Udemy – Data Science : Complete Data Science & Machine Learning

Description

Requirements

• No prerequisites required

• You will learn everything from Python basics to advanced Deep Learning concepts

• Passion for learning data analysis, machine learning, and AI

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Course Description

Data Science and Machine Learning are among the most in-demand and future-proof career skills. If you’ve been looking for one complete course that teaches everything—from Math for Machine Learning, Advanced Statistics, Data Processing, ML Algorithms, Deep Learning, and real-world projects—then this is the perfect place to start.

This all-in-one Data Science and Machine Learning course includes:

• 250+ high-quality lectures

• 25+ hours of step-by-step training

• 11 real-life projects

• 1 Kaggle competition project scoring in the top 1%

• Code templates, quizzes, and hands-on exercises

You will build real-world, industry-grade projects including:

• Kaggle Bike Demand Prediction

• Automated Loan Approval System

• IRIS Classification

• Adult Income Prediction (US Census Dataset)

• Bank Telemarketing Response Prediction

• Breast Cancer Detection

• Diabetes Prediction (PIMA Indians Dataset)

Today, Data Science and Machine Learning power industries such as automobile, healthcare, fintech, media, telecom, retail, and more. This course equips you with the complete skill set needed to work with data, build machine learning models, perform analysis, and create business-driven AI solutions.

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What You’ll Learn

1. Introduction to Data Science & Machine Learning

• End-to-end Data Science lifecycle

• Types of Data Analytics

• Data Architecture essentials

• Real-world deployment workflows

2. Python for Data Science

• Python programming fundamentals

• Popular libraries: NumPy, Pandas, Matplotlib, Seaborn

• File handling, data manipulation, scripting

Python is the #1 language for Data Science, ML, and Deep Learning—and you will master it step-by-step.

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3. Mathematics for Machine Learning

• Linear Algebra: vectors, matrices, transformations

• Calculus for ML: derivatives, gradients

• Understanding Gradient Descent, cost functions, optimizers

Math is the foundation behind algorithms like Logistic Regression, SVM, and Neural Networks.

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4. Advanced Statistics for Data Science

• Probability and distributions

• Inferential statistics and significance testing

• Foundations behind PCA, Feature Selection, and ML model evaluation

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5. Data Visualization & Exploratory Data Analysis (EDA)

• Visualizing data patterns and trends

• Creating plots, charts, heatmaps, and pair plots

• Detecting hidden correlations for ML-ready datasets

EDA is essential for identifying the right Machine Learning approach.

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6. Data Processing & Cleaning

• Handling missing values

• Encoding categorical features

• Data scaling and normalization

• Using Pandas for large datasets

More than 70% of a data scientist’s time goes into cleaning and preparing data—you will master this process thoroughly.

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7. Machine Learning (Complete ML A–Z Training)

• Supervised & Unsupervised Learning

• Regression, Classification, Clustering

• Logistic Regression, Decision Trees, Random Forest, SVM, KNN, Naive Bayes

Advanced ML Concepts

• Ridge, Lasso, ElasticNet Regression

• Model Selection & Evaluation

• Cross Validation & Hyperparameter Tuning

• Bias-Variance Tradeoff explained clearly

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8. Feature Selection & Dimensionality Reduction

• Statistical Feature Selection methods

• PCA (Principal Component Analysis)

• Reducing dimensions while retaining maximum variance

• Making ML models faster, smarter, and more accurate

This is what separates beginner data scientists from professionals.

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9. Deep Learning with TensorFlow & Keras

• Neural Network foundations

• Activation functions, backpropagation, optimization

• Building deep learning models step-by-step

• Hands-on training using TensorFlow + Keras

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10. Kaggle Competition Project

You will complete a full Kaggle Machine Learning solution, including:

• End-to-end workflow

• Data cleaning & feature engineering

• Model training, tuning, and evaluation

• Achieving a top-tier Kaggle score

This mimics real-world data science job challenges.

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What You Will Gain

• Complete hands-on experience with Data Science, Machine Learning, and Deep Learning

• Strong understanding of advanced algorithms and statistical techniques

• Practical skills to solve real business problems

• Ability to work confidently with Python, ML frameworks, and large datasets

• Future updates and continuous enhancements included

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Who This Course Is For

• Beginners who want to start a career in Data Science, Machine Learning, or AI

• Students, professionals, and developers wanting to upgrade their ML skills

• Anyone passionate about working with data and solving real-world problems using AI

Please Note: Files will be included in this purchase only Full Course Video & Course Resources. You will get cloud storage download link with life time download access.