analytics managers seeking to apply data science techniques and lead ML-driven projects more effectively.,AI & ML Practitioners Upskilling: Working professionals in AI/ML aiming to formalize their skills, build scalable models, and stay current with industry tools.