Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2025]
-
Welcome to the course! Here we will help you get started in the best conditions
-
———-Part 1: Data Preprocessing———-
-
Data Preprocessing in PythonStep 1 – Data Preprocessing in Python: Preparing Your Dataset for ML Models0sStep 2 – Data Preprocessing Techniques: From Raw Data to ML-Ready Datasets0sMachine Learning Toolkit: Importing NumPy, Matplotlib, and Pandas Libraries0sStep 1 – Machine Learning Basics: Importing Datasets Using Pandas read_csv()0sStep 2 – Using Pandas iloc for Feature Selection in ML Data Preprocessing0sStep 3 – Preprocessing Data: Building X and Y Vectors for ML Model Training0sFor Python learners, summary of Object-oriented programming: classes & objectsStep 1 – Using Scikit-Learn to Replace Missing Values in Machine Learning0sStep 2 – Imputing Missing Data in Python: SimpleImputer and Numerical Columns0sStep 1 – One-Hot Encoding: Transforming Categorical Features for ML Algorithms0sStep 2 – Handling Categorical Data: One-Hot Encoding with ColumnTransformer0sStep 3 – Preprocessing Categorical Data: One-Hot and Label Encoding Techniques0sStep 1 – How to Prepare Data for Machine Learning: Training vs Test Sets0sStep 2 – Preparing Data: Creating Training and Test Sets in Python for ML Models0sStep 3 – Splitting Data into Training and Test Sets: Best Practices in Python0sStep 1 – Feature Scaling in ML: Why It’s Crucial for Data Preprocessing0sStep 2 – How to Scale Numeric Features in Python for ML Preprocessing0sStep 3 – Implementing Feature Scaling: Fit and Transform Methods Explained0sStep 4 – Applying the Same Scaler to Training and Test Sets in Python0s
-
Data Preprocessing in RData Preprocessing for Beginners: Preparing Your Dataset for Machine Learning0sData Preprocessing Tutorial: Understanding Independent vs Dependent Variables0sR Tutorial: Importing and Viewing Datasets for Data Preprocessing0sHow to Handle Missing Values in R: Data Preprocessing for Machine Learning0sUsing R’s Factor Function to Handle Categorical Variables in Data Analysis0sStep 1 – How to Prepare Data for Machine Learning: Training vs Test Sets0sStep 2 – Preparing Data: Creating Training and Test Sets in R for ML Models0sFeature Scaling in ML Step 1: Why It’s Crucial for Data Preprocessing0sHow to Scale Numeric Features in R for Machine Learning Preprocessing – Step 20sEssential Steps in Data Preprocessing: Preparing Your Dataset for ML Models0s
-
———-Part 2: Regression———-
-
Simple Linear RegressionSimple Linear Regression: Understanding the Equation and Potato Yield Prediction0sHow to Find the Best Fit Line: Understanding Ordinary Least Squares Regression0sStep 1a – Mastering Simple Linear Regression: Key Concepts and Implementation0sStep 1b: Data Preprocessing for Linear Regression: Import & Split Data in Python0sStep 2a – Building a Simple Linear Regression Model with Scikit-learn in Python0sStep 2b – Machine Learning Basics: Training a Linear Regression Model in Python0sStep 3 – Using Scikit-Learn’s Predict Method for Linear Regression in Python0sStep 4a – Linear Regression: Plotting Real vs Predicted Salaries Visualization0sStep 4b – Evaluating Linear Regression Model Performance on Test Data0sSimple Linear Regression in Python – Additional LectureStep 1 – Data Preprocessing in R: Preparing for Linear Regression Modeling0sStep 2 – Fitting Simple Linear Regression in R: LM Function and Model Summary0sStep 3 – How to Use predict() Function in R for Linear Regression Analysis0sStep 4a – Plotting Linear Regression Data in R: ggplot2 Step-by-Step Guide0sStep 4b – Creating a Scatter Plot with Regression Line in R Using ggplot20sStep 4c – Comparing Training vs Test Set Predictions in Linear Regression0s
-
Multiple Linear RegressionStartup Success Prediction: Regression Model for VC Fund Decision-Making0sMultiple Linear Regression: Independent Variables & Prediction Models0sUnderstanding Linear Regression Assumptions: Linearity, Homoscedasticity & More0sHow to Handle Categorical Variables in Linear Regression Models0sMulticollinearity in Regression: Understanding the Dummy Variable Trap0sUnderstanding P-Values and Statistical Significance in Hypothesis Testing0sBackward Elimination: Building Robust Multiple Linear Regression Models0sStep 1a – Hands-On Data Preprocessing for Multiple Linear Regression in Python0sStep 1b – Hands-On Guide: Implementing Multiple Linear Regression in Python0sStep 2a – Hands-on Multiple Linear Regression: Preparing Data in Python0sStep 2b – Multiple Linear Regression in Python: Preparing Your Dataset0sStep 3a – Scikit-learn for Multiple Linear Regression: Efficient Model Building0sStep 3b – Scikit-Learn: Building & Training Multiple Linear Regression Models0sStep 4a: Comparing Real vs Predicted Profits in Linear Regression – Hands-on Gui0sStep 4b – ML in Python: Evaluating Multiple Linear Regression Accuracy0sMultiple Linear Regression in Python – Backward EliminationMultiple Linear Regression in Python – EXTRA CONTENTStep 1a – Data Preprocessing for MLR: Handling Categorical Data0sStep 1b – Preparing Datasets for Multiple Linear Regression in R0sStep 2a – Multiple Linear Regression in R: Building & Interpreting the Regressor0sStep 2b: Statistical Significance – P-values & Stars in Regression0sStep 3 – How to Use predict() Function in R for Multiple Linear Regression0sOptimizing Multiple Regression Models: Backward Elimination Technique in R0sMastering Feature Selection: Backward Elimination in R for Linear Regression0sMultiple Linear Regression in R – Automatic Backward Elimination
-
Polynomial RegressionUnderstanding Polynomial Linear Regression: Applications and Examples0sStep 1a – Building a Polynomial Regression Model for Salary Prediction in Python0sStep 1b – Setting Up Data for Linear vs Polynomial Regression Comparison0sStep 2a: Linear to Polynomial Regression – Preparing Data for Advanced Models0sStep 2b – Transforming Linear to Polynomial Regression: A Step-by-Step Guide0sStep 3a – Plotting Real vs Predicted Salaries: Linear Regression Visualization0sStep 3b – Polynomial vs Linear Regression: Better Fit with Higher Degrees0sStep 4a: Predicting Salaries – Linear Regression in Python (Array Input Guide)0sStep 4b: Python Polynomial Regression – Predicting Salaries Accurately0sStep 1a – Implementing Polynomial Regression in R: HR Salary Analysis Case Study0sStep 1b – ML Fundamentals: Preparing Data for Polynomial Regression0sStep 2a – Building Linear & Polynomial Regression Models in R: A Comparison0sStep 2b – Building a Polynomial Regression Model: Adding Squared & Cubed Terms0sStep 3a: Visualizing Regression Results – Creating Scatter Plots with ggplot2 in0sStep 3b: Visualizing Linear Regression – Plotting Predictions vs Observations0sStep 3c – Polynomial Regression: Curve Fitting for Better Predictions0sStep 4a – How to Make Single Predictions Using Polynomial Regression in R0sStep 4b – Predicting Salaries with Polynomial Regression: A Practical Example0sStep 1 – Building a Reusable Framework for Nonlinear Regression Analysis in R0sStep 2 – Mastering Regression Model Visualization: Increasing Data Resolution0s
-
Support Vector Regression (SVR)How Does Support Vector Regression (SVR) Differ from Linear Regression?0sRBF Kernel SVR: From Linear to Non-Linear Support Vector Regression0sStep 1a – SVR Model Training: Feature Scaling and Dataset Preparation in Python0sStep 1b – SVR in Python: Importing Libraries and Dataset for Machine Learning0sStep 2a – Mastering Feature Scaling for Support Vector Regression in Python0sStep 2b: Reshaping Data for SVR – Preparing Y Vector for Feature Scaling (Python0sStep 2c: SVR Data Prep – Scaling X & Y Independently with StandardScaler0sStep 3: SVM Regression: Creating & Training SVR Model with RBF Kernel in Python0sStep 4 – SVR Model Prediction: Handling Scaled Data and Inverse Transformation0sStep 5a – How to Plot Support Vector Regression (SVR) Models: Step-by-Step Guide0sStep 5b – SVR: Scaling & Inverse Transformation in Python0sStep 1 – SVR Tutorial: Creating a Support Vector Machine Regressor in R0sStep 2 – Support Vector Regression: Building a Predictive Model in Python0s
-
Decision Tree RegressionHow to Build a Regression Tree: Step-by-Step Guide for Machine Learning0sStep 1a – Decision Tree Regression: Building a Model without Feature Scaling0sStep 1b: Uploading & Preprocessing Data for Decision Tree Regression in Python0sStep 2 – Implementing DecisionTreeRegressor: A Step-by-Step Guide in Python0sStep 3 – Implementing Decision Tree Regression in Python: Making Predictions0sStep 4 – Visualizing Decision Tree Regression: High-Resolution Results0sStep 1 – Creating a Decision Tree Regressor: Using rpart Function in R0sStep 2 – Decision Tree Regression: Fixing Splits with rpart Control Parameter0sStep 3: Non-Continuous Regression – Decision Tree Visualization Challenges0sStep 4 – Visualizing Decision Tree: Understanding Intervals and Predictions0s
-
Random Forest RegressionUnderstanding Random Forest Algorithm: Intuition and Application in ML0sStep 1 – Building a Random Forest Regression Model with Python and Scikit-Learn0sStep 2 – Creating a Random Forest Regressor: Key Parameters and Model Fitting0sStep 1 – Building a Random Forest Model in R: Regression Tutorial0sStep 2 – Visualizing Random Forest Regression: Interpreting Stairs and Splits0sStep 3 – Fine-Tuning Random Forest: From 10 to 500 Trees for Accurate Prediction0s
-
Evaluating Regression Models Performance
-
Regression Model Selection in PythonMake sure you have this Model Selection folder readyStep 1 – Mastering Regression Toolkit: Comparing Models for Optimal Performance0sStep 2 – Creating Generic Code Templates for Various Regression Models in Python0sStep 3: Evaluating Regression Models – R-Squared & Performance Metrics Explained0sStep 4 – Implementing R-Squared Score in Python with Scikit-Learn’s Metrics0sStep 1 – Selecting the Best Regression Model: R-squared Evaluation in Python0sStep 2 – Selecting the Best Regression Model: Random Forest vs. SVR Performance0s
-
Regression Model Selection in R
-
———-Part 3: Classification———-
-
Logistic RegressionUnderstanding Logistic Regression: Predicting Categorical Outcomes0sLogistic Regression: Finding the Best Fit Curve Using Maximum Likelihood0sStep 1a – Building a Logistic Regression Model for Customer Behavior Prediction0sStep 1b – Implementing Logistic Regression in Python: Data Preprocessing Guide0sStep 2a: Python Data Preprocessing for Logistic Regression Dataset Prep0sStep 2b – Data Preprocessing: Feature Scaling Techniques for Logistic Regression0sStep 3a – How to Import and Use LogisticRegression Class from Scikit-learn0sStep 3b – Training Logistic Regression Model: Fit Method for Classification0sStep 4a – Formatting Single Observation Input for Logistic Regression Predict0sStep 4b: Predicted vs. Real Purchase Decisions in Logistic Regression0sStep 5 – Comparing Predicted vs Real Results: Python Logistic Regression Guide0sStep 6a – Implementing Confusion Matrix and Accuracy Score in Scikit-Learn0sStep 6b: Evaluating Classification Models – Confusion Matrix & Accuracy Metrics0sStep 7a – Visualizing Logistic Regression Decision Boundaries in Python: 2D Plot0sStep 7b – Interpreting Logistic Regression Results: Prediction Regions Explained0sStep 7c – Visualizing Logistic Regression Performance on New Data in Python0sLogistic Regression in Python – Step 7 (Colour-blind friendly image)Step 1 – Data Preprocessing for Logistic Regression in R: Preparing Your Dataset0sStep 2 – How to Create a Logistic Regression Classifier Using R’s GLM Function0sStep 3 – How to Use R for Logistic Regression Prediction: Step-by-Step Guide0sStep 4 – How to Assess Model Accuracy Using a Confusion Matrix in R0sWarning – UpdateStep 5a – Interpreting Logistic Regression Plots: Prediction Regions Explained0sStep 5b: Logistic Regression – Linear Classifiers & Prediction Boundaries0sStep 5c – Data Viz in R: Colorizing Pixels for Logistic Regression0sLogistic Regression in R – Step 5 (Colour-blind friendly image)Optimizing R Scripts for Machine Learning: Building a Classification Template0sMachine Learning Regression and Classification EXTRAEXTRA CONTENT: Logistic Regression Practical Case Study
-
K-Nearest Neighbors (K-NNK-Nearest Neighbors (KNN) Explained: A Beginner’s Guide to Classification0sStep 1 – Python KNN Tutorial: Classifying Customer Data for Targeted Marketing0sStep 2 – Building a K-Nearest Neighbors Model: Scikit-Learn KNeighborsClassifier0sStep 3 – Visualizing KNN Decision Boundaries: Python Tutorial for Beginners0sStep 1 – Implementing KNN Classification in R: Setup & Data Preparation0sStep 2 – Building a KNN Classifier: Preparing Training and Test Sets in R0sStep 3 – Implementing KNN Classification in R: Adapting the Classifier Template0s
-
Support Vector Machine (SVM)Support Vector Machines Explained: Hyperplanes and Support Vectors in ML0sStep 1 – Building a Support Vector Machine Model with Scikit-learn in Python0sStep 2 – Building a Support Vector Machine Model with Sklearn’s SVC in Python0sStep 3 – Understanding Linear SVM Limitations: Why It Didn’t Beat kNN Classifier0sStep 1 – Building a Linear SVM Classifier in R: Data Import and Initial Setup0sStep 2: Creating & Evaluating Linear SVM Classifier in R – Predictions & Results0s
-
Kernel SVMFrom Linear to Non-Linear SVM: Exploring Higher Dimensional Spaces0sSupport Vector Machines: Transforming Non-Linear Data for Linear Separation0sKernel Trick: SVM Machine Learning for Non-Linear Classification0sUnderstanding Different Types of Kernel Functions for Machine Learning0sMastering Support Vector Regression: Non-Linear SVR with RBF Kernel Explained0sStep 1 – Python Kernel SVM: Applying RBF to Solve Non-Linear Classification0sStep 2 – Mastering Kernel SVM: Improving Accuracy with Non-Linear Classifiers0sStep 1 – Kernel SVM vs Linear SVM: Overcoming Non-Linear Separability in R0sStep 2 – Building a Gaussian Kernel SVM Classifier for Advanced Machine Learning0sStep 3: Visualizing Kernel SVM – Non-Linear Classification in Machine Learning0s
-
Naive BayesUnderstanding Bayes’ Theorem Intuitively: From Probability to Machine Learning0sUnderstanding Naive Bayes Algorithm: Probability Classification Explained0sBayes Theorem in Machine Learning: Step-by-Step Probability Calculation0sWhy is Naive Bayes Called Naive? Understanding the Algorithm’s Assumptions0sStep 1 – Naive Bayes in Python: Applying ML to Social Network Ads Optimisation0sStep 2 – Python Naive Bayes: Training and Evaluating a Classifier on Real Data0sStep 3 – Analyzing Naive Bayes Algorithm Results: Accuracy and Predictions0sStep 1 – Getting Started with Naive Bayes Algorithm in R for Classification0sStep 2 – Troubleshooting Naive Bayes Classification: Empty Prediction Vectors0sStep 3 – Visualizing Naive Bayes Results: Creating Confusion Matrix and Graphs0s
-
Decision Tree ClassificationHow Decision Tree Algorithms Work: Step-by-Step Guide with Examples0sStep 1 – Implementing Decision Tree Classification in Python with Scikit-learn0sStep 2 – Training a Decision Tree Classifier: Optimizing Performance in Python0sStep 1 – R Tutorial: Creating a Decision Tree Classifier with rpart Library0sStep 2 – Decision Tree Classifier: Optimizing Prediction Boundaries in R0sStep 3 – Decision Tree Visualization: Exploring Splits and Conditions in R0s
-
Random Forest ClassificationUnderstanding Random Forest: Decision Trees and Majority Voting Explained0sStep 1 – Implementing Random Forest Classification in Python with Scikit-Learn0sStep 2: Random Forest Evaluation – Confusion Matrix & Accuracy Metrics0sStep 1: Random Forest Classifier – From Template to Implementation in R0sStep 2: Random Forest Classification – Visualizing Predictions & Results0sStep 3 – Evaluating Random Forest Performance: Test Set Results & Overfitting0s
-
Classification Model Selection in PythonMake sure you have this Model Selection folder readyMastering the Confusion Matrix: True Positives, Negatives, and Errors0sStep 1 – How to Choose the Right Classification Algorithm for Your Dataset0sStep 2 – Optimizing Model Selection: Streamlined Classification Code in Python0sStep 3 – Evaluating Classification Algorithms: Accuracy Metrics in Python0sStep 4 – Model Selection Process: Evaluating Classification Algorithms0s
-
Evaluating Classification Model PerformanceLogistic Regression: Interpreting Predictions and Errors in Data Science0sMachine Learning Model Evaluation: Accuracy Paradox and Better Metrics0sUnderstanding CAP Curves: Assessing Model Performance in Data Science 20240sMastering CAP Analysis: Assessing Classification Models with Accuracy Ratio0sConclusion of Part 3 – Classification
-
———-Part 4: Clustering———-
-
K-Means ClusteringWhat is Clustering in Machine Learning? Introduction to Unsupervised Learning0sK-Means Clustering Tutorial: Visualizing the Machine Learning Algorithm0sHow to Use the Elbow Method in K-Means Clustering: A Step-by-Step Guide0sK-Means++ Algorithm: Solving the Random Initialization Trap in Clustering0sStep 1a – Python K-Means Tutorial: Identifying Customer Patterns in Mall Data0sStep 1b: K-Means Clustering – Data Preparation in Google Colab/Jupyter0sStep 2a – K-Means Clustering in Python: Selecting Relevant Features for Analysis0sStep 2b: K-Means Clustering – Optimizing Features for 2D Visualization0sStep 3a – Implementing the Elbow Method for K-Means Clustering in Python0sStep 3b – Optimizing K-means Clustering: WCSS and Elbow Method Implementation0sStep 3c – Plotting the Elbow Method Graph for K-Means Clustering in Python0sStep 4 – Creating a Dependent Variable from K-Means Clustering Results in Python0sStep 5a: Visualizing K-Means Clusters of Customer Data with Python Scatter0sStep 5b – Visualizing K-Means Clusters: Plotting Customer Segments in Python0sStep 5c – Analyzing Customer Segments: Insights from K-means Clustering0sStep 1 – K-Means Clustering in R: Importing & Exploring Segmentation Data0sStep 2 – K-Means Algorithm Implementation in R: Fitting and Analyzing Mall Data0s
-
Hierarchical ClusteringHow to Perform Hierarchical Clustering: Step-by-Step Guide for Machine Learning0sVisualizing Cluster Dissimilarity: Dendrograms in Hierarchical Clustering0sMastering Hierarchical Clustering: Dendrogram Analysis and Threshold Setting0sStep 1 – Getting Started with Hierarchical Clustering: Data Setup in Python0sStep 2a – Implementing Hierarchical Clustering: Building a Dendrogram with SciPy0sStep 2b – Visualizing Hierarchical Clustering: Dendrogram Basics in Python0sStep 2c – Interpreting Dendrograms: Optimal Clusters in Hierarchical Clustering0sStep 3a – Building a Hierarchical Clustering Model with Scikit-learn in Python0sStep 3b – Comparing 3 vs 5 Clusters in Hierarchical Clustering: Python Example0sStep 1 – R Data Import for Clustering: Annual Income & Spending Score Analysis0sStep 2: Using H.clust in R – Building & Interpreting Dendrograms for Clustering0sStep 3 – Implementing Hierarchical Clustering: Using Cat Tree Method in R0sStep 4 – Cluster Plot Method: Visualizing Hierarchical Clustering Results in R0sStep 5 – Hierarchical Clustering in R: Understanding Customer Spending Patterns0sConclusion of Part 4 – Clustering
-
———-Part 5: Association Rule Learning———-
-
AprioriApriori Algorithm: Uncovering Hidden Patterns in Data Mining | Association Rules0sStep 1 – Association Rule Learning: Boost Sales with Python Data Mining0sStep 2 – Creating a List of Transactions for Market Basket Analysis in Python0sStep 3 – Configuring Apriori Function: Support, Confidence, and Lift in Python0sStep 1 – Creating a Sparse Matrix for Association Rule Mining in R0sStep 2 – Optimizing Apriori Model: Choosing Minimum Support and Confidence0sStep 3: Optimizing Product Placement – Apriori Algorithm, Lift & Confidence0s
-
ECLAT
-
———-Part 6: Reinforcement Learning———-
-
Upper Confidence Bound (UCB)Multi-Armed Bandit: Exploration vs Exploitation in Reinforcement Learning0sUpper Confidence Bound Algorithm: Solving Multi-Armed Bandit Problems in ML0sStep 1 – Upper Confidence Bound: Solving Multi-Armed Bandit Problem in Python0sStep 2: Implementing UCB Algorithm in Python – Data Preparation0sStep 3 – Python Code for Upper Confidence Bound: Setting Up Key Variables0sStep 4 – Python for RL: Coding the UCB Algorithm Step-by-Step0sStep 5 – Coding Upper Confidence Bound: Optimizing Ad Selection in Python0sStep 6 – Reinforcement Learning: Finalizing UCB Algorithm in Python0sStep 7 – Visualizing UCB Algorithm Results: Histogram Analysis in Python0sStep 1 – Exploring Upper Confidence Bound in R: Multi-Armed Bandit Problems0sStep 2 – UCB Algorithm in R: Calculating Average Reward & Confidence Interval0sStep 3: Optimizing Ad Selection – UCB & Multi-Armed Bandit Algorithm Explained0sStep 4 – UCB Algorithm Performance: Analyzing Ad Selection with Histograms0s
-
Thompson SamplingUnderstanding Thompson Sampling Algorithm: Intuition and Implementation0sDeterministic vs Probabilistic: UCB and Thompson Sampling in Machine Learning0sStep 1 – Python Implementation of Thompson Sampling for Bandit Problems0sStep 2 – Optimizing Ad Selection with Thompson Sampling Algorithm in Python0sStep 3 – Python Code for Thompson Sampling: Maximizing Random Beta Distributions0sStep 4 – Beating UCB with Thompson Sampling: Python Multi-Armed Bandit Tutorial0sAdditional Resource for this SectionStep 1 – Thompson Sampling vs UCB: Optimizing Ad Click-Through Rates in R0sStep 2 – Reinforcement Learning: Thompson Sampling Outperforms UCB Algorithm0s
-
———-Part 7: Natural Language Processing———-Welcome to Part 7 – Natural Language ProcessingNLP Basics: Understanding Bag of Words and Its Applications in Machine Learning0sDeep NLP & Sequence-to-Sequence Models: Exploring Natural Language Processing0sFrom If/Else Rules to CNNs: Evolution of Natural Language Processing0sImplementing Bag of Words in NLP: A Step-by-Step Tutorial0sStep 1 – Getting Started with Natural Language Processing: Sentiment Analysis0sStep 2 – Importing TSV Data for Sentiment Analysis: Python NLP Data Processing0sStep 3 – Text Cleaning for NLP: Remove Punctuation and Convert to Lowercase0sStep 4 – Text Preprocessing: Stemming and Stop Word Removal for NLP in Python0sStep 5 – Tokenization and Feature Extraction for Naive Bayes Sentiment Analysis0sStep 6 – Training and Evaluating a Naive Bayes Classifier for Sentiment Analysis0sNatural Language Processing in Python – EXTRAHomework ChallengeStep 1 – Text Classification Using Bag-of-Words and Random Forest in R0sWarning – UpdateStep 2 – NLP Data Preprocessing in R: Importing TSV Files for Sentiment Analysis0sStep 3 – NLP in R: Initialising a Corpus for Sentiment Analysis0sStep 4 – NLP Data Cleaning: Lowercase Transformation in R for Text Analysis0sStep 5 – Sentiment Analysis Data Cleaning: Removing Numbers with TM Map0sStep 6 – Cleaning Text Data: Removing Punctuation for NLP and Classification0sStep 7 – Simplifying Corpus: Using SnowballC Package to Remove Stop Words in R0sStep 8 – Enhancing Text Classification: Stemming for Efficient Feature Matrices0sStep 9: Removing Extra Spaces for NLP Sentiment Analysis Text Cleaning0sStep 10 – Building a Document-Term Matrix for NLP Text Classification0sHomework Challenge
-
———-Part 8: Deep Learning……….
-
Artificial Neural NetworksUnderstanding CNN Layers: Convolution, ReLU, Pooling, and Flattening Explained0sDeep Learning Basics: Exploring Neurons, Synapses, and Activation Functions0sNeural Network Basics: Understanding Activation Functions in Deep Learning0sHow Do Neural Networks Work? Step-by-Step Guide to Deep Learning Algorithms0sHow Do Neural Networks Learn? Deep Learning Fundamentals Explained0sDeep Learning Fundamentals: Gradient Descent vs Brute Force Optimization0sStochastic vs Batch Gradient Descent: Deep Learning Fundamentals0sDeep Learning Fundamentals: Training Neural Networks Step-by-Step0sBank Customer Churn Prediction: Machine Learning Model with TensorFlow0sStep 1 ANN in Python: Predicting Customer Churn with TensorFlow0sStep 2 – TensorFlow 2.0 Tutorial: Preprocessing Data for Customer Churn Model0sStep 3 – Designing ANN: Sequential Model & Dense Layers for Deep Learning0sStep 4 – Train Neural Network: Compile & Fit for Customer Churn Prediction0sStep 5 – Implementing ANN for Churn Prediction: From Model to Confusion Matrix0sStep 1 – How to Preprocess Data for Artificial Neural Networks in R0sStep 2 – How to Install and Initialize H2O for Efficient Deep Learning in R0sStep 3: Building Deep Learning Model – H2O Neural Network Layer Config0sStep 4 – H2O Deep Learning: Making Predictions and Evaluating Model Accuracy0sDeep Learning Additional ContentEXTRA CONTENT: ANN Case Study
-
Convolutional Neural NetworksUnderstanding CNN Layers: Convolution, ReLU, Pooling, and Flattening Explained0sIntroduction to CNNs: Understanding Deep Learning for Computer Vision0sStep 1 – Understanding Convolution in CNNs: Feature Detection and Feature Maps0sStep 1b – Applying ReLU to Convolutional Layers: Breaking Up Image Linearity0sStep 2 – Max Pooling in CNNs: Enhancing Spatial Invariance for Image Recognition0sStep 3 – Understanding Flattening in Convolutional Neural Network Architecture0sStep 4 – Fully Connected Layers in CNNs: Optimizing Feature Combination0sDeep Learning Basics: How Convolutional Neural Networks (CNNs) Process Images0sDeep Learning Essentials: Understanding Softmax and Cross-Entropy in CNNs0sStep 1: Intro to CNNs for Image Classification0sStep 2 – Keras ImageDataGenerator: Prevent Overfitting in CNN Models0sStep 3 – TensorFlow CNN: Convolution to Output Layer for Vision Tasks0sStep 4: CNN Training – Epochs, Loss Function & Metrics in TensorFlow0sStep 5 – Making Single Predictions with Convolutional Neural Networks in Python0sHands-on CNN Training: Using Jupyter Notebook for Image Classification0sDeep Learning Additional Content #2
-
———-Part 9: Dimensionality Reduction———-
-
Principal Component Analysis (PCA)PCA Algorithm Intuition: Reducing Dimensions in Unsupervised Learning0sStep 1 PCA in Python : Reducing Wine Dataset Features with Scikit-learn0sStep 2 – PCA in Action: Reducing Dimensions and Predicting Customer Segments0sStep 1 in R – Understanding Principal Component Analysis for Feature Extraction0sStep 2 – Using preProcess Function in R for PCA: Extracting Principal Components0sStep 3 – Implementing PCA and SVM for Customer Segmentation: Practical Guide0s
-
Linear Discriminant Analysis (LDA)
-
Kernel PCA
-
———-Part 10: Model Selection & Boosting……….
-
Model SelectionMastering Model Evaluation: K-Fold Cross-Validation Techniques Explained0sHow to Master the Bias-Variance Tradeoff in Machine Learning Models0sK-Fold Cross-Validation in Python: Improve Machine Learning Model Performance0sOptimizing SVM Models with GridSearchCV: A Step-by-Step Python Tutorial0sEvaluating ML Model Accuracy: K-Fold Cross-Validation Implementation in R0sOptimizing SVM Models with Grid Search: A Step-by-Step R Tutorial0s
-
XGBoost
-
Annex: Logistic Regression (Long Explanation)
-
Congratulations!! Don’t forget your Prize :)
Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.
Over 1 Million students world-wide trust this course.
We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
This course can be completed by either doing either the Python tutorials, or R tutorials, or both – Python & R. Pick the programming language that you need for your career.
This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured the following way:
-
Part 1 – Data Preprocessing
-
Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
-
Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
-
Part 4 – Clustering: K-Means, Hierarchical Clustering
-
Part 5 – Association Rule Learning: Apriori, Eclat
-
Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
-
Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
-
Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
-
Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
-
Part 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
Each section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now.
Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.
And last but not least, this course includes both Python and R code templates which you can download and use on your own projects.
What's included
- 42.5 hours on-demand video
- 5 coding exercises
- 40 articles
- Access on mobile and TV
- Certificate of completion